In this paper, we propose a semi-distributed cooperative spectrum sen
sing (SDCSS) and channel access framework
for multi-channel cognitive radio networks (CRNs). In particular, we c
onsider a SDCSS scheme where secondary
users (SUs) perform sensing and exchange sensing outcomes with ea
ch other to locate spectrum holes. In addition,
we devise the
p
-persistent CSMA-based cognitive MAC protocol integrating the SDCSS to
enable efficient spectrum
sharing among SUs. We then perform throughput analysis and develop
an algorithm to determine the spectrum
sensing and access parameters to maximize the throughput for a given
allocation of channel sensing sets. Moreover,
we consider the spectrum sensing set optimization problem for SUs to maxim
ize the overall system throughput. We
present both exhaustive search and low-complexity greedy algorithms
to determine the sensing sets for SUs and
analyze their complexity. We also show how our design and analysis can be
extended to consider reporting errors.
Finally, extensive numerical results are presented to demonstrate the sig
nificant performance gain of our optimized
design framework with respect to non-optimized designs as well as the imp
acts of different protocol parameters on
the throughput performance.
Channel Assignment With Access Contention Resolution for Cognitive Radio Netw...Polytechnique Montreal
In this paper, we consider the channel assignment problem for cognitive radio networks with hardware-constrained secondary users (SUs). In particular, we assume that SUs exploit spectrum holes on a set of channels where each SU can use at most one available channel for communication. We present the optimal brute-force search algorithm to solve the corresponding nonlinear integer optimization problem and analyze its complexity. Because the optimal solution has exponential complexity with the numbers of channels and SUs, we develop two low-complexity channel assignment algorithms that can efficiently utilize the spectrum holes. In the first algorithm, SUs are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm that can improve the throughput performance compared with its nonoverlapping channel assignment counterpart. Moreover, we design a distributed medium access control (MAC) protocol for access contention resolution and integrate it into the overlapping channel assignment algorithm. We then analyze the saturation throughput and the complexity of the proposed channel assignment algorithms. We also present several potential extensions, including the development of greedy channel assignment algorithms under the max-min fairness criterion and throughput analysis, considering sensing errors. Finally, numerical results are presented to validate the developed theoretical results and illustrate the performance gains due to the proposed channel assignment algorithms.
Implementation of Vacate on Demand Algorithm in Various Spectrum Sensing Netw...IJERA Editor
In present days the wireless communications are widely increases because of this reason spectrum utilization can be rapidly increased.For efficient usage of spectrum we can implement the Vacate on demand algorithm in different networks. CR users also need to sense the spectrum and vacate the channel upon the detection of the PU‟s presence to protectPUs from harmful interference. To achieve these fundamental CR functions, CR users usually coordinate with each other by using a common medium for control message exchange ensuring a priority of PUs over CR users. This paper presents the Vacate on Demand (VD) algorithm which enables dynamic spectrum access and ensures to vacate the assigned channel in case of PU activity and move the CR user to some other vacant channel to make spectrum available to PUs as well as to CR users. The basic idea is to use a ranking table of the available channels based on the PU activity detected on each channel. To improve the spectrum efficiency we can implement the Vacate on demand algorithm in MANET Network.
General analytical framework for cooperative sensing and access trade-off opt...Polytechnique Montreal
In this paper, we investigate the joint cooperative spectrum sensing and access design problem for multi-channel cognitive radio networks. A general heterogeneous setting is considered where the probabilities that different channels are available, SNRs of the signals received at secondary users (SUs) due to transmissions from primary users (PUs) for different users and channels can be different. We assume a cooperative sensing strategy with a general a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs can exploit available channels. We analyze the sensing performance and the throughput achieved by the joint sensing and access design. Based on this analysis, we develop algorithms to find optimal parameters for the sensing and access protocols and to determine channel assignment for SUs to maximize the system throughput. Finally, numerical results are presented to verify the effectiveness of our design and demonstrate the relative performance of our proposed algorithms and the optimal ones.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and ...Polytechnique Montreal
In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
Bio-inspired route estimation in cognitive radio networks IJECEIAES
Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.
COVERAGE OPTIMIZED AND TIME EFFICIENT LOCAL SEARCH BETWEENNESS ROUTING FOR HE...ijcsa
The document proposes a Local Search and Enhanced Betweenness Routing (LS-EBR) model for wireless sensor networks used for health monitoring. The LS-EBR model aims to improve routing efficiency by increasing sensor node coverage and minimizing routing time. It uses a local search algorithm based on greedy forwarding to route packets to neighboring nodes that are closest to the destination while also considering the reliability of sensor nodes. An enhanced betweenness routing algorithm is also used to measure energy consumption and select routes that consider both routing overhead and remaining energy of sensor nodes. Simulation results showed the LS-EBR model achieved higher coverage and improved routing efficiency compared to opportunistic routing.
This document discusses techniques for predicting traffic patterns to evaluate the probability of channel availability for spectrum sharing using cognitive radio. It reviews related work on traffic models and prediction methods, including ARIMA, SARIMA, neural networks, and mean square approaches. The goals are to increase channel utilization and reduce call blockage and interference for secondary users accessing licensed channels when primary users are not using them. Accurately predicting primary user traffic patterns is important to avoid interference and allow secondary users to evaluate channel availability before transmission.
This document summarizes a research paper that proposes using a cuckoo search algorithm to optimize routing in mobile ad hoc networks (MANETs). Specifically:
1) It enhances an existing Loyalty Pair Neighbors selection (LPNS) routing protocol using cuckoo search optimization to select stable neighbor nodes.
2) Cuckoo search is applied to select neighbor nodes based on hop count, energy level, and queue length to find optimal routes.
3) The proposed Enhanced LPNS with Cuckoo Search (ELPNS_Cuckoo) protocol is evaluated in simulations and shown to improve performance metrics like packet delivery ratio, throughput, and delay compared to the original LPNS protocol.
Channel Assignment With Access Contention Resolution for Cognitive Radio Netw...Polytechnique Montreal
In this paper, we consider the channel assignment problem for cognitive radio networks with hardware-constrained secondary users (SUs). In particular, we assume that SUs exploit spectrum holes on a set of channels where each SU can use at most one available channel for communication. We present the optimal brute-force search algorithm to solve the corresponding nonlinear integer optimization problem and analyze its complexity. Because the optimal solution has exponential complexity with the numbers of channels and SUs, we develop two low-complexity channel assignment algorithms that can efficiently utilize the spectrum holes. In the first algorithm, SUs are assigned distinct sets of channels. We show that this algorithm achieves the maximum throughput limit if the number of channels is sufficiently large. In addition, we propose an overlapping channel assignment algorithm that can improve the throughput performance compared with its nonoverlapping channel assignment counterpart. Moreover, we design a distributed medium access control (MAC) protocol for access contention resolution and integrate it into the overlapping channel assignment algorithm. We then analyze the saturation throughput and the complexity of the proposed channel assignment algorithms. We also present several potential extensions, including the development of greedy channel assignment algorithms under the max-min fairness criterion and throughput analysis, considering sensing errors. Finally, numerical results are presented to validate the developed theoretical results and illustrate the performance gains due to the proposed channel assignment algorithms.
Implementation of Vacate on Demand Algorithm in Various Spectrum Sensing Netw...IJERA Editor
In present days the wireless communications are widely increases because of this reason spectrum utilization can be rapidly increased.For efficient usage of spectrum we can implement the Vacate on demand algorithm in different networks. CR users also need to sense the spectrum and vacate the channel upon the detection of the PU‟s presence to protectPUs from harmful interference. To achieve these fundamental CR functions, CR users usually coordinate with each other by using a common medium for control message exchange ensuring a priority of PUs over CR users. This paper presents the Vacate on Demand (VD) algorithm which enables dynamic spectrum access and ensures to vacate the assigned channel in case of PU activity and move the CR user to some other vacant channel to make spectrum available to PUs as well as to CR users. The basic idea is to use a ranking table of the available channels based on the PU activity detected on each channel. To improve the spectrum efficiency we can implement the Vacate on demand algorithm in MANET Network.
General analytical framework for cooperative sensing and access trade-off opt...Polytechnique Montreal
In this paper, we investigate the joint cooperative spectrum sensing and access design problem for multi-channel cognitive radio networks. A general heterogeneous setting is considered where the probabilities that different channels are available, SNRs of the signals received at secondary users (SUs) due to transmissions from primary users (PUs) for different users and channels can be different. We assume a cooperative sensing strategy with a general a-out-of-b aggregation rule and design a synchronized MAC protocol so that SUs can exploit available channels. We analyze the sensing performance and the throughput achieved by the joint sensing and access design. Based on this analysis, we develop algorithms to find optimal parameters for the sensing and access protocols and to determine channel assignment for SUs to maximize the system throughput. Finally, numerical results are presented to verify the effectiveness of our design and demonstrate the relative performance of our proposed algorithms and the optimal ones.
Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis, and ...Polytechnique Montreal
In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.
Bio-inspired route estimation in cognitive radio networks IJECEIAES
Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms.
COVERAGE OPTIMIZED AND TIME EFFICIENT LOCAL SEARCH BETWEENNESS ROUTING FOR HE...ijcsa
The document proposes a Local Search and Enhanced Betweenness Routing (LS-EBR) model for wireless sensor networks used for health monitoring. The LS-EBR model aims to improve routing efficiency by increasing sensor node coverage and minimizing routing time. It uses a local search algorithm based on greedy forwarding to route packets to neighboring nodes that are closest to the destination while also considering the reliability of sensor nodes. An enhanced betweenness routing algorithm is also used to measure energy consumption and select routes that consider both routing overhead and remaining energy of sensor nodes. Simulation results showed the LS-EBR model achieved higher coverage and improved routing efficiency compared to opportunistic routing.
This document discusses techniques for predicting traffic patterns to evaluate the probability of channel availability for spectrum sharing using cognitive radio. It reviews related work on traffic models and prediction methods, including ARIMA, SARIMA, neural networks, and mean square approaches. The goals are to increase channel utilization and reduce call blockage and interference for secondary users accessing licensed channels when primary users are not using them. Accurately predicting primary user traffic patterns is important to avoid interference and allow secondary users to evaluate channel availability before transmission.
This document summarizes a research paper that proposes using a cuckoo search algorithm to optimize routing in mobile ad hoc networks (MANETs). Specifically:
1) It enhances an existing Loyalty Pair Neighbors selection (LPNS) routing protocol using cuckoo search optimization to select stable neighbor nodes.
2) Cuckoo search is applied to select neighbor nodes based on hop count, energy level, and queue length to find optimal routes.
3) The proposed Enhanced LPNS with Cuckoo Search (ELPNS_Cuckoo) protocol is evaluated in simulations and shown to improve performance metrics like packet delivery ratio, throughput, and delay compared to the original LPNS protocol.
This document summarizes a research paper that proposes an Energy Efficient Reserved Path Routing Topology (RPRT) routing scheme for mobile ad hoc networks. The RPRT aims to improve energy efficiency and reduce end-to-end delay compared to the existing M-Trace routing scheme. It allows nodes to use estimated energy levels to make better channel admission control decisions for providing quality of service guarantees. The paper presents the RPRT scheme and its energy level estimation, routing discovery, route maintenance, and path selection mechanisms. Simulation results demonstrate that the RPRT achieves a 16% increase in energy efficiency and 37% reduction in end-to-end delay compared to M-Trace.
A MANET is an autonomous collection of mobile users that communicate over relatively bandwidth constrained wireless links. When designing mobile ad hoc networks, several interesting and difficult problems arise because of the shared nature of the wireless medium, limited transmission power (range) of wireless devices, node mobility, and battery limitations. This paper aims at providing a new schema to improve Dynamic Source Routing (DSR) Protocol. The aim
behind the proposed enhancement is to find the best route in acceptable time limit without having broadcast storm. Moreover, O-DSR enables network not only to overcome congestion but also maximize the lifetime of mobile nodes. Some simulations results show that the Route Request (RREQ) and the Control Packet Overhead decrease by 15% when O-DSR is used, consequently. Also the global energy consumption in O-DSR is lower until to 60 % , which leads to a long lifetime of the network.
Optimal resource allocation in networked control systems using viterbi algorithmjournalBEEI
This paper presents an optimal bandwidth allocation method for a networked control system (NCS) which includes time-driven sensor, event-driven controller and random channels. A hidden markov model (HMM) with a discretized state space is formulated for the random traffic to predict the network states using a suitable data window. Network bandwidth is allocated based on the predicted traffic state subject to bounds on the deterministic traffic that guarantee acceptable NCS performance and do not exceed hardware limitations. Bandwidth allocation uses minimization of unmet bandwidth demand. A stability condition is derived for a variable but bounded sampling period interval. Computer simulation results show the effect of varying the number of discrete states for the HMM and the window width on bandwidth allocation. The results compare favorably with a published approach based on fuzzy logic.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
I-Min: An Intelligent Fermat Point Based Energy Efficient Geographic Packet F...graphhoc
Energy consumption and delay incurred in packet delivery are the two important metrics for measuring the performance of geographic routing protocols for Wireless Adhoc and Sensor Networks (WASN). A protocol capable of ensuring both lesser energy consumption and experiencing lesser delay in packet delivery is thus suitable for networks which are delay sensitive and energy hungry at the same time. Thus a smart packet forwarding technique addressing both the issues is thus the one looked for by any geographic routing protocol. In the present paper we have proposed a Fermat point based forwarding technique which reduces the delay experienced during packet delivery as well as the energy consumed for transmission and reception of data packets.
Fast Data Collection with Interference and Life Time in Tree Based Wireless S...IJMER
This document discusses techniques for fast data collection in wireless sensor networks using a tree-based topology. It specifically focuses on minimizing the schedule length for aggregated convergecast (where data is aggregated at each hop) and raw-data convergecast (where packets are individually relayed to the sink).
It first considers time scheduling on a single channel, and then combines scheduling with transmission power control and multiple frequencies to further reduce interference and schedule length. It provides lower bounds on schedule length when interference is eliminated, and proposes algorithms that achieve these bounds.
Evaluation of different channel assignment methods, routing tree topologies, interference models, and their impact on schedule length is also presented. The key findings are that combining scheduling, power control,
Tech report: Fair Channel Allocation and Access Design for Cognitive Ad Hoc N...Polytechnique Montreal
supplement to Globecom paper: L. T. Tan and L. B. Le, ``Fair Channel Allocation and Access Design for Cognitive Ad Hoc Networks,'' in 2012 IEEE Global Communications Conference (IEEE GLOBECOM 2012), Anaheim, California, USA, pp. 1162-1167, December, 2012.
This document summarizes a research paper on fair channel allocation in cognitive radio ad hoc networks. It discusses:
1) A channel allocation problem where each node is assigned a subset of channels to access periodically using a MAC protocol, with the goal of maximizing fair sharing of spectrum among network flows.
2) An optimal brute-force search algorithm for solving this NP-hard problem and its exponential complexity.
3) The development of low-complexity channel allocation algorithms and an analytical throughput model to evaluate performance.
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
The document proposes a new clustering and routing algorithm for wireless sensor networks that aims to extend network lifetime. Key points:
- The algorithm divides nodes into sensing nodes and relay nodes, with relay nodes responsible for forwarding data to reduce cluster head burden.
- It selects cluster heads and relay nodes based on residual energy to distribute load and avoid early node death.
- A routing tree is constructed among relay nodes to transmit data to the base station in a multi-hop manner, selecting next hops based on residual energy and number of child nodes to balance energy usage.
- The goal is to improve energy efficiency, extend network lifetime, and increase data accuracy through mechanisms like clustering, load balancing, and fault detection
Signal classification in fading channels using cyclic spectral ankareenavolt
This document discusses signal classification in fading channels using cyclic spectral analysis. It proposes a hierarchical classifier that can identify signals such as AM, BFSK, OFDM, CDMA, PSK and QAM with no prior knowledge of carrier frequency, phase or symbol rate. The classifier performance is assessed using various multi-antenna combining schemes in fading channels. Cyclostationary features such as the spectral correlation function and cyclic cumulants are extracted to classify signals based on their spectral fingerprints. The proposed classifier is designed to minimize the number of samples needed for classification while maximizing reliability at each stage.
The document discusses clustering routing protocols for wireless sensor networks. It provides an overview of clustering techniques which group sensor nodes into clusters with elected cluster heads that aggregate and transmit data to the base station. This approach provides benefits like improved scalability, reduced energy consumption and load compared to flat routing protocols. The document also outlines various objectives of clustering like data aggregation, load balancing, fault tolerance and connectivity. It reviews several popular clustering protocols and notes that no single technique performs best in all areas, leaving room for future improvements to address these issues.
