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.
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.
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.
Channel assignment for throughput maximization in cognitive radio networks Polytechnique Montreal
In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users exploit spectrum holes on a set of channels where each secondary user can use at most one available channel for communication. We develop two channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, secondary users 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 to the non-overlapping channel assignment algorithm. In addition, we design a distributed MAC protocol for access contention resolution and integrate the derived MAC protocol overhead into the second channel assignment algorithm. Finally, numerical results are presented to validate the theoretical results and illustrate the performance gain due to the overlapping channel assignment algorithm.
Iaetsd a novel scheduling algorithms for mimo based wireless networksIaetsd Iaetsd
This document proposes new scheduling algorithms for MIMO wireless networks to improve system performance. It discusses designing practical user scheduling algorithms to maximize capacity in MIMO systems. Various MAC scheduling policies are implemented and modified to provide distributed traffic control, robustness against interference, and increased efficiency of resource utilization. Simulations using MATLAB compare the different policies and draw important results and conclusions. The paper suggests new priority scheduling and partially fair scheduling algorithms incorporating awareness of interference to improve system-level performance in MIMO wireless networks.
Localized Algorithm for Channel Assignment in Cognitive Radio NetworksIJERA Editor
Cognitive Radio has been emerged as a revolutionary solution to migrate the current shortage of spectrum
allocation in wireless networks. In this paper, an improved localized channel allocation algorithm based on
channel weight is proposed. A factor of channel stability is introduced based on link environment, which
efficiently assigns the best channels to the links. Based on the framework, a conflict resolution strategy is used to
make the scheme adaptable to different network conditions. Calculations indicate that this algorithm can reduce
the conflicts, increase the delivery rate and link assignment rate compared with the basic channel assignment
algorithm.
In this paper, we consider the joint optimal sensing
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
primary users. We perform saturation throughput analysis for
the proposed MAC protocol that explicitly captures spectrum
sensing performance. Then, we find its optimal configuration
by formulating a throughput maximization problem subject to
detection probability constraints for primary users. In particular,
the optimal solution of this optimization problem returns the
required sensing time for primary users’ protection and optimal
contention window for maximizing total throughput of the
secondary network. Finally, numerical results are presented to
illustrate a significant performance gain of the optimal sensing
and protocol configuration.
Multiuser MIMO Gaussian Channels: Capacity Region and DualityShristi Pradhan
In this paper, I present the MIMO channel for single user case, discuss the decomposition of MIMO into parallel independent channels, and estimate the MIMO channel capacity. Then, I discuss on computation of capacity region for multiuser MIMO broadcast and multiple access channel and plot capacity regions for two users case. I conclude by showing the duality relationship between the multiple access and broadcast channel and show its significance for numerical standpoint.
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.
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.
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.
Channel assignment for throughput maximization in cognitive radio networks Polytechnique Montreal
In this paper, we consider the channel allocation problem for throughput maximization in cognitive radio networks with hardware-constrained secondary users. Specifically, we assume that secondary users exploit spectrum holes on a set of channels where each secondary user can use at most one available channel for communication. We develop two channel assignment algorithms that can efficiently utilize spectrum opportunities on these channels. In the first algorithm, secondary users 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 to the non-overlapping channel assignment algorithm. In addition, we design a distributed MAC protocol for access contention resolution and integrate the derived MAC protocol overhead into the second channel assignment algorithm. Finally, numerical results are presented to validate the theoretical results and illustrate the performance gain due to the overlapping channel assignment algorithm.
Iaetsd a novel scheduling algorithms for mimo based wireless networksIaetsd Iaetsd
This document proposes new scheduling algorithms for MIMO wireless networks to improve system performance. It discusses designing practical user scheduling algorithms to maximize capacity in MIMO systems. Various MAC scheduling policies are implemented and modified to provide distributed traffic control, robustness against interference, and increased efficiency of resource utilization. Simulations using MATLAB compare the different policies and draw important results and conclusions. The paper suggests new priority scheduling and partially fair scheduling algorithms incorporating awareness of interference to improve system-level performance in MIMO wireless networks.