An Adaptive Routing Algorithm for Communication Networks using Back Pressure...IJMER
The basic idea of backpressure techniques is to prioritize transmissions over links that have
the highest queue differentials. Backpressure method effectively makes packets flow through the network
as though pulled by gravity towards the destination end, which has the smallest queue size of zero. Under
high traffic conditions, this method works very well, and backpressure is able to fully utilize the available
network resources in a highly dynamic fashion. Under low traffic conditions, however, because many
other hosts may also have a small or zero queue size, there is inefficiency in terms of an increase in
delay, as packets may loop or take a long time to make their way to the destination end. In this paper we
use the concept of shadow queues. Each node has to maintain some counters, called as shadow queues,
per destination. This is very similar to the idea of maintaining a routing table (for routing purpose) per
destination. Using the concept of shadow queues, we partially decouple routing and the scheduling. A
shadow network is maintained to update a probabilistic routing table that packets use upon arrival at a
node. The same shadow network, with back-pressure technique, is used to activate transmissions between
nodes. The routing algorithm is designed to minimize the average number of hops used by the packets in
the network. This idea, along with the scheduling and routing decoupling, leads to delay reduction
compared with the traditional back-pressure algorithm
A novel routing technique for mobile ad hoc networks (manet)ijngnjournal
Actual network size depends on the application and the protocols developed for the routing for this kind of
networks should be scalable and efficient. Each routing protocol should support small as well as large
scale networks very efficiently. As the number of node increase, it increases the management functionality
of the network. Graph theoretic approach traditionally was applied to networks where nodes are static or
fixed. In this paper, we have applied the graph theoretic routing to MANET where nodes are mobile. Here,
we designed all identical nodes in the cluster except the cluster head and this criterion reduces the
management burden on the network. Each cluster supports a few nodes with a cluster head. The intracluster
connectivity amongst the nodes within the cluster is supported by multi-hop connectivity to ensure
handling mobility in such a way that no service disruption can occur. The inter-cluster connectivity is also
achieved by multi-hop connectivity. However, for inter-cluster communications, only cluster heads are
connected. This paper demonstrates graph theoretic approach produces an optimum multi-hop connectivity
path based on cumulative minimum degree that minimizes the contention and scheduling delay end-toend.
It is applied to both intra-cluster communications as well as inter-cluster communications. The
performance shows that having a multi-hop connectivity for intra-cluster communications is more power
efficient compared to broadcast of information with maximum power coverage. We also showed the total
number of required intermediate nodes in the transmission from source to destination. However, dynamic
behavior of the nodes requires greater understanding of the node degree and mobility at each instance of
time in order to maintain end-to-end QoS for multi-service provisioning. Our simulation results show that
the proposed graph theoretic routing approach will reduce the overall delay and improves the physical
layer data frame transmission.
An optimistic sector oriented approach to mitigate broadcast storm problem in...IAEME Publication
In mobile ad hoc networks (MANETs), due to frequent changes in topology there exist more
link breakages which lead to high rate of path failures and route discoveries, which cause an
increased routing control overhead. Thus, it is necessary to reduce the overhead of route discovery in
the design of routing protocols for MANETs. In a route discovery, broadcasting may be an
elementary and effective data dissemination mechanism, wherever a mobile node blindly
rebroadcasts the first received route request packets unless it has a route to the destination, and
therefore it causes the broadcast storm problem. This paper proposes an optimistic approach OpSOA
to mitigate the broadcasting storm problem and to scale back the communication overheads of
routing protocols by forming sectors within the network and finding the route to destination by two
sectors at a time. The simulation result shows that the proposed mechanism substantially reduces
route requests. Since the proposed protocol searches for the destination sector wise thereby reducing
network wide broadcast of routing requests, traffic, collision and contention. There by there can be
an increase in the packet delivery ratio and decrease in the average end-to-end delay
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
Energy Enhancement by Selecting Optimal Routing Path from Source to sink Node...ijiert bestjournal
WSNs are used for collecting limitations have to be taken into account when desi gning e nergy dissipation significantly methods namely A- star algorithm and fuzzy approach for WSN routing path from the source to the destination,by considering the battery power,number of hops,and traffic load for extend lifetime improves by employing the optimized routing protocol lifetime.
Analyzing the performance of the dynamicIJCNCJournal
In this paper, we are focused to analyse the performance of the two dimensional dynamic
Position Location and Tracking (PL&T) of mobile nodes. The architecture of the dynamic PL&T
is developed based on determining the potential zone of the target node (s) and then tracking
using the triangulation. We assume that the nodes are mobile and have one omnidirectional
antenna per node. The network architecture under consideration is cluster based Mobile Ad Hoc
Network (MANET) where at an instance of time, three nodes are used as reference nodes to track
target node(s) using triangulation method. The novel approach in this PL&T tracking method is
the “a priori” identification of the zone of the target node(s) within a circle with a reasonable
radios, and then placing the three reference nodes for the zone such that a good geometry is
created between the reference nodes and the target nodes to improve the accuracy of
triangulation method. The geometry of the reference nodes’ triangle is closer to equilateral
triangle and all potential target nodes are inside the circle. We establish the fact that when the
target node is moving linearly, the predictive method of zone finding is sufficient to track the
target node accurately. However, when the target node changes the direction, the predictive
method of zone finding will fail and we need to place the three references outside the zone such
that proper geometry with no one angle is less than 30 degrees is maintained to get accurate
PL&T location of the target node at each instance of time. The new zone is always formed for
each instance of time prior to triangulation.
In this paper, we demonstrate the accuracy of integrated zone finding and triangulation for
detecting the PL&T location the node at each instance of time within 1.5 foot accuracy. It should
be noted that as the target node is tracked continuously by applying the integrated zone finding
and triangulation algorithm at different instances of time, one foot accuracy can no longer be
maintained. Periodically, the good PL&T data on each node has to be established by
reinitializing the PL&T locations of the nodes including those that are used as reference nodes.
In this paper, the performance of the dynamic PL&T system is derived using Additive White
Gaussian Noise (AWGN) channel; and using AWGN plus Multi-path fading channel. The impact
of multipath fading on tracking accuracy is analysed using Rician Fading channel for MANET
applications outdoors. Our real time simulations show the PL&T tracking accuracy for the
mobile target nodes in both cases to be within 1.5 foot accuracy.
Fair channel allocation and access design for cognitive ad hoc networksPolytechnique Montreal
The document discusses fair channel allocation and access design for cognitive radio ad hoc networks. It considers a scenario where network nodes can access at most one channel at a time. It analyzes the complexity of the optimal brute-force search algorithm for this NP-hard channel allocation problem. It then develops low-complexity algorithms and a throughput model to analyze their performance in achieving fair spectrum sharing. Specifically, it proposes a non-overlapping channel assignment algorithm and an overlapping channel assignment algorithm combined with a MAC protocol to resolve channel access contention.
Performance Analysis for Parallel MRA in Heterogeneous Wireless NetworksEditor IJCATR
This document analyzes methods for optimal path selection and power allocation in heterogeneous wireless networks where a user can transmit data through multiple radio access technologies (RATs) simultaneously. It formulates the bandwidth and power allocation problem as an optimization problem to maximize total system capacity. The Newton and modified Newton methods are proposed to find the optimal solution. Simulation results show the modified Newton method achieves higher total system capacity compared to the Newton method.
Efficient Destination Discovery using Geographical Gossiping in MANETsidescitation
Due to dynamic topology of Mobile ad hoc networks (MANETS), early designs of
routing protocols incur a large number of discovery packets while trying to discover a route
to a destination node in the network. To reduce the number of discovery packets,
geographical information assisted routing protocols came into picture. In case of
geographical ad hoc routing protocols, there is no need to discover a route to a destination
node. But, they need to discover the fresh location of a destination node to deliver data
packets to the destination location. Geographical information assisted ad hoc routing
protocols reduce discovery packet overhead using past information about the destination
node such as location, velocity and direction of motion. When a source node does not have
any information about a destination node, the existing geographical routing protocols use
flooding techniques or location database server to know the present location of the
destination. A flooding technique or a location database server induces large number of
control packets in the network. To reduce the number of control packets during location
discovery, we propose a novel geographical gossiping technique for MANETs. The
technique basically uses two types of gossiping viz. selective and random gossiping. We have
evaluated the performance of the proposed technique using qualnet simulator and
compared its performance with flooding technique and probability based gossiping
technique. The simulation results clearly show that our technique has considerably reduced
control packet overhead compared to flooding and probability based gossiping technique.
Performance Evaluation Cognitive Medium Access Control Protocolsijtsrd
This document evaluates the performance of two cognitive medium access control (CMAC) protocols: common control-multi channel CMAC protocol and non-common control-multi channel CMAC protocol. It analyzes 30 CMAC protocols based on their features such as number of radios used, prior knowledge of licensed/secondary user systems, and spectrum access techniques. A simulation is conducted using NS2 to compare the throughput and packet delivery ratio of the two protocols. Results show the common control protocol outperforms the non-common control protocol, and that throughput increases with larger packet sizes for both protocols.
This document summarizes a research paper that proposes an Energy Efficient Reserved Path Routing Topology (RPRT) routing scheme for mobile ad hoc networks. The RPRT aims to improve energy efficiency and reduce end-to-end delay compared to the existing M-Trace routing scheme. It allows nodes to use estimated energy levels to make better channel admission control decisions for providing quality of service guarantees. The paper presents the RPRT scheme and its energy level estimation, routing discovery, route maintenance, and path selection mechanisms. Simulation results demonstrate that the RPRT achieves a 16% increase in energy efficiency and 37% reduction in end-to-end delay compared to M-Trace.
A MANET is an autonomous collection of mobile users that communicate over relatively bandwidth constrained wireless links. When designing mobile ad hoc networks, several interesting and difficult problems arise because of the shared nature of the wireless medium, limited transmission power (range) of wireless devices, node mobility, and battery limitations. This paper aims at providing a new schema to improve Dynamic Source Routing (DSR) Protocol. The aim
behind the proposed enhancement is to find the best route in acceptable time limit without having broadcast storm. Moreover, O-DSR enables network not only to overcome congestion but also maximize the lifetime of mobile nodes. Some simulations results show that the Route Request (RREQ) and the Control Packet Overhead decrease by 15% when O-DSR is used, consequently. Also the global energy consumption in O-DSR is lower until to 60 % , which leads to a long lifetime of the network.
Optimal resource allocation in networked control systems using viterbi algorithmjournalBEEI
This paper presents an optimal bandwidth allocation method for a networked control system (NCS) which includes time-driven sensor, event-driven controller and random channels. A hidden markov model (HMM) with a discretized state space is formulated for the random traffic to predict the network states using a suitable data window. Network bandwidth is allocated based on the predicted traffic state subject to bounds on the deterministic traffic that guarantee acceptable NCS performance and do not exceed hardware limitations. Bandwidth allocation uses minimization of unmet bandwidth demand. A stability condition is derived for a variable but bounded sampling period interval. Computer simulation results show the effect of varying the number of discrete states for the HMM and the window width on bandwidth allocation. The results compare favorably with a published approach based on fuzzy logic.
Optimized Cluster Establishment and Cluster-Head Selection Approach in WSNIJCNCJournal
In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.
I-Min: An Intelligent Fermat Point Based Energy Efficient Geographic Packet F...graphhoc
Energy consumption and delay incurred in packet delivery are the two important metrics for measuring the performance of geographic routing protocols for Wireless Adhoc and Sensor Networks (WASN). A protocol capable of ensuring both lesser energy consumption and experiencing lesser delay in packet delivery is thus suitable for networks which are delay sensitive and energy hungry at the same time. Thus a smart packet forwarding technique addressing both the issues is thus the one looked for by any geographic routing protocol. In the present paper we have proposed a Fermat point based forwarding technique which reduces the delay experienced during packet delivery as well as the energy consumed for transmission and reception of data packets.
Fast Data Collection with Interference and Life Time in Tree Based Wireless S...IJMER
This document discusses techniques for fast data collection in wireless sensor networks using a tree-based topology. It specifically focuses on minimizing the schedule length for aggregated convergecast (where data is aggregated at each hop) and raw-data convergecast (where packets are individually relayed to the sink).
It first considers time scheduling on a single channel, and then combines scheduling with transmission power control and multiple frequencies to further reduce interference and schedule length. It provides lower bounds on schedule length when interference is eliminated, and proposes algorithms that achieve these bounds.
Evaluation of different channel assignment methods, routing tree topologies, interference models, and their impact on schedule length is also presented. The key findings are that combining scheduling, power control,
Tech report: Fair Channel Allocation and Access Design for Cognitive Ad Hoc N...Polytechnique Montreal
supplement to Globecom paper: L. T. Tan and L. B. Le, ``Fair Channel Allocation and Access Design for Cognitive Ad Hoc Networks,'' in 2012 IEEE Global Communications Conference (IEEE GLOBECOM 2012), Anaheim, California, USA, pp. 1162-1167, December, 2012.
This document summarizes a research paper on fair channel allocation in cognitive radio ad hoc networks. It discusses:
1) A channel allocation problem where each node is assigned a subset of channels to access periodically using a MAC protocol, with the goal of maximizing fair sharing of spectrum among network flows.
2) An optimal brute-force search algorithm for solving this NP-hard problem and its exponential complexity.
3) The development of low-complexity channel allocation algorithms and an analytical throughput model to evaluate performance.
A Cluster-Based Routing Protocol and Fault Detection for Wireless Sensor NetworkIJCNCJournal
The document proposes a new clustering and routing algorithm for wireless sensor networks that aims to extend network lifetime. Key points:
- The algorithm divides nodes into sensing nodes and relay nodes, with relay nodes responsible for forwarding data to reduce cluster head burden.
- It selects cluster heads and relay nodes based on residual energy to distribute load and avoid early node death.
- A routing tree is constructed among relay nodes to transmit data to the base station in a multi-hop manner, selecting next hops based on residual energy and number of child nodes to balance energy usage.
- The goal is to improve energy efficiency, extend network lifetime, and increase data accuracy through mechanisms like clustering, load balancing, and fault detection
Signal classification in fading channels using cyclic spectral ankareenavolt
This document discusses signal classification in fading channels using cyclic spectral analysis. It proposes a hierarchical classifier that can identify signals such as AM, BFSK, OFDM, CDMA, PSK and QAM with no prior knowledge of carrier frequency, phase or symbol rate. The classifier performance is assessed using various multi-antenna combining schemes in fading channels. Cyclostationary features such as the spectral correlation function and cyclic cumulants are extracted to classify signals based on their spectral fingerprints. The proposed classifier is designed to minimize the number of samples needed for classification while maximizing reliability at each stage.
The document discusses clustering routing protocols for wireless sensor networks. It provides an overview of clustering techniques which group sensor nodes into clusters with elected cluster heads that aggregate and transmit data to the base station. This approach provides benefits like improved scalability, reduced energy consumption and load compared to flat routing protocols. The document also outlines various objectives of clustering like data aggregation, load balancing, fault tolerance and connectivity. It reviews several popular clustering protocols and notes that no single technique performs best in all areas, leaving room for future improvements to address these issues.
An Adaptive Routing Algorithm for Communication Networks using Back Pressure...IJMER
The basic idea of backpressure techniques is to prioritize transmissions over links that have
the highest queue differentials. Backpressure method effectively makes packets flow through the network
as though pulled by gravity towards the destination end, which has the smallest queue size of zero. Under
high traffic conditions, this method works very well, and backpressure is able to fully utilize the available
network resources in a highly dynamic fashion. Under low traffic conditions, however, because many
other hosts may also have a small or zero queue size, there is inefficiency in terms of an increase in
delay, as packets may loop or take a long time to make their way to the destination end. In this paper we
use the concept of shadow queues. Each node has to maintain some counters, called as shadow queues,
per destination. This is very similar to the idea of maintaining a routing table (for routing purpose) per
destination. Using the concept of shadow queues, we partially decouple routing and the scheduling. A
shadow network is maintained to update a probabilistic routing table that packets use upon arrival at a
node. The same shadow network, with back-pressure technique, is used to activate transmissions between
nodes. The routing algorithm is designed to minimize the average number of hops used by the packets in
the network. This idea, along with the scheduling and routing decoupling, leads to delay reduction
compared with the traditional back-pressure algorithm
A novel routing technique for mobile ad hoc networks (manet)ijngnjournal
Actual network size depends on the application and the protocols developed for the routing for this kind of
networks should be scalable and efficient. Each routing protocol should support small as well as large
scale networks very efficiently. As the number of node increase, it increases the management functionality
of the network. Graph theoretic approach traditionally was applied to networks where nodes are static or
fixed. In this paper, we have applied the graph theoretic routing to MANET where nodes are mobile. Here,
we designed all identical nodes in the cluster except the cluster head and this criterion reduces the
management burden on the network. Each cluster supports a few nodes with a cluster head. The intracluster
connectivity amongst the nodes within the cluster is supported by multi-hop connectivity to ensure
handling mobility in such a way that no service disruption can occur. The inter-cluster connectivity is also
achieved by multi-hop connectivity. However, for inter-cluster communications, only cluster heads are
connected. This paper demonstrates graph theoretic approach produces an optimum multi-hop connectivity
path based on cumulative minimum degree that minimizes the contention and scheduling delay end-toend.