Localized Algorithm for Channel Assignment in Cognitive Radio NetworksIJERA Editor
Cognitive Radio has been emerged as a revolutionary solution to migrate the current shortage of spectrum
allocation in wireless networks. In this paper, an improved localized channel allocation algorithm based on
channel weight is proposed. A factor of channel stability is introduced based on link environment, which
efficiently assigns the best channels to the links. Based on the framework, a conflict resolution strategy is used to
make the scheme adaptable to different network conditions. Calculations indicate that this algorithm can reduce
the conflicts, increase the delivery rate and link assignment rate compared with the basic channel assignment
algorithm.
In this paper, we consider the joint optimal sensing
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
primary users. We perform saturation throughput analysis for
the proposed MAC protocol that explicitly captures spectrum
sensing performance. Then, we find its optimal configuration
by formulating a throughput maximization problem subject to
detection probability constraints for primary users. In particular,
the optimal solution of this optimization problem returns the
required sensing time for primary users’ protection and optimal
contention window for maximizing total throughput of the
secondary network. Finally, numerical results are presented to
illustrate a significant performance gain of the optimal sensing
and protocol configuration.
Multiuser MIMO Gaussian Channels: Capacity Region and DualityShristi Pradhan
In this paper, I present the MIMO channel for single user case, discuss the decomposition of MIMO into parallel independent channels, and estimate the MIMO channel capacity. Then, I discuss on computation of capacity region for multiuser MIMO broadcast and multiple access channel and plot capacity regions for two users case. I conclude by showing the duality relationship between the multiple access and broadcast channel and show its significance for numerical standpoint.
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.
Performance Analysis of Ultra Wideband Receivers for High Data Rate Wireless ...graphhoc
The document discusses performance comparisons of different receiver structures for high data rate ultra wideband communication systems. It analyzes Rake, MMSE, and Rake-MMSE receivers using MATLAB simulations on IEEE 802.15.3a channel models. The Rake-MMSE receiver combines advantages of Rake fingers and equalization to combat inter-symbol interference. Simulation results show the Rake-MMSE receiver achieves a lower bit error rate than Rake or MMSE receivers alone. The number of Rake fingers improves performance at low-medium SNR, while more equalizer taps help at high SNR.
An improved dft based channel estimationsakru naik
This document proposes an improved DFT-based channel estimation method for MIMO-OFDM systems. The conventional DFT method causes energy leakage in non-sample-spaced multipath channels. The improved method extends the LS estimate using symmetry, calculates the changing rate of leakage energy, and selects useful paths based on this rate to reduce leakage energy. Simulation results show the improved method reduces leakage energy more efficiently and provides better channel estimation performance than LS and conventional DFT algorithms.
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.
OPTIMAL POWER ALLOCATION FOR MULTIPLE ACCESS CHANNELijwmn
This paper considers the problem of power allocation between the two senders in the multiple access channel. Two power allocation criteria are developed. In particular, in the first criterion, the total available power is allocated between the two users such that the two users have the same achievable rate.
In addition, the second criterion allocates the total available power such that the sum rate is maximized. In addition, many numerical examples are shown to show the value of power allocation and also to compare between the proposed criteria
The document proposes a distributed adaptive opportunistic routing scheme for wireless ad hoc networks. It uses a reinforcement learning framework to route packets opportunistically even without knowledge of channel statistics or network models. This approach jointly addresses learning and routing opportunistically by exploiting transmission successes. Nodes learn to optimally explore and exploit opportunities in the network to minimize the expected average per packet cost of routing from source to destination.
New strategy to optimize the performance of spray and wait routing protocolijwmn
Delay Tolerant Networks have been (DTN) have been developed to support the irregular connectivity often
separate networks. The main routing problem in this type of network is embarrassed by time that is
extremely long, since connections are intermittent and opportunistic. Routing protocols must take into
account the maximum constraint encountered in this type of environment , use effective strategies
regarding the choice of relay nodes and buffer management nodes to improve the delivery of messages and
the time of their delivery . This article proposes a new strategy that optimizes the routing Spray and wait.
The proposed method uses the information contained in the messages delivered mostly paths traversed by
the messages before arriving at their destination and the time when nodes have receive these messages.
Simulation results show that the proposed strategy can increase the probability of delivery and minimizing
overhead unlike FIFO technology used with the default routing ' sprat and wait'
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
This document summarizes a research paper that proposes a new routing metric called NMH (New Metric for Hybrid Wireless Mesh Protocol) for wireless mesh networks. The paper argues that existing routing metrics do not adequately consider factors like channel diversity, interference, and end-to-end delay. The proposed NMH metric combines two-hop channel diversity and hop delay. Simulation results showed that NMH outperformed WCETT (Weighted Cumulative Expected Transmission Time) in terms of average network throughput, end-to-end delay, and number of flows supported.
This document summarizes a research paper on a multi-user MIMO cognitive radio system that allows for simultaneous spectrum sensing and data transmission. The secondary receiver performs MMSE detection to decode signals from multiple secondary transmitters while also sensing the spectrum to detect potential primary activity. The analysis presents novel expressions for important metrics like detection probability, false alarm probability, and secondary transmission power under assumptions of Rayleigh fading, time-varying channels, and channel estimation errors. Numerical results verify the accuracy of the analysis.
A QUANTITATIVE ANALYSIS OF HANDOVER TIME AT MAC LAYER FOR WIRELESS MOBILE NET...ijwmn
Extensive studies have been carried out for reducing the handover time of wireless mobile network at
medium access control (MAC) layer. However, none of them show the impact of reduced handover time
on the overall performance of wireless mobile networks. This paper presents a quantitative analysis to
show the impact of reduced handover time on the performance of wireless mobile networks. The proposed
quantitative model incorporates many critical performance parameters involve in reducing the handover
time for wireless mobile networks. In addition, we analyze the use of active scanning technique with
comparatively shorter beacon interval time in a handoff process. Our experiments verify that the active
scanning can reduce the overall handover time at MAC layer if comparatively shorter beacon intervals are
utilized for packet transmission. The performance measures adopted in this paper for experimental
verifications are network throughput under different network loads.
This paper proposes and evaluates three algorithms for determining the channel quality of multicast sessions in cellular networks:
1) Algorithm I takes the best channel condition among users as the session's quality. This favors multicast but reduces overall throughput.
2) Algorithm II takes the worst condition, disfavoring multicast.
3) The proposed algorithm takes the average effective throughput per user, balancing multicast and unicast fairness.
The paper simulates these algorithms under varying conditions to evaluate their throughput and fairness between multicast and unicast sessions. The proposed algorithm achieves significantly higher throughput while ensuring fair resource allocation.
A PROPOSAL FOR IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKIJCNCJournal
The document proposes a new routing protocol for wireless sensor networks that aims to improve network lifetime. The protocol is based on LEACH, an existing energy-efficient clustering protocol, but improves on it by electing cluster heads based on both remaining node energy and distance to the base station. Simulation results show the proposed protocol extends network lifetime by up to 75% compared to LEACH alone by distributing energy usage more evenly across nodes.
Clustering based Time Slot Assignment Protocol for Improving Performance in U...journal ijrtem
Recently, numerous approaches have been proposed for designing medium access control (MAC)
in underwater acoustic networks (UANs). Some of those works tried to adapt MAC protocols proposed for
terrestrial networks. However, unique environmental characteristics of UANs make the MAC protocols hard to be
used in the UANs and degrade network performance. In order to improve network performance, COD-TS MAC
protocol was proposed. COD-TS focuses on both single hop and multi-hop mode and utilizes CDMA for
exchanging schedule information between cluster heads. COD-TS has shortcomings such as collisions, additional
energy consumption by exchanging schedule information and near-far effect of CDMA. To overcome above
shortcomings, we propose a clustering-based time slot assignment protocol. In the proposed protocol, nodes are
clustered, and each cluster head performs two-hop neighbor cluster discovery operation. And then, a cluster head
obtains its own relative position information. Finally, the cluster head assigns its own time slot for data
transmission based on the information. Simulation results show that the proposed protocol has always better
performance compared to the COD-TS.