It is applied to both intra-cluster communications as well as inter-cluster communications. The
performance shows that having a multi-hop connectivity for intra-cluster communications is more power
efficient compared to broadcast of information with maximum power coverage. We also showed the total
number of required intermediate nodes in the transmission from source to destination. However, dynamic
behavior of the nodes requires greater understanding of the node degree and mobility at each instance of
time in order to maintain end-to-end QoS for multi-service provisioning. Our simulation results show that
the proposed graph theoretic routing approach will reduce the overall delay and improves the physical
layer data frame transmission.
An optimistic sector oriented approach to mitigate broadcast storm problem in...IAEME Publication
In mobile ad hoc networks (MANETs), due to frequent changes in topology there exist more
link breakages which lead to high rate of path failures and route discoveries, which cause an
increased routing control overhead. Thus, it is necessary to reduce the overhead of route discovery in
the design of routing protocols for MANETs. In a route discovery, broadcasting may be an
elementary and effective data dissemination mechanism, wherever a mobile node blindly
rebroadcasts the first received route request packets unless it has a route to the destination, and
therefore it causes the broadcast storm problem. This paper proposes an optimistic approach OpSOA
to mitigate the broadcasting storm problem and to scale back the communication overheads of
routing protocols by forming sectors within the network and finding the route to destination by two
sectors at a time. The simulation result shows that the proposed mechanism substantially reduces
route requests. Since the proposed protocol searches for the destination sector wise thereby reducing
network wide broadcast of routing requests, traffic, collision and contention. There by there can be
an increase in the packet delivery ratio and decrease in the average end-to-end delay
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
Energy Enhancement by Selecting Optimal Routing Path from Source to sink Node...ijiert bestjournal
WSNs are used for collecting limitations have to be taken into account when desi gning e nergy dissipation significantly methods namely A- star algorithm and fuzzy approach for WSN routing path from the source to the destination,by considering the battery power,number of hops,and traffic load for extend lifetime improves by employing the optimized routing protocol lifetime.
Analyzing the performance of the dynamicIJCNCJournal
In this paper, we are focused to analyse the performance of the two dimensional dynamic
Position Location and Tracking (PL&T) of mobile nodes. The architecture of the dynamic PL&T
is developed based on determining the potential zone of the target node (s) and then tracking
using the triangulation. We assume that the nodes are mobile and have one omnidirectional
antenna per node. The network architecture under consideration is cluster based Mobile Ad Hoc
Network (MANET) where at an instance of time, three nodes are used as reference nodes to track
target node(s) using triangulation method. The novel approach in this PL&T tracking method is
the “a priori” identification of the zone of the target node(s) within a circle with a reasonable
radios, and then placing the three reference nodes for the zone such that a good geometry is
created between the reference nodes and the target nodes to improve the accuracy of
triangulation method. The geometry of the reference nodes’ triangle is closer to equilateral
triangle and all potential target nodes are inside the circle. We establish the fact that when the
target node is moving linearly, the predictive method of zone finding is sufficient to track the
target node accurately. However, when the target node changes the direction, the predictive
method of zone finding will fail and we need to place the three references outside the zone such
that proper geometry with no one angle is less than 30 degrees is maintained to get accurate
PL&T location of the target node at each instance of time. The new zone is always formed for
each instance of time prior to triangulation.
In this paper, we demonstrate the accuracy of integrated zone finding and triangulation for
detecting the PL&T location the node at each instance of time within 1.5 foot accuracy. It should
be noted that as the target node is tracked continuously by applying the integrated zone finding
and triangulation algorithm at different instances of time, one foot accuracy can no longer be
maintained. Periodically, the good PL&T data on each node has to be established by
reinitializing the PL&T locations of the nodes including those that are used as reference nodes.
In this paper, the performance of the dynamic PL&T system is derived using Additive White
Gaussian Noise (AWGN) channel; and using AWGN plus Multi-path fading channel. The impact
of multipath fading on tracking accuracy is analysed using Rician Fading channel for MANET
applications outdoors. Our real time simulations show the PL&T tracking accuracy for the
mobile target nodes in both cases to be within 1.5 foot accuracy.
Fair channel allocation and access design for cognitive ad hoc networksPolytechnique Montreal
The document discusses fair channel allocation and access design for cognitive radio ad hoc networks. It considers a scenario where network nodes can access at most one channel at a time. It analyzes the complexity of the optimal brute-force search algorithm for this NP-hard channel allocation problem. It then develops low-complexity algorithms and a throughput model to analyze their performance in achieving fair spectrum sharing. Specifically, it proposes a non-overlapping channel assignment algorithm and an overlapping channel assignment algorithm combined with a MAC protocol to resolve channel access contention.
Performance Analysis for Parallel MRA in Heterogeneous Wireless NetworksEditor IJCATR
This document analyzes methods for optimal path selection and power allocation in heterogeneous wireless networks where a user can transmit data through multiple radio access technologies (RATs) simultaneously. It formulates the bandwidth and power allocation problem as an optimization problem to maximize total system capacity. The Newton and modified Newton methods are proposed to find the optimal solution. Simulation results show the modified Newton method achieves higher total system capacity compared to the Newton method.
Efficient Destination Discovery using Geographical Gossiping in MANETsidescitation
Due to dynamic topology of Mobile ad hoc networks (MANETS), early designs of
routing protocols incur a large number of discovery packets while trying to discover a route
to a destination node in the network. To reduce the number of discovery packets,
geographical information assisted routing protocols came into picture. In case of
geographical ad hoc routing protocols, there is no need to discover a route to a destination
node. But, they need to discover the fresh location of a destination node to deliver data
packets to the destination location. Geographical information assisted ad hoc routing
protocols reduce discovery packet overhead using past information about the destination
node such as location, velocity and direction of motion. When a source node does not have
any information about a destination node, the existing geographical routing protocols use
flooding techniques or location database server to know the present location of the
destination. A flooding technique or a location database server induces large number of
control packets in the network. To reduce the number of control packets during location
discovery, we propose a novel geographical gossiping technique for MANETs. The
technique basically uses two types of gossiping viz. selective and random gossiping. We have
evaluated the performance of the proposed technique using qualnet simulator and
compared its performance with flooding technique and probability based gossiping
technique. The simulation results clearly show that our technique has considerably reduced
control packet overhead compared to flooding and probability based gossiping technique.
Performance Evaluation Cognitive Medium Access Control Protocolsijtsrd
This document evaluates the performance of two cognitive medium access control (CMAC) protocols: common control-multi channel CMAC protocol and non-common control-multi channel CMAC protocol. It analyzes 30 CMAC protocols based on their features such as number of radios used, prior knowledge of licensed/secondary user systems, and spectrum access techniques. A simulation is conducted using NS2 to compare the throughput and packet delivery ratio of the two protocols. Results show the common control protocol outperforms the non-common control protocol, and that throughput increases with larger packet sizes for both protocols.
The document discusses applying compressed sampling (CS) techniques for spectrum sensing and channel estimation in cognitive radio (CR) networks. It first provides background on CS theory, noting that signals can be reconstructed from fewer samples than required by Nyquist's theorem if the signal is sparse. It then proposes a compressed spectrum sensing scheme to detect wideband spectrum using sub-Nyquist sampling. After sensing, it formalizes the notion of sparse multipath channels and discusses estimating such channels using orthogonal matching pursuit. The effectiveness of these CS-based approaches is demonstrated through comparisons with conventional sensing and estimation methods.
A CRITICAL REVIEW OF THE ROUTING PROTOCOLS FOR COGNITIVE RADIO NETWORKS AND A...cscpconf
We present a critical review and analysis of different categories of routing protocols for cognitive radio networks. We first classify the available solutions to two broad categories: those
based on full spectrum knowledge (typically used to establish performance benchmarks) and those based on local spectrum knowledge (used for real-time implementation). The full spectrum knowledge based routing solutions are analyzed from a graph-theoretic point of view, and we review the layered graph, edge coloring and conflict graph models. We classify the various local spectrum knowledge based routing protocols into the following five categories: Minimum power, Minimum delay, Maximum throughput, Geographic and Class-based routing. A total of 25 routing protocols proposed for cognitive radio networks have been reviewed. We discuss the working principle and analyze the pros and cons of the routing protocols. Finally, we propose an idea of a load balancing-based local spectrum knowledge-based routing protocol for cognitive radio ad hoc networks.
Sensing of Spectrum for SC-FDMA Signals in Cognitive Radio NetworksIRJET Journal
The document proposes a technique for detecting spectrum used by interleaved single-carrier frequency-division multiple access (SC-FDMA) signals in cognitive radio networks. A metric is defined based on cyclostationary features to identify if subcarriers allocated to primary users are available for secondary users. The Neyman-Pearson test is used to examine two hypotheses (H0 and H1) representing the absence and presence of primary users. Simulation results show the proposed method outperforms existing techniques like autocorrelation of cyclic prefix and energy detection, with lower complexity but similar detection performance at low signal-to-noise ratios. The performance is evaluated under various conditions like number of users, pilot signals, window length, and block length
Hybrid Novel Approach for Channel Allocation in Heterogeneous Cognitive Radi...IJSRED
The document describes a proposed hybrid approach for channel allocation in heterogeneous cognitive radio networks. It aims to improve throughput and minimize packet transmission delay. The proposed approach uses Channel Quality Indicator (CQI) to identify the best channel for communication. It employs a common control channel (CCC) to control channel states. Transmission is controlled using TDMA-based Slotted Cognitive Function (SCF) and directional antenna-based Distributed Co-ordination Function (DCF). Simulation results show the proposed approach achieves higher throughput and lower delay compared to other protocols.
LARGE-SCALE MULTI-USER MIMO APPROACH FOR WIRELESS BACKHAUL BASED HETNETScsandit
In this paper, we consider the optimization of wireless capacity-limited backhaul links in future heterogeneous networks (HetNets). We assume that the HetNet is formed with one macro-cell base station (MBS), which is associated with multiple small-cell base stations (SBSs). It is also assumed both the MBS and the SBSs are equipped with massive arrays, while all mobiles users (macro-cell and small-cell users) have single antenna. For the backhaul links, we propose to use a capacity-aware beamforming scheme at the SBSs and MRC at the MBS. Using particle swarm optimization (PSO), each SBS seeks the optimal transmit weight vectors that maximize the backhaul uplink capacity and the access uplinks signal-to-interference plus noise ratio (SINR). The performance evaluation in terms of the symbol error rate (SER) and the ergodic system capacity shows that the proposed capacity-aware backhaul link scheme achieves similar or better performance than traditional wireless backhaul links and requires considerably less computational complexity.
Optimization of Cognitive Radio spectrum and
1. To optimise maximum throughput and SNIR of secondary user’s w.r.t Primary user’s.
2. To calculate throughput w.r.t no of slots by varying time slots and channel bandwidth.
3. To study the performance characteristics achieved through Greedy Algorithm and Optimal algorithm.
The document analyzes and optimizes random sensing order in cognitive radio systems. It presents the following:
- A Markov model is used to evaluate the average throughput of secondary users and average interference between secondary and primary users under a random sensing order policy.
- An optimization problem is formulated to maximize secondary network throughput while keeping interference below a threshold.
- A distributed adaptive algorithm is developed to optimize the network performance in a non-stationary environment. It outperforms static optimization by following wireless channel variations.
- Numerical results demonstrate the algorithm achieves substantial performance gains without centralized control or message passing between users. Fully distributed optimization can efficiently utilize spectrum in cognitive radio networks.
enhanced cognitive radio network in dynamic spectrum sensingDinokongkham
The document discusses enhancing cognitive radio networks through dynamic spectrum sensing over fading channels. It proposes a methodology that uses a hidden Markov model for primary user detection along with signal strength and location information. This is combined with analyzing spectrum availability and channel estimation to optimize sensing time and maximize throughput while protecting primary users. Simulation results show the proposed approach improves performance metrics like throughput, loss rate, latency and delay compared to existing methods.
Design and analysis of routing protocol for cognitive radio ad hoc networks i...IJECEIAES
Multi-hop routing protocol in cognitive radio mobile ad hoc networks (CRMANETs) is a critical issue. Furthermore, the routing metric used in multi-hop CRMANETs should reflect the bands availability, the links quality, the PU activities and quality of service (QoS) requirements of SUs. For the best of our knowledge, many of researchers investigated the performance of the different routing protocols in a homogeneous environment only. In this paper, we propose a heterogeneous cognitive radio routing protocol (HCR) operates in heterogeneous environment (i.e. the route from source to destination utilize the licensed and unlicensed spectrum bands). The proposed routing protocol is carefully developed to make a tradeoff between the channel diversity of the routing path along with the CRMANETs throughput. Using simulations, we discuss the performance of the proposed HCR routing protocol and compare it with the AODV routing protocol using a discrete-event simulation which we developed using JAVA platform.
Cross Layering using Reinforcement Learning in Cognitive Radio-based Industri...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Network, enables better interaction, coordination, and joint optimization of different protocols in achieving remarkable performance improvements. In this paper, network, and medium access control (MAC) layer functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal decision on their environment of operations. The proposed RLCLD schemes improve the SU network performance up to 30% as compared to conventional methods.
CROSS LAYERING USING REINFORCEMENT LEARNING IN COGNITIVE RADIO-BASED INDUSTRI...IJCNCJournal
The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor
Network, enables better interaction, coordination, and joint optimization of different protocols in achieving
remarkable performance improvements. In this paper, network, and medium access control (MAC) layer
functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing
and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial
ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for
next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as
computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal
decision on their environment of operations. The proposed RLCLD schemes improve the SU network
performance up to 30% as compared to conventional methods.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing...graphhoc
There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.
EDSA: ENERGY-EFFICIENT DYNAMIC SPECTRUMACCESS PROTOCOLS FOR COGNITIVE RADIO N...Nexgen Technology
The document proposes energy-efficient dynamic spectrum access (DSA) protocols for secondary users in cognitive radio networks. It derives the optimal packet length and sensing time for secondary users to maximize performance using Markov chain models. The protocols are evaluated in terms of secondary user goodput, energy efficiency, and primary user collision ratio. Adaptability of the protocols is tested over cellular GSM bands and real-time video traffic in ISM bands. Results show the protocols provide high channel utilization while keeping primary user collisions below an acceptable threshold.
IRJET- Spectrum Availability based Routing with Security Consideration fo...IRJET Journal
The document proposes routing algorithms for cognitive sensor networks that take into account spectrum availability and reliability. It discusses challenges for routing in cognitive sensor networks due to the dynamic availability of spectrum bands and the need for secondary users to vacate bands when primary users are detected. The document introduces two new routing metrics - one that measures the probability of successful transmission and another that measures average transmission delay. It also outlines two routing algorithms that aim to find paths with the highest probability of delivery success or lowest average delay. Extensive simulations are conducted to evaluate the performance of the proposed algorithms compared to existing approaches for cognitive sensor networks.
The document proposes a multi-channel MAC protocol called MMDQS-MAC for wireless sensor networks. It aims to improve network performance by selecting the best channel for each sensor node and supporting dynamic channel assignment. MMDQS-MAC is designed to decrease collision probability, interference, and improve throughput, energy efficiency, packet delivery ratio, and end-to-end delay. It analyzes the performance of MMDQS-MAC through mathematical modeling and simulation.
Multihop Multi-Channel Distributed QOS Scheduling MAC Scheme for Wireless Sen...IOSR Journals
This document proposes a Multihop Multi-Channel Distributed QoS Scheduling MAC scheme (MMDQS-MAC) to improve the performance of wireless sensor networks. MMDQS-MAC supports dynamic channel assignment where each sensor node is equipped with a directional antenna. It aims to decrease collisions and interference, improve overall network performance, and is suitable for low traffic networks. Simulation results show that MMDQS-MAC improves aggregate throughput, transmission success rate, packet delivery ratio, energy efficiency, and end-to-end delay.