The document discusses query optimization techniques for sensor networks. It describes the basic architecture of querying in TinyDB where queries are sent to and processed by the sensor network. It notes disadvantages like hotspots and lack of in-network aggregation. The goal is to design a scheme to support multiple queries minimizing communication cost through query co-relation and transformations. An example flood warning query is provided. Queries are classified and optimization techniques like sync-joins and predicate push-down are discussed.
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
The document compares resource allocation algorithms for OFDMA wireless systems. It discusses dynamic sub-channel assignment and adaptive power allocation algorithms. The algorithms are evaluated based on parameters like Jain's Fairness Index, sum capacity, and capacity distribution among users. Resource allocation algorithms aim to optimize margin and rate by assigning subcarriers and power levels to users in an OFDMA system.
Packet Loss and Overlay Size Aware Broadcast in the Kademlia P2P SystemIDES Editor
Kademlia is a structured peer-to-peer (P2P)
application level network, which implements a distributed
hash table (DHT). Its key-value storage and lookup service is
made efficient and reliable by its well-designed binary tree
topology and dense mesh of connections between participant
nodes. While it can carry out data storage and retrieval in
logarithmic time if the key assigned to the value in question
is precisely known, no complex queries of any kind are
supported. In this article a broadcast algorithm for the
Kademlia network is presented, which can be used to
implement such queries. The replication scheme utilized is
compatible with the lookup algorithm of Kademlia, and it
uses the same routing tables. The reliability (coverage) of the
algorithm is increased by assigning the responsibility of
disseminating the broadcast message to many nodes at the
same time. The article presents a model validated with
simulation as well. The model can be used by nodes at runtime
to calculate the required level of replication for any desired
level of coverage. This calculation can take node churn, packet
loss ratio and the size of the overlay into account.
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.
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel ...Polytechnique Montreal
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.
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.
Performance Analysis of Ultra Wideband Receivers for High Data Rate Wireless ...graphhoc
The document discusses performance comparisons of different receiver structures for high data rate ultra wideband communication systems. It analyzes Rake, MMSE, and Rake-MMSE receivers using MATLAB simulations on IEEE 802.15.3a channel models. The Rake-MMSE receiver combines advantages of Rake fingers and equalization to combat inter-symbol interference. Simulation results show the Rake-MMSE receiver achieves a lower bit error rate than Rake or MMSE receivers alone. The number of Rake fingers improves performance at low-medium SNR, while more equalizer taps help at high SNR.
An improved dft based channel estimationsakru naik
This document proposes an improved DFT-based channel estimation method for MIMO-OFDM systems. The conventional DFT method causes energy leakage in non-sample-spaced multipath channels. The improved method extends the LS estimate using symmetry, calculates the changing rate of leakage energy, and selects useful paths based on this rate to reduce leakage energy. Simulation results show the improved method reduces leakage energy more efficiently and provides better channel estimation performance than LS and conventional DFT algorithms.
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.
OPTIMAL POWER ALLOCATION FOR MULTIPLE ACCESS CHANNELijwmn
This paper considers the problem of power allocation between the two senders in the multiple access channel. Two power allocation criteria are developed. In particular, in the first criterion, the total available power is allocated between the two users such that the two users have the same achievable rate.