Similar to Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks (20)
This document discusses the joint design of data compression and medium access control (MAC) protocol for smart grids with renewable energy sources. It proposes using compressed sensing (CS) techniques to compress reported power injection data from multiple nodes in both space and time, and adapting the 802.15.4 MAC protocol to enable efficient data transmission and reliable data reconstruction. An analytical model is developed to determine optimal MAC parameter configurations that minimize reporting delay while achieving reliable data recovery. Numerical results are presented to demonstrate performance gains over existing solutions in terms of reporting delay, energy consumption, and bandwidth usage.
Design and Optimal Configuration of Full-Duplex MAC Protocol for Cognitive Ra...Polytechnique Montreal
In this paper, we propose an adaptive medium access control (MAC) protocol for
full-duplex (FD) cognitive radio networks in which FD secondary users (SUs) perform channel contention
followed by concurrent spectrum sensing and transmission, and transmission only with maximum power
in two different stages (called the FD sensing and transmission stages, respectively) in each contention
and access cycle. The proposed FD cognitive MAC (FDC-MAC) protocol does not require synchronization
among SUs, and it efciently utilizes the spectrum and mitigates the self-interference in the FD transceiver.
We develop a mathematical model to analyze the throughput performance of the FDC-MAC protocol, where
both half-duplex (HD) transmission and FD transmission modes are considered in the transmission stage.
Then, we study the FDC-MAC conguration optimization through adaptively controlling the spectrum
sensing duration and transmit power level in the FD sensing stage.We prove that there exists optimal sensing
time and transmit power to achieve the maximum throughput, and we develop an algorithm to congure
the proposed FDC-MAC protocol. Extensive numerical results are presented to illustrate the optimal
FDC-MAC conguration and the impacts of protocol parameters and the self-interference cancellation
quality on the throughput performance. Moreover, we demonstrate the signicant throughput gains of the
FDC-MAC protocol with respect to the existing HD MAC and single-stage FD MAC protocols
This dissertation investigates design and optimization issues for cognitive medium access control (CMAC) protocols in cognitive radio networks. It presents four main contributions:
1) A CMAC protocol for parallel spectrum sensing that maximizes throughput for single-channel and multi-channel scenarios.
2) A CMAC protocol and channel assignment algorithm for sequential sensing that maximizes network throughput in hardware-constrained networks.
3) A distributed CMAC protocol integrated with cooperative sensing to improve spectrum utilization for multi-channel heterogeneous networks. Impacts of reporting errors are also considered.
4) An asynchronous full-duplex CMAC protocol that enables simultaneous sensing and access to maximize throughput by adapting sensing time and transmit powers based on detected primary user activity
The document proposes a full-duplex cognitive MAC (FDC-MAC) protocol for cognitive radio networks. The protocol consists of two stages: (1) a FD sensing stage where secondary users perform concurrent spectrum sensing and transmission at a controlled power level to mitigate self-interference, and (2) a transmission stage where secondary users transmit at maximum power if the sensing stage indicated an available channel. The document develops a mathematical model to analyze the throughput performance of the FDC-MAC protocol and proves that there exists an optimal sensing time and transmit power configuration to maximize throughput. Extensive simulation results demonstrate significant throughput gains of the FDC-MAC protocol over half-duplex and single-stage full-duplex MAC protocols.
The document discusses four MAC protocol designs for cognitive radio networks:
1) CMAC protocol with parallel spectrum sensing.
2) CMAC protocol with sequential sensing.
3) CMAC protocol with cooperative sensing.
4) Asynchronous full-duplex CMAC protocol.
For each design, the document analyzes throughput and optimizes sensing and access parameters to maximize throughput while protecting primary users. Numerical results demonstrate throughput improvements from the different MAC protocol designs.
The document summarizes the research scope and contributions of the author's PhD dissertation on medium access control (MAC) protocols for cognitive radio networks (CRNs). It discusses four past research areas: (1) CMAC protocol design with parallel spectrum sensing, (2) CMAC protocol with sequential sensing, (3) CMAC protocol with cooperative sensing, and (4) asynchronous full-duplex CMAC protocol. It also outlines three potential future research directions: (1) multi-channel MAC protocol design for full-duplex CRNs, (2) cross-layer CMAC and routing design for multi-hop CRNs, and (3) joint cognitive protocol and data processing design for smart grid applications.
This document proposes a joint design of data compression and MAC protocol for smart grids using compressed sensing. It aims to minimize reporting time while reliably reconstructing power injection data from multiple nodes. Specifically, it employs two-dimensional compressed sensing to compress correlated power data in space and time. It also adapts the 802.15.4 MAC protocol frame structure to enable efficient data transmission for reconstruction. An analytical model is developed to optimize MAC parameters and guarantee reliable data reconstruction with minimum reporting delay.
This document proposes and analyzes a distributed medium access control (MAC) protocol for full-duplex cognitive radio networks. The key aspects of the proposed protocol are:
1) It employs frame fragmentation, dividing each data packet into multiple fragments, to enable secondary users to perform spectrum sensing during data transmission and detect primary user activity in a timely manner.
2) Secondary users use a backoff mechanism similar to 802.11 to contend for channel access. The winning secondary user then transmits data fragments while sensing for primary users between fragments.
3) The document develops a mathematical model to analyze the throughput performance of the protocol and proposes an algorithm to optimize protocol parameters like fragment time and transmit power to maximize throughput
In order to improve sensing performance when the noise variance is not known, this paper considers a so-called
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and distributed MAC protocol design for cognitive radio
networks. Specifically, we design a synchronized MAC protocol
for dynamic spectrum sharing among multiple secondary
users, which incorporates spectrum sensing for protecting active
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required sensing time for primary users’ protection and optimal
contention window for maximizing total throughput of the
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illustrate a significant performance gain of the optimal sensing
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Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks
1. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 1
Joint Cooperative Spectrum Sensing and MAC
Protocol Design for Multi-channel Cognitive
Radio Networks
Le Thanh Tan and Long Bao Le
Abstract
In this paper, we propose a semi-distributed cooperative spectrum sensing (SDCSS) and channel access framework
for multi-channel cognitive radio networks (CRNs). In particular, we consider a SDCSS scheme where secondary
users (SUs) perform sensing and exchange sensing outcomes with each other to locate spectrum holes. In addition,
we devise the p-persistent CSMA-based cognitive MAC protocol integrating the SDCSS to enable efficient spectrum
sharing among SUs. We then perform throughput analysis and develop an algorithm to determine the spectrum
sensing and access parameters to maximize the throughput for a given allocation of channel sensing sets. Moreover,
we consider the spectrum sensing set optimization problem for SUs to maximize the overall system throughput. We
present both exhaustive search and low-complexity greedy algorithms to determine the sensing sets for SUs and
analyze their complexity. We also show how our design and analysis can be extended to consider reporting errors.
Finally, extensive numerical results are presented to demonstrate the significant performance gain of our optimized
design framework with respect to non-optimized designs as well as the impacts of different protocol parameters on
the throughput performance.
Index Terms
MAC protocol, cooperative spectrum sensing, throughput maximization, cognitive radio, and sensing set opti-
mization.
I. INTRODUCTION
It has been well recognized that cognitive radio is one of the most important technologies that would enable
us to meet exponentially growing spectrum demand via fundamentally improving the utilization of our precious
spectral resources [1]. Development of efficient spectrum sensing and access algorithms for cognitive radios are
among the key research issues for successful deployment of this promising technology. There is indeed a growing
literature on MAC protocol design and analysis for CRNs [2]-[12] (see [3] for a survey of recent works in this
topic). In [2], it was shown that a significant throughput gain can be achieved by optimizing the sensing time under
the single-SU setting. Another related effort along this line was conducted in [6] where sensing-period optimization
and optimal channel-sequencing algorithms were proposed to efficiently discover spectrum holes and to minimize
the exploration delay.
Manuscript received January 16, 2014; revised April 19, 2014; accepted June 5, 2014. The editor coordinating the review of this paper and
approving it for publication is Dr. Ashish Pandharipande.
The authors are with the Institut National de la Recherche Scientifique– ´Energie, Mat´eriaux et T´el´ecommunications, Universit´e du Qu´ebec,
Montr´eal, Qu´ebec, QC J3X 1S2, Canada. Emails: lethanh@emt.inrs.ca; long.le@emt.inrs.ca. L. T. Tan is the corresponding author.
June 6, 2014 DRAFT
2. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2
In [7], a control-channel based MAC protocol was proposed for SUs to exploit white spaces in the cognitive
ad hoc network. In particular, the authors of this paper developed both random and negotiation-based spectrum
sensing schemes and performed throughput analysis for both saturation and non-saturation scenarios. There exists
several other synchronous cognitive MAC protocols, which rely on a control channel for spectrum negotiation and
access [8]-[12]. In [4] and [5], we designed, analyzed, and optimized a window-based MAC protocol to achieve
efficient tradeoff between sensing time and contention overhead. However, these works considered the conventional
single-user-energy-detection-based spectrum sensing scheme, which would only work well if the signal to noise
ratio (SNR) is sufficiently high. In addition, the MAC protocol in these works was the standard window-based
CSMA MAC protocol, which is known to be outperformed by the p-persistent CSMA MAC protocol [30].
Optimal sensing and access design for CRNs were designed by using optimal stopping theory in [13]. In [14],
a multi-channel MAC protocol was proposed considering the distance among users so that white spaces can be
efficiently exploited while satisfactorily protecting primary users (PUs). Different power and spectrum allocation
algorithms were devised to maximize the secondary network throughput in [15]-[17]. Optimization of spectrum
sensing and access in which either cellular or TV bands can be employed was performed in [18]. These existing
works either assumed perfect spectrum sensing or did not consider the cooperative spectrum sensing in their design
and analysis.
Cooperative spectrum sensing has been proposed to improve the sensing performance where several SUs collab-
orate with each other to identify spectrum holes [20]-[27] and [37]. In a typical cooperative sensing scheme, each
SU performs sensing independently and then sends its sensing result to a central controller (e.g., an access point
(AP)). Here, various aggregation rules can be employed to combine these sensing results at the central controller to
decide whether or not a particular spectrum band is available for secondary access. In [37], the authors studied the
performance of hard decisions and soft decisions at a fusion center. They also investigated the impact of reporting
channel errors on the cooperative sensing performance. Recently, the authors of [38] proposed a novel cooperative
spectrum sensing scheme using hard decision combining considering feedback errors. In [23]-[26], optimization of
cooperative sensing under the a-out-of-b rule was studied. In [25], the game-theoretic based method was proposed
for cooperative spectrum sensing. In [27], the authors investigated the multi-channel scenario where the AP collects
statistics from SUs to decide whether it should stop at the current time slot. In [39], [40], two different optimization
problems for cooperative sensing were studied. The first one focuses on throughput maximization where the objective
is the probability of false alarm. The second one attempts to perform interference management where the objective
is the probability of detection. These existing works focused on designing and optimizing parameters for the
cooperative spectrum sensing algorithm; however, they did not consider spectrum access issues. Furthermore, either
the single channel setting or homogeneous network scenario (i.e., SUs experience the same channel condition and
spectrum statistics for different channels) was assumed in these works.
In [28] and [29], the authors conducted design and analysis for cooperative spectrum sensing and MAC protocol
design for cognitive radios where parallel spectrum sensing on different channels was assumed to be performed by
multiple spectrum sensors at each SU. In CRNs with parallel-sensing, there is no need to optimize spectrum sensing
sets for SUs. These works again considered the homogeneous network and each SU simply senses all channels. To
the best of our knowledge, existing cooperative spectrum sensing schemes rely on a central controller to aggregate
sensing results for white space detection (i.e., centralized design). In addition, homogeneous environments and
parallel sensing have been commonly assumed in the literature, which would not be very realistic.
In this work, we consider a general SDCSS and access framework under the heterogeneous environment where
June 6, 2014 DRAFT
3. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 3
statistics of wireless channels, and spectrum holes can be arbitrary and there is no central controller to collect
sensing results and make spectrum status decisions. In addition, we assume that each SU is equipped with only one
spectrum sensor so that SUs have to sense channels sequentially. This assumption would be applied to real-world
hardware-constrained cognitive radios. The considered SDCSS scheme requires SUs to perform sensing on their
assigned sets of channels and then exchange spectrum sensing results with other SUs, which can be subject to
errors. After the sensing and reporting phases, SUs employ the p-persistent CSMA MAC protocol [30] to access
one available channel. In this MAC protocol, parameter p denotes the access probability to the chosen channel if
the carrier sensing indicates an available channel (i.e., no other SUs transmit on the chosen channel). It is of interest
to determine the access parameter p that can mitigate the collisions and hence enhance the system throughput [30].
Also, optimization of the spectrum sensing set for each SU (i.e., the set of channels sensed by the SU) is very
critical to achieve good system throughput. Moreover, analysis and optimization of the joint spectrum sensing and
access design become much more challenging in the heterogeneous environment, which, however, can significantly
improve the system performance. Our current paper aims to resolve these challenges whose contributions can be
summarized as follows:
• We propose the distributed p-persistent CSMA protocol incorporating SDCSS for multi-channel CRNs. Then
we analyze the saturation throughput and optimize the spectrum sensing time and access parameters to achieve
maximum throughput for a given allocation of channel sensing sets. This analysis and optimization are
performed in the general heterogeneous scenario assuming that spectrum sensing sets for SUs have been
predetermined.
• We study the channel sensing set optimization (i.e., channel assignment) for throughput maximization and
devise both exhaustive search and low-complexity greedy algorithms to solve the underlying NP-hard opti-
mization problem. Specifically, an efficient solution for the considered problem would only allocate a subset
of “good” SUs to sense each channel so that accurate sensing can be achieved with minimal sensing time. We
also analyze the complexity of the brute-force search and the greedy algorithms.
• We extend the design and analysis to consider reporting errors as SUs exchange their spectrum sensing results.
In particular, we describe cooperative spectrum sensing model, derive the saturation throughput considering
reporting errors. Moreover, we discuss how the proposed algorithms to optimize the sensing/access parameters
and sensing sets can be adapted to consider reporting errors. Again, all the analysis is performed for the
heterogeneous environment.
• We present numerical results to illustrate the impacts of different parameters on the secondary throughput
performance and demonstrate the significant throughput gain due to the optimization of different parameters
in the proposed framework.
The remaining of this paper is organized as follows. Section II describes system and sensing models. MAC
protocol design, throughput analysis, and optimization are performed in Section III assuming no reporting errors.
Section IV provides further extension for the analysis and optimization considering reporting errors. Section V
presents numerical results followed by concluding remarks in Section VI. The summary of key variables in the
paper is given in Table IV.
II. SYSTEM MODEL AND SPECTRUM SENSING DESIGN
In this section, we describe the system model and spectrum sensing design for the multi-channel CRNs. Specif-
ically, sensing performances in terms of detection and false alarm probabilities are presented.
June 6, 2014 DRAFT
8. 4 0P H
4 4PU / C
1 1PU / C
3 3PU / C
2 2PU / C
Fig. 1. Considered network and spectrum sharing model (PU: primary user, SU: secondary user, and Ci is the channel i corresponding to
PUi)
A. System Model
We consider a network setting where N pairs of SUs opportunistically exploit white spaces in M channels for
data transmission. For simplicity, we refer to pair i of SUs simply as SU i. We assume that each SU can exploit
only one available channel for transmission (i.e., SUs are equipped with narrow-band radios). We will design a
synchronized MAC protocol integrating SDCSS for channel access. We assume that each channel is either in the
idle or busy state for each predetermined periodic interval, which is referred to as a cycle in this paper.
We further assume that each pair of SUs can overhear transmissions from other pairs of SUs (i.e., collocated
networks). There are M PUs each of which may or may not use one corresponding channel for its data transmission
in each cycle. In addition, it is assumed that transmission from any pair of SUs on a particular channel will affect
the primary receiver which receives data on that channel. The network setting under investigation is shown in Fig. 1
where Ci denotes channel i that belongs to PU i.
B. Semi-Distributed Cooperative Spectrum Sensing
We assume that each SU i is assigned a set of channels Si where it senses all channels in this assigned set at
beginning of each cycle in a sequential manner (i.e., sense one-by-one). Optimization of such channel assignment
will be considered in the next section. Upon completing the channel sensing, each SU i exchanges the sensing
results (i.e., idle/busy status of all channels in Si) with other SUs for further processing. Here, the channel status
of each channel can be represented by one bit (e.g., 1 for idle and 0 for busy status). Upon collecting sensing
results, each SU will decide idle/busy status for all channels. Then, SUs are assumed to employ a distributed MAC
protocol to perform access resolution so that only the winning SUs on each channel are allowed to transmit data.