In addition, the second criterion allocates the total available power such that the sum rate is maximized. In addition, many numerical examples are shown to show the value of power allocation and also to compare between the proposed criteria
The document proposes a distributed adaptive opportunistic routing scheme for wireless ad hoc networks. It uses a reinforcement learning framework to route packets opportunistically even without knowledge of channel statistics or network models. This approach jointly addresses learning and routing opportunistically by exploiting transmission successes. Nodes learn to optimally explore and exploit opportunities in the network to minimize the expected average per packet cost of routing from source to destination.
New strategy to optimize the performance of spray and wait routing protocolijwmn
Delay Tolerant Networks have been (DTN) have been developed to support the irregular connectivity often
separate networks. The main routing problem in this type of network is embarrassed by time that is
extremely long, since connections are intermittent and opportunistic. Routing protocols must take into
account the maximum constraint encountered in this type of environment , use effective strategies
regarding the choice of relay nodes and buffer management nodes to improve the delivery of messages and
the time of their delivery . This article proposes a new strategy that optimizes the routing Spray and wait.
The proposed method uses the information contained in the messages delivered mostly paths traversed by
the messages before arriving at their destination and the time when nodes have receive these messages.
Simulation results show that the proposed strategy can increase the probability of delivery and minimizing
overhead unlike FIFO technology used with the default routing ' sprat and wait'
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
This document summarizes a research paper that proposes a new routing metric called NMH (New Metric for Hybrid Wireless Mesh Protocol) for wireless mesh networks. The paper argues that existing routing metrics do not adequately consider factors like channel diversity, interference, and end-to-end delay. The proposed NMH metric combines two-hop channel diversity and hop delay. Simulation results showed that NMH outperformed WCETT (Weighted Cumulative Expected Transmission Time) in terms of average network throughput, end-to-end delay, and number of flows supported.
This document summarizes a research paper on a multi-user MIMO cognitive radio system that allows for simultaneous spectrum sensing and data transmission. The secondary receiver performs MMSE detection to decode signals from multiple secondary transmitters while also sensing the spectrum to detect potential primary activity. The analysis presents novel expressions for important metrics like detection probability, false alarm probability, and secondary transmission power under assumptions of Rayleigh fading, time-varying channels, and channel estimation errors. Numerical results verify the accuracy of the analysis.
A QUANTITATIVE ANALYSIS OF HANDOVER TIME AT MAC LAYER FOR WIRELESS MOBILE NET...ijwmn
Extensive studies have been carried out for reducing the handover time of wireless mobile network at
medium access control (MAC) layer. However, none of them show the impact of reduced handover time
on the overall performance of wireless mobile networks. This paper presents a quantitative analysis to
show the impact of reduced handover time on the performance of wireless mobile networks. The proposed
quantitative model incorporates many critical performance parameters involve in reducing the handover
time for wireless mobile networks. In addition, we analyze the use of active scanning technique with
comparatively shorter beacon interval time in a handoff process. Our experiments verify that the active
scanning can reduce the overall handover time at MAC layer if comparatively shorter beacon intervals are
utilized for packet transmission. The performance measures adopted in this paper for experimental
verifications are network throughput under different network loads.
This paper proposes and evaluates three algorithms for determining the channel quality of multicast sessions in cellular networks:
1) Algorithm I takes the best channel condition among users as the session's quality. This favors multicast but reduces overall throughput.
2) Algorithm II takes the worst condition, disfavoring multicast.
3) The proposed algorithm takes the average effective throughput per user, balancing multicast and unicast fairness.
The paper simulates these algorithms under varying conditions to evaluate their throughput and fairness between multicast and unicast sessions. The proposed algorithm achieves significantly higher throughput while ensuring fair resource allocation.
A PROPOSAL FOR IMPROVE THE LIFETIME OF WIRELESS SENSOR NETWORKIJCNCJournal
The document proposes a new routing protocol for wireless sensor networks that aims to improve network lifetime. The protocol is based on LEACH, an existing energy-efficient clustering protocol, but improves on it by electing cluster heads based on both remaining node energy and distance to the base station. Simulation results show the proposed protocol extends network lifetime by up to 75% compared to LEACH alone by distributing energy usage more evenly across nodes.