The detailed MAC protocol design will be presented later.
Let H0 and H1 denote the events that a particular PU is idle and active on its corresponding channel in any cycle,
respectively. In addition, let Pj (H0) and Pj (H1) = 1−Pj (H0) be the probabilities that channel j is available and
not available for secondary access, respectively. We assume that SUs employ an energy detection sensing scheme
and let fs be the sampling frequency used in the sensing period for all SUs. There are two important performance
measures, which are used to quantify the sensing performance, namely detection and false alarm probabilities. In
particular, a detection event occurs when a SU successfully senses a busy channel and false alarm represents the
June 6, 2014 DRAFT
9. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 5
Fig. 2. Example for SDCSS on 1 channel.
situation when a spectrum sensor returns a busy status for an idle channel (i.e., the transmission opportunity is
overlooked).
Assume that transmission signals from PUs are complex-valued PSK signals while the noise at the SUs is
independent and identically distributed circularly symmetric complex Gaussian CN (0, N0) [2]. Then, the detection
and false alarm probabilities experienced by SU i for the channel j can be calculated as [2]
Pij
d εij
, τij
= Q
εij
N0
− γij
− 1
τijfs
2γij + 1
, (1)
Pij
f εij
, τij
= Q
εij
N0
− 1 τijfs
= Q 2γij + 1Q−1
Pij
d εij
, τij
+ τijfsγij
, (2)
where i ∈ [1, N] is the SU index, j ∈ [1, M] is the channel index, εij
is the detection threshold for the energy detec-
tor, γij
is the signal-to-noise ratio (SNR) of the PU’s signal at the SU, fs is the sampling frequency, N0 is the noise
power, τij
is the sensing time of SU i on channel j, and Q (.) is defined as Q (x) = 1/
√
2π
∞
x
exp −t2
/2 dt.
We assume that a general cooperative sensing scheme, namely a-out-of-b rule, is employed by each SU to
determine the idle/busy status of each channel based on reported sensing results from other SUs. Under this
scheme, an SU will declare that a channel is busy if a or more messages out of b sensing messages report that the
underlying channel is busy. The a-out-of-b rule covers different rules including OR, AND and majority rules as
special cases. In particular, a = 1 corresponds to the OR rule; if a = b then it is the AND rule; and the majority
rule has a = ⌈b/2⌉.
To illustrate the operations of the a-out-of-b rule, let us consider a simple example shown in Fig. 2. Here, we
assume that 3 SUs collaborate to sense channel one with a = 2 and b = 3. After sensing channel one, all SUs
exchange their sensing outcomes. SU3 receives the reporting results comprising two “1” and one “0” where “1”
means that the channel is busy and “0” means channel is idle. Because the total number of “1s” is two which is
larger than or equal to a = 2, SU3 outputs the “1” in the final sensing result, namely the channel is busy.
Let us consider a particular channel j. Let SU
j denote the set of SUs that sense channel j, bj = SU
j be the
number of SUs sensing channel j, and aj be the number of messages indicating that the underlying channel is
busy. Then, the final decision on the spectrum status of channel j under the a-out-of-b rule has detection and false
June 6, 2014 DRAFT
10. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 6
TABLE I
CHANNEL ASSIGNMENT EXAMPLE FOR SUS (X DENOTES AN ASSIGNMENT)
Channel
1 2 3 4 5
1 x x x
2 x x
SU 3 x x x
4
5 x x
alarm probabilities that can be written as [25]
Pj
u εj
, τj
, aj =
bj
l=aj
Cl
bj
k=1 i1∈Φk
l
Pi1j
u
i2∈SU
j Φk
l
¯Pi2j
u , (3)
where u represents d or f as we calculate the probability of detection Pj
d or false alarm Pj
f , respectively; ¯P
is defined as ¯P = 1 − P; Φk
l in (3) denotes a particular set with l SUs whose sensing outcomes suggest that
channel j is busy given that this channel is indeed busy and idle as u represents d and f, respectively. Here, we
generate all possible combinations of Φk
l where there are indeed Cl
bj
combinations. Also, εj
= εij
, τj
= τij
,
i ∈ SU
j represent the set of detection thresholds and sensing times, respectively. For brevity, Pj
d εj
, τj
, aj and
Pj
f εj
, τj
, aj are sometimes written as Pj
d and Pj
f in the following.
Each SU exchanges the sensing results on its assigned channels with other SUs over a control channel, which
is assumed to be always available (e.g., it is owned by the secondary network). To avoid collisions among these
message exchanges, we assume that there are N reporting time slots for N SUs each of which has length equal to
tr. Hence, the total time for exchanging sensing results among SUs is Ntr. Note that the set of channels assigned
to SU i for sensing, namely Si, is a subset of all channels and these sets can be different for different SUs. An
example of channel assignment (i.e., channel sensing sets) is presented in Table I. In this table, SU 4 is not assigned
any channel. Hence, this SU must rely on the sensing results of other SUs to determine the spectrum status.
Remark 1: In practice, the idle/busy status of primary system on a particular channel can be arbitrary and would
not be synchronized with the operations of the SUs (i.e., the idle/busy status of any channel can change in the
middle of a cycle). Hence, to strictly protect the PUs, SUs should continuously scan the spectrum of interest and
evacuate from an exploited channel as soon as the PU changes from an idle to a busy state. However, this continuous
spectrum monitoring would be very costly to implement since each SU should be equipped with two half-duplex
transceivers to perform spectrum sensing and access at the same time. A more efficient protection method for PUs
is to perform periodic spectrum sensing where SUs perform spectrum sensing at the beginning of each fixed-length
interval and exploits available frequency bands for data transmission during the remaining time of the interval. In
this paper, we assume that the idle/busy status of each channel remains the same in each cycle, which enables us
to analyze the system throughput. In general, imposing this assumption would not sacrifice the accuracy of our
throughput analysis if PUs maintain their idle/busy status for a sufficiently long time. This is actually the case for
many practical scenarios such as in the TV bands, as reported by several recent studies [34]. In addition, our MAC
protocol that is developed under this assumption would result in very few collisions with PUs because the cycle
time is quite small compared to the typical intervals over which the active/idle statuses of PUs change.
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III. PERFORMANCE ANALYSIS AND OPTIMIZATION FOR COGNITIVE MAC PROTOCOL
We present the cognitive MAC protocol design, performance analysis, and optimization for the multi-channel
CRNs in this section.
A. Cognitive MAC Protocol Design
We assume that time is divided into fixed-size cycles and it is assumed that SUs can perfectly synchronize with
each other (i.e., there is no synchronization error) [11]. We propose a synchronized multi-channel MAC protocol for
dynamic spectrum sharing as follows. The MAC protocol has four phases in each cycle as illustrated in Fig. 3. The
beacon signal is sent on the control channel to achieve synchronization in the first phase [11] which is presented in
the simple manner as follows. At the beginning of this phase, each SU senses the beacon signal from the volunteered
synchronized SU which is the first SU sending the beacon. If an SU does not receive any beacon, it selects itself as
the volunteered SU and sends out the beacon for synchronization. In the second phase, namely the sensing phase of
length τ, all SUs simultaneously perform spectrum sensing on their assigned channels. Here, we have τ = maxi τi
,
where τi
= j∈Si
τij
is total sensing time of SU i, τij
is the sensing time of SU i on channel j, and Si is the set
of channels assigned for SU i. We assume that one separate channel is assigned as a control channel which is used
to exchange sensing results for reporting as well as broadcast a beacon signal for synchronization. This control
channel is assumed to be always available (e.g., it is owned by the secondary network). In the third phase, all SUs
exchange their sensing results with each other via the control channel. Based on these received sensing results,
each SU employs SDCSS techniques to decide the channel status of all channels and hence has a set of available
channels. Then each SU transmitter will choose one available channel randomly (which is used for contention and
data transmission) and inform it to the corresponding SU receiver via the control channel.
In the fourth phase, SUs will participate in contention and data transmission on their chosen channels. We assume
that the length of each cycle is sufficiently large so that SUs can transmit several packets during this data contention
and transmission phase. In particular, we employ the p-persistent CSMA principle [30] to devise our cognitive MAC
protocol. In this protocol, each SU attempts to transmit on the chosen channel with a probability of p if it senses
an available channel (i.e., no other SUs transmit data on its chosen channel). In case the SU decides not to transmit
(with probability of 1 − p), it will sense the channel and attempt to transmit again in the next slot with probability
p. If there is a collision, the SU will wait until the channel is available and attempt to transmit with probability p
as before.
The standard 4-way handshake with RTS/CTS (request-to-send/clear-to-send) [31] will be employed to reserve
a channel for data transmission. So the SU choosing to transmit on each available channel exchanges RTS/CTS
messages before transmitting its actual data packet. An acknowledgment (ACK) from the receiver is transmitted
to the transmitter for successful reception of any packet. The detailed timing diagram of this MAC protocol is
presented in Fig. 3.
Remark 2: For simplicity, we consider the fixed control channel in our design. However, extensions to consider
dynamic control channel selections to avoid the congestion can be adopted in our proposed framework. More
information on these designs can be found in [32].
B. Saturation Throughput Analysis
In this section, we analyze the saturation throughput of the proposed cognitive p-persistent CSMA protocol
assuming that there are no reporting errors in exchanging the spectrum sensing results among SUs. Because there
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. . . . . .. . .Data Data
One cycle
time. . .
: Data channel j
Report
: Idle (I)
I(1)
RTS PDDIFS
Epoch 1
PS PD SIFS ACK PDSIFS
: Collision (C)
C(1) C(k) I(k+1) U
: Useful transmission (U)
SYN Sensing
DIFS CTS PDRTS PD
jDC
jDC
Epoch m
CC
CC : Control channel
. . . . . . . . .
I C . . . I C I U I . . . C I U
Contention and Data Transmission
Epoch 1 Epoch m. . . . . .
Fig. 3. Timing diagram of cognitive p-persistent CSMA protocol for one specific channel j.
are no reporting errors, all SUs acquire the same sensing results for each channel, which implies that they make the
same final sensing decisions since the same a-out-b aggregation rule is employed for each channel. In the analysis,
transmission time is counted in terms of contention time slot, which is assumed to be v seconds. Each data packet
is assumed to be of fixed size of PS time slots. Detailed timing diagram of the p-persistent CSMA MAC protocol
is illustrated in Fig. 3.
Any particular channel alternates between idle and busy periods from the viewpoint of the secondary system
where each busy period corresponds to either a collision or a successful transmission. We use the term “epoch”
to refer to the interval between two consecutive successful transmissions. This means an epoch starts with an idle
period followed by some alternating collision periods and idle periods before ending with a successful transmission
period. Note that an idle period corresponds to the interval between two consecutive packet transmissions (collisions
or successful transmissions).
Recall that each SU chooses one available channel randomly for contention and transmission according to the
final cooperative sensing outcome. We assume that upon choosing a channel, an SU keeps contending and accessing
this channel until the end of the current cycle. In the case of missed detection (i.e., the PU is using the underlying
channel but the sensing outcome suggests that the channel is available), there will be collisions between SUs and
the PU. Therefore, RTS and CTS exchanges will not be successful in this case even though SUs cannot differentiate
whether they collide with other SUs or the PU. Note that channel accesses of SUs due to missed detections do not
contribute to the secondary system throughput.
To calculate the throughput for the secondary network, we have to consider all scenarios of idle/busy statuses
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of all channels and possible mis-detection and false alarm events for each particular scenario. Specifically, the
normalized throughput per one channel achieved by our proposed MAC protocol, NT τij
, {aj} , p, {Si} can
be written as
NT =
M
k0=1
C
k0
M
l0=1 j1∈Ψ
l0
k0
Pj1
(H0)
j2∈SΨ
l0
k0
Pj2
(H1) × (4)
k0
k1=1
C
k1
k0
l1=1 j3∈Θ
l1
k1
¯Pj3
f
j4∈Ψ
l0
k0
Θ
l1
k1
Pj4
f × (5)
M−k0
k2=0
C
k2
M−k0
l2=1 j5∈Ω
l2
k2
¯Pj5
d
j6∈SΨ
l0
k0
Ω
l2
k2
Pj6
d × (6)
T ne
p (τ, {aj} , p) . (7)
The quantity (4) represents the probability that there are k0 available channels, which may or may not be correctly
determined by the SDCSS. Here, Ψl0
k0
denotes a particular set of k0 available channels out of M channels whose
index is l0. In addition, the quantity (5) describes the probability that the SDCSS indicates k1 available channels
whereas the remaining available channels are overlooked due to sensing errors where Θl1
k1
denotes the l1-th set
with k1 available channels. For the quantity in (6), k2 represents the number of channels that are not available but
the sensing outcomes indicate that they are available (i.e., due to misdetection) where Ωl2
k2
denotes the l2-th set
with k2 mis-detected channels. The quantity in (6) describes the probability that the sensing outcomes due to SUs
incorrectly indicates k2 available channels. Finally, T ne
p (τ, {aj} , p) in (7) denotes the conditional throughput for a
particular realization of sensing outcomes corresponding to two sets Θl1
k1
and Ωl2
k2
.
Therefore, we have to derive the conditional throughput T ne
p (τ, {aj} , p) to complete the throughput analysis,
which is pursued in the following. Since each SU randomly chooses one available channel according to the SDCSS
for contention and access, the number of SUs actually choosing a particular available channel is a random number.
In addition, the SDCSS suggests that channels in Θl1
k1
∪ Ωl2
k2
are available for secondary access but only channels
in Θl1
k1
are indeed available and can contribute to the secondary throughput (channels in Ωl2
k2
are misdetected
by SUs). Let {nj} = {n1, n2, . . . , nke
} be the vector describing how SUs choose channels for access where
ke = Θl1
k1
∪ Ωl2
k2
and nj denotes the number of SUs choosing channel j for access. Therefore, the conditional
throughput T ne
p (τ, {aj} , p) can be calculated as follows:
T ne
p (τ, {aj} , p) =
{nj }:
j∈Θ
l1
k1
∪Ω
l2
k2
nj =N
P ({nj}) × (8)
j2∈Θ
l1
k1
1
M
T ne
j2
(τ, {aj2
} , p |n = nj2
) I (nj2
0) , (9)
where P ({nj}) in (8) represents the probability that the channel access vector {nj} is realized (each channel j
where j ∈ Θl1
k1
∪ Ωl2
k2
is selected by nj SUs). The sum in (9) describes the normalized throughput per channel due
to a particular realization of the access vector {nj}. Therefore, it is equal to the total throughput achieved by all
available channels (in the set Θl1
k1
) divided by the total number of channels M. Here, T ne
j2
(τ, {aj2
} , p |n = nj2
)
denotes the conditional throughput achieved by a particular channel j2 when there are nj2
contending on this
channel and I (nj2
0) represents the indicator function, which is equal to zero if nj2
= 0 (i.e., no SU chooses
channel j2) and equal to one, otherwise. Note that the access of channels in the set Ωl2
k2
due to missed detection
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does not contribute to the system throughput, which explains why we do not include these channels in the sum in
(9).
Therefore, we need to drive P ({nj}) and T ne
j2
(τ, {aj2
} , p |n = nj2
) to determine the normalized throughput.
Note that the sensing outcome due to the SDCSS is the same for all SUs and each SU chooses one channel in the
set of ke = Θl1
k1
∪ Ωl2
k2
channels randomly. Therefore, the probability P ({nj}) can be calculated as follows:
P ({nj}) =
N
{nj}
1
ke
j∈Θ
l1
k1
∪Ω
l2
k2
nj
(10)
=
N
{nj}
1
ke
N
, (11)
where
N
{nj}
is the multinomial coefficient which is defined as
N
{nj}
=
N
n1, n2, . . . , nk
=
N!
n1!n2!...nk! .
The calculation of the conditional throughput T ne
j2
(τ, {aj2
} , p |n = nj2
) must account for the overhead due to
spectrum sensing and exchanges of sensing results among SUs. Let us define TR = Ntr where tr is the report
time from each SU to all the other SUs; τ = maxi τi
is the total the sensing time; ¯Tj2
cont is the average total time
due to contention, collisions, and RTS/CTS exchanges before a successful packet transmission; TS is the total time
for transmissions of data packet, ACK control packet, and overhead between these data and ACK packets. Then,
the conditional throughput T ne
j2
(τ, {aj2
} , p |n = nj2
) can be written as
T ne
j2
(τ, {aj2
} , p |n = nj2
) =
T − τ − TR
¯Tj2
cont + TS
TS
T
, (12)
where ⌊.⌋ denotes the floor function and recall that T is the duration of a cycle. Note that T −τ−TR
¯T
j2
cont+TS
denotes
the average number of successfully transmitted packets in one particular cycle excluding the sensing and reporting
phases. Here, we omit the length of the synchronization phase, which is assumed to be negligible.
To calculate ¯Tj2
cont, we define some further parameters as follows. Let denote TC as the duration of the collision;
¯TS is the required time for successful RTS/CTS transmission. These quantities can be calculated under the 4-way
handshake mechanism as [30]
TS = PS + 2SIFS + 2PD + ACK
¯TS = DIFS + RTS + CTS + 2PD
TC = RTS + DIFS + PD
, (13)
where PS is the packet size, ACK is the length of an ACK packet, SIFS is the length of a short interframe
space, DIFS is the length of a distributed interframe space, PD is the propagation delay where PD is usually
very small compared to the slot size v.
Let Ti,j2
I be the i-th idle duration between two consecutive RTS/CTS transmissions (they can be collisions
or successes) on a particular channel j2. Then, Ti,j2
I can be calculated based on its probability mass function
(pmf), which is derived in the following. Recall that all quantities are defined in terms of number of time slots.
Now, suppose there are nj2
SUs choosing channel j2, let Pj2
S , Pj2
C and Pj2
I be the probabilities of a generic slot
corresponding to a successful transmission, a collision and an idle slot, respectively. These quantities are calculated
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as follows
Pj2
S = nj2
p (1 − p)
nj2
−1
(14)
Pj2
I = (1 − p)
nj2
(15)
Pj2
C = 1 − Pj2
S − Pj2
C , (16)
where p is the transmission probability of an SU in a generic slot. Note that ¯Tj2
cont is a random variable (RV)
consisting of several intervals corresponding to idle periods, collisions, and one successful RTS/CTS transmission.
Hence this quantity for channel j2 can be written as
¯Tj2
cont =
Nj2
c
i=1
TC + Ti,j2
I + T
Nj2
c +1,j2
I + ¯TS, (17)
where Nj2
c is the number of collisions before the first successful RTS/CTS exchange. Hence it is a geometric RV
with parameter 1 − Pj2
C / ¯Pj2
I (where ¯Pj2
I = 1 − Pj2
I ). Its pmf can be expressed as
fNc
X (x) =
Pj2
C
¯Pj2
I
x
1 −
Pj2
C
¯Pj2
I
, x = 0, 1, 2, . . . (18)
Also, Ti,j2
I represents the number of consecutive idle slots, which is also a geometric RV with parameter 1 − Pj2
I
with the following pmf
fI
X (x) = Pj2
I
x
1 − Pj2
I , x = 0, 1, 2, . . . (19)
Therefore, ¯Tj2
cont can be written as follows [30]:
¯Tj2
cont = ¯Nj2
c TC + ¯Tj2
I
¯Nj2
c + 1 + ¯TS, (20)
where ¯Tj2
I and ¯Nj2
c can be calculated as
¯Tj2
I =
(1 − p)
nj2
1 − (1 − p)
nj2
(21)
¯Nj2
c =
1 − (1 − p)
nj2
nj2
p (1 − p)
nj2
−1 − 1. (22)
These expressions are obtained by using the pmfs of the corresponding RVs given in (18) and (19), respectively
[30].
C. Semi-Distributed Cooperative Spectrum Sensing and p-persistent CSMA Access Optimization
We determine optimal sensing and access parameters to maximize the normalized throughput for our proposed
SDCSS and p-persistent CSMA protocol. Here, we assume that the sensing sets SU
j for different channels j have
been given. Optimization of these sensing sets is considered in the next section. Note that the optimization performed
in this paper is different from those in [4], [5] because the MAC protocols and sensing algorithms in the current
and previous works are different. The normalized throughput optimization problem can be presented as
max
{τij },{aj },p
NT p τij
, {aj} , p, {Si} (23)
s.t. Pj
d εj
, τj
, aj ≥ Pj
d, j ∈ [1, M] (24)
0 τij
≤ T, 0 ≤ p ≤ 1, (25)
where Pj
d is the detection probability for channel j; Pj
d denotes the target detection probability; εj
and τj
represent
the vectors of detection thresholds and sensing times on channel j, respectively; aj describes the parameter of the
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aj-out-of-bj aggregation rule for SDCSS on channel j with bj = |SU
j | where recall that SU
j is the set of SUs
sensing channel j. The optimization variables for this problem are sensing times τij
and parameters aj of the
sensing aggregation rule, and transmission probability p of the MAC protocol.
It was shown in [2] that the constraints on detection probability should be met with equality at optimality under
the energy detection scheme and single-user scenario. This is quite intuitive since lower detection probability implies
smaller sensing time, which leads to higher throughput. This is still the case for our considered multi-user scenario
as can be verified by the conditional throughput formula (12). Therefore, we can set Pj
d εj
, τj
, aj = Pj
d to solve
the optimization problem (23)-(25).
However, Pj
d εj
, τj
, aj is a function of Pij
d for all SUs i ∈ SU
j since we employ the SDCSS scheme in this
paper. Therefore, to simplify the optimization we set Pij
d = Pj∗
d for all SUs i ∈ SU
j (i.e., all SUs are required to
achieve the same detection probability for each assigned channel). Then, we can calculate Pj∗
d by using (3) for a
given value of Pj
d. In addition, we can determine Pij
f with the obtained value of Pj∗
d by using (2), which is the
function of sensing time τij
.
Even after these steps, the optimization problem (23)-(25) is still very difficult to solve. In fact, it is the mixed
integer non-linear problem since the optimization variables aj take integer values while other variables take real
values. Moreover, even the corresponding optimization problem achieved by relaxing aj to real variables is a difficult
and non-convex problem to solve since the throughput in the objective function (23) given in (7) is a complicated
and non-linear function of optimization variables.
Algorithm 1 OPTIMIZATION OF SENSING AND ACCESS PARAMETERS
1: Assume we have the sets of all SU i, {Si}. Initialize τij
, j ∈ Si, the sets of {aj} for all channel j and p.
2: For each chosen p ∈ [0, 1], find ¯τij
and {¯aj} as follows:
3: for each possible set {aj} do
4: repeat
5: for i = 1 to N do
6: Fix all τi1j
, i1 = i.
7: Find the optimal ¯τij
as ¯τij
= argmax
0τij ≤T
NT p τij
, {aj} , p .
8: end for
9: until convergence
10: end for
11: The best ¯τij
, {¯aj} is determined for each value of p as ¯τij
, {¯aj} = argmax
{aj },{¯τij }
NT ¯τij
, {aj} , p .
12: The final solution ¯τij
, {¯aj} , ¯p is determined as ¯τij
, {¯aj} , ¯p = argmax
{¯τij },{¯aj },p
NT ¯τij
, {¯aj} , p .
Given this observation, we have devised Alg. 1 to determine the solution for this optimization problem based on
the coordinate-descent searching techniques. The idea is that at one time we fix all variables while searching for the
optimal value of the single variable. This operation is performed sequentially for all variables until convergence is
achieved. Since the normalized throughput given in (7) is quite insensitive with respect to p, we attempt to determine
the optimized values for ¯τij
, {¯aj} first for different values of p (steps 3–11 in Alg. 1) before searching the
optimized value of p in the outer loop (step 12 in Alg. 1). This algorithm converges to the fixed point solution since
we improve the objective value over iterations (steps 4–9). This optimization problem is non-convex in general.
However, we can obtain its optimal solution easily by using the bisection search technique since the throughput
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function is quite smooth [35]. For some specific cases such as in homogeneous systems [4], [23], [26], the underlying
optimization problem is convex, which can be solved efficiently by using standard convex optimization algorithms.
D. Optimization of Channel Sensing Sets
For the CRNs considered in the current work, the network throughput strongly depends on the availability of
different channels, the spectrum sensing time, and the sensing quality. Specifically, long sensing time τ reduces the
communications time on the available channels in each cycle of length T, which, therefore, decreases the network
throughput. In addition, poor spectrum sensing performance can also degrade the network throughput since SUs
can either overlook available channels (due to false alarm) or access busy channels (due to missed detection). Thus,
the total throughput of SUs can be enhanced by optimizing the access parameter p and sensing design, namely
optimizing the assignments of channels to SUs (i.e., optimizing the sensing sets for SUs) and the corresponding
sensing times.
Recall that we have assumed the channel sensing sets for SUs are fixed to optimize the sensing and access
parameters in the previous section. In this section, we attempt to determine an efficient channel assignment solution
(i.e., channel sensing sets) by solving the following problem
max
{Si},{aj }
NT ¯τij
, {aj} , ¯p, {Si} . (26)
Note that the optimal values of aj can only be determined if we have fixed the channel sensing set SU
j for each
channel j. This is because we aim to optimize the aj-out-of-bj aggregation rule of the SDCSS scheme for each
channel j where bj = |SU
j |. Since aj takes integer values and optimization of channel sensing sets SU
j also involves
integer variables where we have to determine the set of SUs SU
j assigned to sense each channel j. Therefore, the
optimization problem (26) is the non-linear integer program, which is NP-hard [36]. In the following, we present
both brute-force search algorithm and low-complexity greedy algorithm to solve this problem.
1) Brute-force Search Algorithm: Due to the non-linear and combinatorial structure of the formulated channel
assignment problem, it would be impossible to explicitly determine the optimal closed form solution for problem
(26). However, we can employ the brute-force search (i.e., the exhaustive search) to determine the best channel
assignment. Specifically, we can enumerate all possible channel assignment solutions. Then, for each channel
assignment solution (i.e., sets SU
j for all channels j), we employ Alg. 1 to determine the best spectrum sensing
and accessing parameters τij
, {aj} , p and calculate the corresponding total throughput by using the throughput
analytical model in III-A. The channel assignment achieving the maximum throughput together with its best spectrum
sensing and accessing parameters provides the best solution for the optimization problem (26).
2) Low-Complexity Greedy Algorithm: We propose another low-complexity and greedy algorithm to find the
solution for this problem, which is described in Alg. 2. In this algorithm, we perform the initial channel assignment
in step 1, which works as follows. We first temporarily assign all channels for each SU. Then, we run Alg. 1 to
find the optimal sensing times for this temporary assignment, i.e., to determine ¯τij
, which is used to assign one
SU to each channel so that the total sensing time is minimized. In particular, the initial channel assignments are
set according to the solution of the optimization problem (27)-(28) presented in the following.
min
{xij }
i,j
τij
xij (27)
s.t.
i
xij = 1, j ∈ [1, M] . (28)
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where xij are binary variables representing the channel assignments where xij = 1 if channel j is allocated for SU
i (i.e., j ∈ Si) and xij = 0, otherwise. We employ the well-known Hungarian algorithm [33] to solve this problem.
Then, we perform further channel assignments in steps 2-18 of Alg. 2. Specifically, to determine one channel
assignment in each iteration, we temporarily assign one channel to the sensing set Si of each SU i and calculate
the increase of throughput for such channel assignment ∆Tij with the optimized channel and access parameters
obtained by using Alg. 1 (step 6). We then search for the best channel assignment (¯i, ¯j) = argmax
i,j∈SSi
∆Tij and
actually perform the corresponding channel assignment if ∆T¯i¯j δ (steps 7–10).
In Alg. 2, δ 0 is a small number which is used in the stopping condition for this algorithm (step 11). In
particular, if the increase of the normalized throughput due to the new channel assignment is negligible in any
iteration (i.e., the increase of throughput is less than δ) then the algorithm terminates. Therefore, we can choose
δ to efficiently balance the achievable throughput performance with the algorithm running time. In the numerical
studies, we will choose δ equal to 10−3
× NTc.
The convergence of Alg. 2 can be explained as follows. Over the course of this algorithm, we attempt to increase
the throughput by performing additional channel assignments. It can be observed that we can increase the throughput
by allowing i) SUs to achieve better sensing performance or ii) SUs to reduce their sensing times. However, these
two goals could not be achieved concurrently due to the following reason. If SUs wish to improve the sensing
performance via cooperative spectrum sensing, we should assign more channels to each of them. However, SUs
would spend longer time sensing the assigned channels with the larger sensing sets, which would ultimately decrease
the throughput. Therefore, there would exist a point when we cannot improve the throughput by performing further
channel assignments, which implies that Alg. 2 must converge.
There is a key difference in the current work and [5] regarding the sensing sets of SUs. Specifically, the sets of
assigned channels are used for spectrum sensing and access in [5]. However, the sets of assigned channels are used
for spectrum sensing only in the current work. In addition, the sets of available channels for possible access at SUs
are determined based on the reporting results, which may suffer from communications errors. We will investigate
the impact of reporting errors on the throughput performance in Section IV.
E. Complexity Analysis
In this section, we analyze the complexity of the proposed brute-force search and low-complexity greedy
algorithms.
1) Brute-force Search Algorithm: To determine the complexity of the brute-force search algorithm, we need to
calculate the number of possible channel assignments. Since each channel can be either allocated or not allocated to
any SU, the number of channel assignments is 2MN
. Therefore, the complexity of the brute-force search algorithm
is O 2MN
. Note that to obtain the best channel assignment solution, we must run Alg. 1 to find the best sensing
and access parameters for each potential channel assignment, calculate the throughput achieved by such optimized
configuration, and compare all the throughput values to determine the best solution.
2) Low-complexity Greedy Algorithm: In step 1, we run Hungarian algorithm to perform the first channel
assignment for each SU i. The complexity of this operation can be upper-bounded by O M2
N (see [33] for
more details). In each iteration in the assignment loop (i.e., steps 2-18), each SU i needs to calculate the increases
of throughput for different potential channel assignments. Then, we select the assignment resulting in maximum
increase of throughput. Hence, the complexity involved in these tasks is upper-bounded by MN since there are
at most M channels to assign for each of N SUs. Also, the number of assignments to perform is upper bounded
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19. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 15
Algorithm 2 GREEDY ALGORITHM
1: Initial channel assignment is obtained as follows:
• Temporarily perform following channel assignments Si = S, i ∈ [1, N]. Then, run Alg. 1 to obtain optimal
sensing and access parameters ¯τij
, {¯aj} , ¯p .
• Employ Hungarian algorithm [33] to allocate each channel to exactly one SU to minimize the total cost
where the cost of assigning channel j to SU i is ¯τij
(i.e., to solve the optimization problem (27)-(28)).
• The result of this Hungarian algorithm is used to build the initial channel assignment sets {Si} for different
SU i.
2: Set continue = 1.
3: while continue = 1 do
4: Optimize sensing and access parameters for current channel assignment solution {Si} by using Alg. 1.
5: Calculate the normalized throughput NT c = NT ¯τij
, {¯aj} , ¯p, {Si} for the optimized sensing and
access parameters.
6: Each SU i calculates the increase of throughput if it is assigned one further potential channel j as ∆Tij =
NT ¯τij
, {¯aj} , ¯p, Si − NT c where Si = Si ∪ j, Sl = Sl, l = i, and ¯τij
, {¯aj} , ¯p are determined
by using Alg. 1 for the temporary assignment sets Si .
7: Find the “best” assignment (¯i, ¯j) as (¯i, ¯j) = argmax
i,j∈SSi
∆Tij.
8: if ∆T¯i¯j δ then
9: Assign channel ¯j to SU ¯i: Si = Si ∪ j.
10: else
11: Set continue = 0.
12: end if
13: end while
14: if continue = 1 then
15: Return to step 2.
16: else
17: Terminate the algorithm.
18: end if
by MN (i.e., iterations of the main loop). Therefore, the complexity of the assignment loop is upper-bounded
by M2
N2
. Therefore, the total worst-case complexity of Alg. 2 is O M2
N + M2
N2
= O M2
N2
, which is
much lower than that of the brute-force search algorithm. As a result, Table II in Section V demonstrates that
our proposed greedy algorithms achieve the throughput performance very close to that achieved by the brute-force
search algorithms albeit they require much lower computational complexity.
F. Practical Implementation Issues
In our design, the spectrum sensing and access operation is distributed, however, channel assignment is performed
in centralized manner. In fact, one SU is pre-assigned as a cluster head, which conducts channel assignment for
SUs (i.e., determine channel sensing sets for SUs). For fairness, we can assign the SU as the cluster head in the
round-robin manner. To perform channel assignment, the cluster head is responsible for estimating Pj (H0). Upon
determining the channel sensing sets for all SUs, the cluster head will forward the results to the SUs. Then based
June 6, 2014 DRAFT
20. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 16
on these pre-determined sensing sets, SUs will perform spectrum sensing and run the underlying MAC protocol
to access the channel distributively in each cycle. It is worth to emphasize that the sensing sets for SUs are only
determined once the probabilities Pj (H0) change, which would be quite infrequent in practice (e.g., in the time
scale of hours or even days). Therefore, the estimation cost for Pj (H0) and all involved communication overhead
due to sensing set optimization operations would be acceptable.
IV. CONSIDERATION OF REPORTING ERRORS
In this section, we consider the impact of reporting errors on the performance of the proposed joint SDCSS and
access design. Note that each SU relies on the channel sensing results received from other SUs in SU
j to determine
the sensing outcome for each channel j. If there are reporting errors then different SUs may receive different
channel sensing results, which lead to different final channel sensing decisions. The throughput analysis, therefore,
must account for all possible error patterns that can occur in reporting channel sensing results. We will present the
cooperative sensing model and throughput analysis considering reporting errors in the following.
A. Cooperative Sensing with Reporting Errors
In the proposed SDCSS scheme, each SU i1 collects sensing results for each channel j from all SUs i2 ∈ SU
j
who are assigned to sense channel j. In this section, we consider the case where there can be errors in reporting the
channel sensing results among SUs. We assume that the channel sensing result for each channel transmitted by one
SU to other SUs is represented by a single bit whose 1/0 values indicates that the underlying channel is available
and busy, respectively. In general, the error probability of the reporting message between SUs i1 and i2 depends
on the employed modulation scheme and the signal to noise ratio (SNR) of the communication channel between
the two SUs. We denote the bit error probability of transmitting the reporting bit from SU i2 to SU i1 as Pi1i2
e . In
addition, we assume that the error processes of different reporting bits for different SUs are independent. Then, the
probability of detection and probability of false alarm experienced by SU i1 on channel j with the sensing result
received from SU i2 can be written as
Pi1i2j
u,e =
Pi2j
u 1−Pi1i2
e + 1−Pi2j
u Pi1i2
e if i1 =i2
Pi2j
u if i1 =i2
(29)
where u ≡ d and u ≡ f represents probabilities of detection and false alarm, respectively. Note that we have
Pi1i2
e = 0 if i1 = i2 = i since there is no sensing result exchange involved in this case. As SU i employs the
aj-out-of-bj aggregation rule for channel j, the probabilities of detection and false alarm for SU i on channel j
can be calculated as
˜Pij
u εj
, τj
, aj =
bj
l=aj
Cl
bj
k=1 i1∈Φl
k
Pii1j
u,e
i2∈SU
j Φl
k
¯Pii2j
u,e . (30)
Again, u ≡ d and u ≡ f represent the corresponding probabilities of detection or false alarm, respectively. Recall that
SU
j represents the set of SUs who are assigned to sense channel j; thus, we have bj = |SU
j | and 1 ≤ aj ≤ bj = SU
j .
For brevity, ˜Pij
u εj
, τj
, aj is written as ˜Pij
u in the following.
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21. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 17
B. Throughput Analysis Considering Reporting Errors
In order to analyze the saturation throughput for the case there are reporting errors, we have to consider all possible
scenarios due to the idle/busy status of all channels, sensing outcomes given by different SUs, and error/success
events in the sensing result exchange processes. For one such combined scenario we have to derive the total
conditional throughput due to all available channels. Illustration of different involved sets for one combined scenario
of following analysis is presented in Fig. 4. In particular, the normalized throughput considering reporting errors
can be expressed as follows:
NT =
M
k0=1
C
k0
M
l0=1 j1∈Ψ
l0
k0
Pj1
(H0)
j2∈SΨ
l0
k0
Pj2
(H1) × (31)
j3∈Ψ
l0
k0
|SU
j3
|
k1=0
C
k1
|SU
j3
|
l1=1 i0∈Θ
l1
k1,j3
¯Pi0,j3
f
i1∈SU
j3
Θ
l1
k1,j3
Pi1,j3
f × (32)
j4∈SΨ
l0
k0
|SU
j4
|
k2=0
C
k2
|SU
j4
|
l2=1 i2∈Ω
l2
k2,j4
¯Pi2,j4
d
i3∈SU
j4
Ω
l2
k2,j4
Pi3,j4
d × (33)
i4∈SU
k1
k3=0
C
k3
k1
l3=1 i5∈Φ
l3
k3,j3
¯Pi4,i5
e
i6∈Θ
l1
k1,j3
Φ
l3
k3,j3
Pi4,i6
e × (34)
|SU
j3
|−k1
k4=0
C
k4
|SU
j3
|−k1
l4=1 i7∈Λ
l4
k4,j3
Pi4,i7
e
i8∈SU
j3
Θ
l1
k1,j3
Λ
l4
k4,j3
¯Pi4,i8
e × (35)
i9∈SU
k2
k5=0
C
k5
k2
l5=1 i10∈Ξ
l5
k5,j4
¯Pi9,i10
e
i11∈Ω
l2
k2,j4
Ξ
l5
k5,j4
Pi9,i11
e × (36)
|SU
j4
|−k2
k6=0
C
k6
|SU
j4
|−k2
l6=1 i12∈Γ
l6
k6,j4
Pi9,i12
e
i13∈SU
j4
Ω
l2
k2,j4
Γ
l6
k6,j4
¯Pi9,i13
e × (37)
T re
p (τ, {aj} , p) , (38)
where T re
p (τ, {aj} , p) denotes the conditional throughput for one combined scenario discussed above. In (31), we
generate all possible sets where k0 channels are available for secondary access (i.e., they are not used by PUs)
while the remaining channels are busy. There are Ck0
M such sets and Ψl0
k0
represents one particular set of available
channels. The first product term in (31) denotes the probability that all channels in Ψl0
k0
are available while the
second product term describes the probability that the remaining channels are busy.
Then, for one particular channel j3 ∈ Ψl0
k0
, we generate all possible sets with k1 SUs in SU
j3
(SU
j3
is the set of SUs
who are assigned to sense channel j3) whose sensing results indicate that channel j3 is available in (32). There are
Ck1
|SU
j3
|
sets and Θl1
k1,j3
denotes one such typical set. Again, the first product term in (32) is the probability that the
sensing outcomes of all SUs in Θl1
k1,j3
indicate that channel j3 is available; and the second term is the probability
that the sensing outcomes of all SUs in the remaining set SU
j3
Θl1
k1,j3
indicate that channel j3 is not available.
In (33), for one specific channel j4 ∈ SΨl0
k0
, we generate all possible sets with k2 SUs in SU
j4
whose sensing
outcomes indicate that channel j4 is available due to missed detection. There are Ck2
|SU
j4
|
such sets and Ωl2
k2,j4
is
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22. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 18
5
5 4,
l
k j;
52
2 4 5 4, , ll
k j k j: ;
6
6 4,
l
k j*
2
4 2 4
6
6 4
,
,
lU
j k j
l
k j
S :
*
3
3 3,
l
k j)
31
1 3 3 3, , ll
k j k j4 )
4
4 3,
l
k j/
1
3 1 3
4
4 3
,
,
lU
j k j
l
k j
S 4
/
1
1 3,
l
k j4 1
3 1 3, lU
j k jS 4 2
2 4,
l
k j: 2
4 2 4, lU
j k jS 4
0
0
0
03
:Setof vacantchannels
or spectrum holes;
l
k
l
kj
0
0
0
04
:Setof busy
channels;
l
k
l
k
S
j S
SUs detect
a “spectrum
hole”
SUs mis-detect
a “spectrum
hole”
SUs mis-
detect a busy
channel
SUs detect a
busy channel
3
U
jS 4
U
jS
a) b)
c) d)
4 9Setof SUsfromwhomSU / collects wrong
information (reporting errors)
i i
4 9
3 4
Setof SUsfromwhomSU / collects
information that channel / isvacant
i i
j j
Fig. 4. Illustration of different sets in one combined scenario.
a typical one. Similarly, the first product term in (33) is the probability that the sensing outcomes of all SUs in
Ωl2
k2,j4
indicate that channel j4 is available; and the second term is the probability that the sensing outcomes of all
SUs in the remaining set SU
j4
Ωl2
k2,j4
indicate that channel j4 is not available.
Recall that for any specific channel j, each SU in SU
(the set of all SUs) receives sensing results from a group
of SUs who are assigned to sense the channel j. In (34), we consider all possible error events due to message
exchanges from SUs in Θl1
k1,j3
. The first group denoted as Φl3
k3,j3
includes SUs in Θl1
k1,j3
has its sensing results
received at SU i4 ∈ SU
indicating that channel j3 available (no reporting error) while the second group of SUs
Θl1
k1,j3
Φl3
k3,j3
has the sensing results received at SU i4 ∈ SU
suggesting that channel j3 is not available due to
reporting errors. For each of these two groups, we generate all possible sets of SUs of different sizes and capture the
corresponding probabilities. In particular, we generate all sets with k3 SUs i5 ∈ Φl3
k3,j3
where SU i4 collects correct
sensing information from SUs i5 (i.e., there is no error on the channel between i4 and i5). Similar expression is
presented for the second group in which we generate all sets of k4 SUs i6 ∈ Θl1
k1,j3
Φl3
k3,j3
where SU i4 collects
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23. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 19
wrong sensing information from each SU i6 (i.e., there is an error on the channel between i4 and i6). Similarly,
we present the possible error events due to exchanges of sensing results from the set of SUs SU
j3
Θl1
k1,j3
in (35).
In (36) and (37), we consider all possible error events due to sensing result exchanges for channel j4 ∈ SΨl0
k0
.
Here, each SU in SU
collects sensing result information from two sets of SUs in Ωl2
k2,j4
and SU
j4
Ωl2
k2,j4
, respectively.
The first set includes SUs in Ωl2
k2,j4
whose sensing results indicate that channel j4 available due to missed detection,
while the second set includes SUs in SU
j4
Ωl2
k2,j4
whose sensing results indicate that channel j4 is not available.
Possible outcomes for the message exchanges due to the first set Ωl2
k2,j4
are captured in (36) where we present the
outcomes for two groups of this first set. For group one, we generate all sets with k5 SUs i10 ∈ Ξl5
k5,j4
where
SU i9 collects correct sensing information from SUs i10 (i.e., there is no error on the channel between i9 and
i10). For group two, we consider the remaining sets of SUs in Ωl2
k2,j4
Ξl5
k5,j4
where SU i9 receives wrong sensing
information from each SU i11 (i.e., there is an error on the channel between i9 and i11). Similar partitioning of
the set SU
j4
Ωl2
k2,j4
into two groups Γl6
k6,j4
and SU
j4
Ωl2
k2,j4
Γl6
k6,j4
with the corresponding message reporting error
patterns is captured in (37).
For each combined scenario whose probability is presented above, each SU i has collected sensing result
information for each channel, which is the sensing results obtained by itself or received from other SUs. Then,
each SU i determines the idle/busy status of each channel j by applying the aj-out-of-bj rule on the collected
sensing information. Let Sa
i be set of channels, whose status is “available” as being suggested by the aj-out-of-bj
rule at SU i. According to our design MAC protocol, SU i will randomly select one channel in the set Sa
i to
perform contention and transmit its data. In order to obtain the conditional throughput T re
p (τ, {aj} , p) for one
particular combined scenario, we have to reveal the contention operation on each actually available channel, which
is presented in the following.
Let Sa
i = Sa
1,i ∪ Sa
2,i where channels in Sa
1,i are actually available and channels in Sa
2,i are not available but the
SDCSS policy suggests the opposite due to sensing and/or reporting errors. Moreover, let ˆSa
1 = i∈SU Sa
1,i be the
set of actually available channels, which are detected by all SUs by using the SDCSS policy. Similarly, we define
ˆSa
2 = i∈SU Sa
2,i as the set of channels indicated as available by some SUs due to errors. Let ki
e = |Sa
i | be the
number of available channels at SU i; then SU i chooses one channel in Sa
i to transmit data with probability 1/ki
e.
In addition, let ˆSa
= ˆSa
1 ∪ ˆSa
2 be set of all “available” channels each of which is determined as being available by
at least one SU and let kmax = ˆSa
be the size of this set.
To calculate the throughput for each channel j, let Ψa
j be the set of SUs whose SDCSS outcomes indicate that
channel j is available and let Ψa
= j∈ ˆSa Ψa
j be the set of SUs whose SDCSS outcomes indicate that at least one
channel in the assigned spectrum sensing set is available. In addition, let us define Nj = Ψa
j and Nmax = |Ψa
|,
which describe the sizes of these sets, respectively. It is noted that Nmax ≤ N due to the following reason. In any
specific combination that is generated in Eqs. (31)–(37), there can be some SUs, denoted as {i}, whose sensing
outcomes indicate that all channels in the assigned spectrum sensing sets are not available (i.e., not available for
access). Therefore, we have Ψa
= SU
{i}, which implies Nmax ≤ N where N = SU
. Moreover, we assume
that channels in ˆSa
are indexed by 1, 2, . . . , kmax. Similar to the throughput analysis without reporting errors, we
consider all possible sets {nj} = {n1, n2, . . . , nkmax
} where nj is the number of SUs choosing channel j for access.
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Then, we can calculate the conditional throughput as follows:
T re
p (τ, {aj} , p) =
{nj1 }: j1∈ ˆSa nj1
=Nmax
P ({Nj1
, nj1
}) × (39)
j2∈ ˆSa
1
1
M
T re
j2
(τ, {aj2
} , p |n = nj2
) I (nj2
0) . (40)
Here P ({Nj1
, nj1
}) is the probability that each channel j1 (j1 ∈ ˆSa
) is selected by nj1
SUs for j1 = 1, 2, . . . , kmax.
This probability can be calculated as
P ({Nj1
, nj1
}) =
{Nj1
}
{nj1
}
i∈Ψa
1
ki
e
, (41)
where
{Nj1
}
{nj1
}
describes the number of ways to realize the access vector {nj} for kmax channels, which can
be obtained by using the enumeration technique as follows. For a particular way that the specific set of n1 SUs Sn1
1
choose channel one (there are Cn1
N1
such ways), we can express the set of remaining SUs that can choose channel
two as Ψa
(2) = Ψa
2(Sn1
1 ∩Ψa
2). We then consider all possible ways that n2 SUs in the set Ψa
(2) choose channel two
and we denote this set of SUs as Sn2
2 (there are Cn2
N2
such ways where N2 = |Ψa
(2)|). Similarly, we can express the
set of SUs that can choose channel three as Ψa
(3) = Ψa
3((∪2
i=1Sni
i ) ∩ Ψa
3) and consider all possible ways that n3
SUs in the set Ψa
(3) can choose channel three, and so on. This process is continued until nkmax
SUs choose channel
kmax. Therefore, the number of ways to realize the access vector {nj} can be determined by counting all possible
cases in the enumeration process.
The product term in (41) is due to the fact that each SU i chooses one available with probability 1/ki
e. The
conditional throughput T re
j2
(τ, {aj2
} , p |n = nj2
) is calculated by using the same expression (12) given in Section
III. In addition, only actually available channel j2 ∈ ˆSa
1 can contribute the total throughput, which explains the
throughput sum in (40).
C. Design Optimization with Reporting Errors
The optimization of channel sensing/access parameters as well as channel sensing sets can be conducted in the
same manner with that in Section III. However, we have to utilize the new throughput analytical model presented
in Section IV-B in this case. Specifically, Algs. 1 and 2 can still be used to determine the optimized sensing/access
parameters and channel sensing sets, respectively. Nonetheless, we need to use the new channel sensing model
capturing reporting errors in Section IV-A in these algorithms. In particular, from the equality constraint on
the detection probability, i.e., Pj
d εj
, τj
, aj = Pj
d, we have to use (29) and (30) to determine Pij
d (and the
corresponding Pij
f ) assuming that Pij
d are all the same for all pairs {i, j} as what we have done in Section III.
V. NUMERICAL RESULTS
To obtain numerical results in this section, the key parameters for the proposed MAC protocol are chosen as
follows: cycle time is T = 100ms; the slot size is v = 20µs, which is the same as in IEEE 802.11p standard;
packet size is PS = 450 slots (i.e., 450v); propagation delay PD = 1µs; SIFS = 2 slots; DIFS = 10 slots;
ACK = 20 slots; CTS = 20 slots; RTS = 20 slots; sampling frequency for spectrum sensing is fs = 6MHz;
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25. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 21
TABLE II
THROUGHPUT VS PROBABILITY OF VACANT CHANNEL (MXN=4X4)
Pj (H0)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Greedy 0.0816 0.1524 0.2316 0.2982 0.3612 0.4142 0.4662 0.5058 0.5461 0.5742
NT Optimal 0.0817 0.1589 0.2321 0.3007 0.3613 0.4183 0.4681 0.5087 0.5488 0.5796
Gap (%) 0.12 4.09 0.22 0.83 0.03 0.98 0.40 0.57 0.49 0.93
and tr = 80µs. The results presented in all figures except Fig. 11 correspond to the case where there is no reporting
error.
To investigate the efficacy of our proposed low-complexity channel assignment algorithm (Alg. 2), we compare
the throughput performance achieved by the optimal brute-force search and greedy channel assignment algorithm in
Table II. In particular, we show normalized throughput NT versus probabilities Pj (H0) for these two algorithms
and the relative gap between them. Here, the probabilities Pj (H0) for different channels j are chosen to be the same
and we choose M = 4 channels and N = 4 SUs. To describe the SNR of different SUs and channels, we use {i, j}
to denote a combination of channel j and SU i who senses this channel. The SNR setting for different combinations
of SUs and channels {i, j} is performed for two groups of SUs as γij
1 = −15dB: channel 1: {1, 1} , {2, 1} , {3, 1};
channel 2: {2, 2} , {4, 2}; channel 3: {1, 3} , {4, 3}; and channel 4: {1, 4} , {3, 4}. The remaining combinations
correspond to the SNR value γij
2 = −20dB for group two. The results in this table confirms that the throughput
gaps between our greedy algorithm and the brute-force optimal search algorithm are quite small, which are less that
1% for all except the case two presented in this table. These results confirm that our proposed greedy algorithm
works well for small systems (i.e., small M and N). In the following, we investigate the performance of our proposed
algorithms for larger systems.
To investigate the performance of our proposed algorithm for a typical system, we consider the network setting
with N = 10 and M = 4. We divide SUs into 2 groups where the received SNRs at SUs due to the transmission
from PU i is equal to γij
1,0 = −15dB and γij
2,0 = −10dB (or their shifted values described later) for the
two groups, respectively. Again, to describe the SNR of different SUs and channels, we use {i, j} to denote a
combination of channel j and SU i who senses this channel. The combinations of the first group corresponding to
γij
1,0 = −10dB are chosen as follows: channel 1: {1, 1} , {2, 1} , {3, 1}; channel 2: {2, 2} , {4, 2} , {5, 2}; channel
3: {4, 3} , {6, 3} , {7, 3}; and channel 4: {1, 4} , {3, 4} , {6, 4} , {8, 4} , {9, 4} , {10, 4}. The remaining combinations
belong to the second group with the SNR equal to γij
2,0 = −15dB. To obtain results for different values of SNRs, we
consider different shifted sets of SNRs where γij
1 and γij
2 are shifted by ∆γ around their initial values γij
1,0 = −15dB
and γij
2,0 = −10dB as γij
1 = γij
1,0 + ∆γ and γij
2 = γij
2,0 + ∆γ. For example, as ∆γ = −10, the resulting SNR
values are γij
1 = −25dB and γij
2 = −20dB. These parameter settings are used to obtain the results presented in
Figs. 5, 6, 7, 8, and 9 in the following.
Fig. 5 illustrates the convergence of Alg. 2 where we show the normalized throughput NTp versus the iterations
for ∆γ = −2, −5, −8 and −11dB. For simplicity, we choose δ equals 10−3
×NTc in Alg. 2. This figure confirms
that Alg. 2 converges after about 11, 13, 15 and 16 iterations for ∆γ = −2, −5, −8, and −11dB, respectively. In
addition, the normalized throughput increases over the iterations as expected.
Fig. 6 presents normalized throughput NT p versus transmission probability p and sensing time τ11
for the
SNR shift equal to ∆γ = −7 where the sensing times for other pairs of SUs and channels are optimized as in
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26. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 22
2 4 6 8 10 12 14 16
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Iterations
Throughput(NT)
∆γ = −2
∆γ = −5
∆γ = −8
∆γ = −11
Fig. 5. Convergence illustration for Alg. 2.
10
−5 10
−4 10
−3 10
−2 10
−110
−2
10
−1
0
0.2
0.4
0.6
0.8
Sensing time (τ11
)
Trans. prob. (p)
Throughput(NT)
0.2
0.3
0.4
0.5
0.6
0.7
N T opt(0.0054, 0.1026) = 0.7104
Fig. 6. Normalized throughput versus transmission probability p and sensing time τ11 for ∆γ = −7, N = 10 and M = 4.
−15 −14 −13 −12 −11 −10 −9 −8 −7 −6 −5
0.1
0.2
0.3
0.4
0.5
0.6
0.7
SNR (∆γ)
Throughput(NT)
a−out−of−b rule
Major rule
OR rule
AND rule
Fig. 7. Normalized throughput versus SNR shift ∆γ for N = 10 and M = 4 under 4 aggregation rules.
Alg. 1. This figure shows that channel sensing and access parameters can strongly impact the throughput of the
secondary network, which indicates the need to optimize them. This figure shows that the optimal values of p and
τ11
are around ¯τ11
, ¯p = (0.0054s, 0.1026) to achieve the maximum normalized throughput of NT p = 0.7104.
It can be observed that normalized throughput NT p is less sensitive to transmission probability p while it varies
more significantly as the sensing time τ11
deviates from the optimal value. In fact, there can be multiple available
channels which each SU can choose from. Therefore, the contention level on each available channel would not be
very intense for most values of p. This explains why the throughput is not very sensitive to the access parameter p.
In Fig. 7, we compare the normalized throughput of the secondary network as each SU employs four different
aggregation rules, namely AND, OR, majority, and the optimal a-out-of-b rules. The four throughput curves in this
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27. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 23
−15 −10 −5 0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
SNR (∆γ)
Throughput(NT)
a−out−of−b rule −OPT
1% T − Non−OPT
2% T − Non−OPT
5% T − Non−OPT
10% T − Non−OPT
Fig. 8. Normalized throughput versus SNR shift ∆γ for N = 10 and M = 4 for optimized and non-optimized scenarios.
−15 −10 −5 0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
SNR (∆γ)
Throughput(T)
a−out−of−b rule − OPT
Case 1
Case 2
Case 3
Fig. 9. Normalized throughput versus SNR shift ∆γ for N = 10 and M = 4 for optimized and RR channel assignments.
figure represent the optimized normalized throughput values achieved by using Algs. 1 and 2. For the OR, AND,
majority rules, we do not need to find optimized aj parameters for different channels j in Alg. 1. Alternatively,
aj = 1, aj = bj and aj = ⌈b/2⌉ correspond to the OR, AND and majority rules, respectively. It can be seen that
the optimal a-out-of-b rule achieves the highest throughput among the considered rules. Moreover, the performance
gaps between the optimal a-out-of-b rule and other rule tends to be larger for smaller SNR values.
In Fig. 8, we compare the throughput performance as the sensing times are optimized by using Alg. 1 and they
are fixed at different fractions of the cycle time in Alg. 1. For fair comparison, the optimized a-out-of-b rules are
used in both schemes with optimized and non-optimized sensing times. For the non-optimized scheme, we employ
Alg. 2 for channel assignment; however, we do not optimize the sensing times in Alg. 1. Alternatively, τij
is chosen
from the following values: 1%T, 2%T, 5%T and 10%T where T is the cycle time. Furthermore, for this non-
optimized scheme, we still find an optimized value of ¯aj for each channel j (corresponding to the sensing phase)
and the optimal value of ¯p (corresponding to the access phase) in Alg. 1. This figure confirms that the optimized
design achieves the largest throughput. Also, small sensing times can achieve good throughput performance at the
high-SNR regime but result in poor performance if the SNR values are low. In contrast, too large sensing times (e.g.,
equal 10%T) may become inefficient if the SNR values are sufficiently large. These observations again illustrate
the importance of optimizing the channel sensing and access parameters.
We compare the normalized throughput under our optimized design and the round-robin (RR) channel assignment
strategies in Fig. 9. For RR channel assignment schemes, we first allocate channels for SUs as described in Table III
(i.e., we consider three different RR channel assignments). In the considered round-robin channel assignment
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28. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 24
TABLE III
ROUND-ROBIN CHANNEL ASSIGNMENT (X DENOTES AN ASSIGNMENT)
Channel
Case 1 Case 2 Case 3
1 2 3 4 1 2 3 4 1 2 3 4
1 x x x x x x
2 x x x x x x
3 x x x x x
4 x x x
SU 5 x x x x x x
6 x x x x x x
7 x x x x x
8 x x x
9 x x x x x x
10 x x x x x x
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Pj(H0)
Throughput(NT)
∆ γ = −4
∆ γ = −6
∆ γ = −8
∆ γ = −10
∆ γ = −12
Fig. 10. Normalized throughput versus probability of having vacant channel Pj (H0) for N = 10 and M = 4 for optimized channel
assignments and a-out-of-b aggregation rule.
schemes, we assign at most 1, 2 and 3 channels for each SU corresponding to cases 1, 2 and 3 as shown in
Table III. In particular, we sequentially assign channels with increasing indices for the next SUs until exhausting
(we then repeat this procedure for the following SU). Then, we only employ Alg. 1 to optimize the sensing and
access parameters for these RR channel assignments. Fig. 9 shows that the optimized design achieves much higher
throughput than those due to RR channel assignments. These results confirm that channel assignments for cognitive
radios play a very important role in maximizing the spectrum utilization for CRNs. In particular, if it would be
sufficient to achieve good sensing and throughput performance if we assign a small number of nearby SUs to
sense any particular channel instead of requiring all SUs to sense the channel. This is because “bad SUs” may not
contribute to improve the sensing performance but result in more sensing overhead, which ultimately decreases the
throughput of the secondary network.
In Fig. 10, we consider the impact of PUs’ activities on throughput performance of the secondary network. In
particular, we vary the probabilities of having idle channels for secondary spectrum access (Pj (H0)) in the range
of [0.1, 1]. For larger values of Pj (H0), there are more opportunities for SUs to find spectrum holes to transmit
data, which results in higher throughput and vice versa. Moreover, this figure shows that the normalized throughput
increases almost linearly with Pj (H0). Also as the ∆γ increases (i.e., higher SNR), the throughput performance
can be improved significantly. However, the improvement becomes negligible if the SNR values are sufficiently
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29. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 25
−15 −10 −5 0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
SNR (∆γ)
Throughput(T)
Pe = 0%
Pe = 1%
Pe = 5%
Fig. 11. Normalized throughput versus SNR shift ∆γ for N = 4 and M = 3 for optimized channel assignments and a-out-of-b aggregation
rules.
large (for ∆γ in [−6, −4]). This is because for large SNR values, the required sensing time is sufficiently small,
therefore, further increase of SNR does not reduce the sensing time much to improve the normalized throughput.
Finally, we study the impact of reporting errors on the throughput performance by using the extended throughput
analytical model in Section IV. The network setting under investigation has N = 4 SUs and M = 3 channels. Again,
we use notation {i, j} to represent a combination of channel j and SU i. The combinations with γij
10 = −10dB
are chosen as follows: channel 1: {1, 1} , {2, 1} , {3, 1}; channel 2: {2, 2} , {4, 2}; channel 3: {1, 3} , {4, 3}. The
remaining combinations correspond to γij
20 = −15dB. We assume that the reporting errors between every pair of
2 SUs are the same, which is denoted as Pe. In Fig. 11, we show the achieved throughput as Pe = 0%, Pe = 1%
and Pe = 5% under optimized design. We can see that when Pe increases, the normalized throughput decreases
quite significantly if the SNR is sufficiently low. However, in the high-SNR regime, the throughput performance is
less sensitive to the reporting errors.
VI. CONCLUSION
We have proposed a general analytical and optimization framework for SDCSS and access design in multi-
channel CRNs. In particular, we have proposed the p-persistent CSMA MAC protocol integrating the SDCSS
mechanism. Then, we have analyzed the throughput performance of the proposed design and have developed
an efficient algorithm to optimize its sensing and access parameters. Moreover, we have presented both optimal
brute-force search and low-complexity algorithms to determine efficient channel sensing sets and have analyzed
their complexity. We have also extended the framework to consider reporting errors in exchanging sensing results
among SUs. Finally, we have evaluated the impacts of different parameters on the throughput performance of the
proposed design and illustrated the significant performance gap between the optimized and non-optimized designs.
Specifically, it has been confirmed that optimized sensing and access parameters as well as channel assignments
can achieve considerably better throughput performance than that due to the non-optimized design. In the future,
we will extend SDCSS and MAC protocol design for the multihop CRNs.
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TABLE IV
SUMMARY OF KEY VARIABLES
Variable Description
Key variables for no-reporting-error scenario
Pj (H0) (Pj (H1)) probability that channel j is available (or not available)
Pij
d (Pij
f ) probability of detection (false alarm) experienced by SU i for channel j
Pj
d (Pj
f ) probability of detection (false alarm) for channel j under SDCSS
εij
, γij
detection threshold, signal-to-noise ratio of the PU’s signal
τij
, τ sensing time at SU i on channel j, total sensing time
N0, fs noise power, sampling frequency
aj , bj parameters of a-out-of-b rule for channel j
N, M total number of SUs, total number of channels
SU
j , SU
set of SUs that sense channel j, set of all N SUs
Si, S set of assigned channels for SU i, set of all M channels
Φk
l particular set k of l SUs
Ψ
l0
k0
set l0 of k0 actually available channels
Θ
l1
k1
, Ω
l2
k2
set l1 of k1 available channels (which are indicated by sensing outcomes),
set l2 of k2 misdetected channels (which are indicated by sensing outcomes)
N T normalized throughput per one channel
T ne
p , T ne
j2
conditional throughput: for one particular realization of sensing outcomes corresponding to 2 sets Θ
l1
k1
and Ω
l2
k2
,
for a particular channel j2
nj , ke number of SUs who select channel j to access, ke =| Θ
l1
k1
Ω
l2
k2
|
T , TR cycle time, total reporting time
TS , T S time for transmission of packet, time for successful RTS/CTS transmission
T i,j
I (T
j
I ) i-th duration between 2 consecutive RTS/CTS transmission on channel j (its average value)
TC , T
j
cont duration of collision, average contention time on channel j
P D propagation delay
P S, ACK lengths of packet and acknowledgment, respectively
SIF S, DIF S lengths of short time interframe space and distributed interframe space, respectively
RT S, CT S lengths of request-to-send and clear-to-send, respectively
p, Pj
C transmission probability, probability of a generic slot corresponding to collision
Pj
S , Pj
I probabilities of a generic slot corresponding to successful transmission, idle slot
Nj
c (N
j
c) number of collisions before the first successful RTS/CTS exchange (its average value)
fNc
X , fI
X pmfs of Nj
c , T i,j
I
Key variables as considering reporting errors
Pi1i2
e probability of reporting errors between SUs i1 and i2
P
i1i2j
d (P
i1i2j
f ) probabilities of detection (false alarm) experienced by SU i1 on channel j with the sensing result received from SU i2
Θ
l1
k1,j3
l1-th set of k1 SUs whose sensing outcomes indicate that channel j3 is vacant
Ω
l2
k2,j4
l2-th set of k2 SUs whose sensing outcomes indicate that channel j4 is vacant due to misdetection
Φ
l3
k3,j3
l3-th set of k3 SUs in Θ
l1
k1,j3
who correctly report their sensing information on channel j3 to SU i4
Λ
l4
k4,j3
l4-th set of k4 SUs in SU
j3
Θ
l1
k1,j3
who incorrectly report their sensing information on channel j3 to SU i4
Ξ
l5
k5,j4
l5-th set of k5 SUs in Ω
l2
k2,j4
who correctly report their sensing information on channel j4 to SU i9
Γ
l6
k6,j4
l6-th set of k6 SUs in SU
j4
Ω
l2
k2,j4
who incorrectly report their sensing information on channel j4 to SU i9
Sa
1,i, Sa
2,i sets of actually available channels and available due to sensing and/or reporting errors, respectively
ˆSa
1 , ˆSa
2
ˆSa
1 = i∈SU Sa
1,i, ˆSa
2 = i∈SU Sa
2,i
Sa
i , ˆSa
Sa
i = Sa
1,i Sa
2,i, ˆSa
= ˆSa
1
ˆSa
2
ki
e, kmax ki
e =| Sa
i |, kmax =| ˆSa
|
Ψa
j , Ψa
set of SUs whose SDCSS outcomes indicate that channel j is available, Ψa
= j∈ ˆSa Ψa
j
Nj , Nmax Nj =| Ψa
j |, Nmax =| Ψa
|
T re
p , T re
j2
conditional throughput for one particular realization of sensing outcomes and for a particular channel j2, respectively
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