Clustering based Time Slot Assignment Protocol for Improving Performance in U...journal ijrtem
Recently, numerous approaches have been proposed for designing medium access control (MAC)
in underwater acoustic networks (UANs). Some of those works tried to adapt MAC protocols proposed for
terrestrial networks. However, unique environmental characteristics of UANs make the MAC protocols hard to be
used in the UANs and degrade network performance. In order to improve network performance, COD-TS MAC
protocol was proposed. COD-TS focuses on both single hop and multi-hop mode and utilizes CDMA for
exchanging schedule information between cluster heads. COD-TS has shortcomings such as collisions, additional
energy consumption by exchanging schedule information and near-far effect of CDMA. To overcome above
shortcomings, we propose a clustering-based time slot assignment protocol. In the proposed protocol, nodes are
clustered, and each cluster head performs two-hop neighbor cluster discovery operation. And then, a cluster head
obtains its own relative position information. Finally, the cluster head assigns its own time slot for data
transmission based on the information. Simulation results show that the proposed protocol has always better
performance compared to the COD-TS.
The document discusses query optimization techniques for sensor networks. It describes the basic architecture of querying in TinyDB where queries are sent to and processed by the sensor network. It notes disadvantages like hotspots and lack of in-network aggregation. The goal is to design a scheme to support multiple queries minimizing communication cost through query co-relation and transformations. An example flood warning query is provided. Queries are classified and optimization techniques like sync-joins and predicate push-down are discussed.
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
The document compares resource allocation algorithms for OFDMA wireless systems. It discusses dynamic sub-channel assignment and adaptive power allocation algorithms. The algorithms are evaluated based on parameters like Jain's Fairness Index, sum capacity, and capacity distribution among users. Resource allocation algorithms aim to optimize margin and rate by assigning subcarriers and power levels to users in an OFDMA system.
Packet Loss and Overlay Size Aware Broadcast in the Kademlia P2P SystemIDES Editor
Kademlia is a structured peer-to-peer (P2P)
application level network, which implements a distributed
hash table (DHT). Its key-value storage and lookup service is
made efficient and reliable by its well-designed binary tree
topology and dense mesh of connections between participant
nodes. While it can carry out data storage and retrieval in
logarithmic time if the key assigned to the value in question
is precisely known, no complex queries of any kind are
supported. In this article a broadcast algorithm for the
Kademlia network is presented, which can be used to
implement such queries. The replication scheme utilized is
compatible with the lookup algorithm of Kademlia, and it
uses the same routing tables. The reliability (coverage) of the
algorithm is increased by assigning the responsibility of
disseminating the broadcast message to many nodes at the
same time. The article presents a model validated with
simulation as well. The model can be used by nodes at runtime
to calculate the required level of replication for any desired
level of coverage. This calculation can take node churn, packet
loss ratio and the size of the overlay into account.
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.
Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel ...Polytechnique Montreal
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.
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.
Effective capacity in cognitive radio broadcast channelsMarwan Hammouda
Abstract—In this paper, we investigate effective capacity by
modeling a cognitive radio broadcast channel with one secondary transmitter (ST) and two secondary receivers (SRs) under quality-of-service constraints and interference power limitations.We initially describe three different ooperative channel sensing strategies with different ard-decision combining algorithms at the ST, namely OR, Majority, and AND rules. Since the channel sensing occurs with possible errors, we consider a combined
interference power constraint by which the transmission power of the secondary users (SUs) is bounded when the channel is sensed as both busy and idle. Furthermore, regarding the channel sensing decision and its correctness, there exist ...
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.
Stochastic analysis of random ad hoc networks with maximum entropy deploymentsijwmn
In this paper, we present the first stochastic analysis of the link performance of an ad hoc network modelled
by a single homogeneous Poisson point process (HPPP). According to the maximum entropy principle, the
single HPPP model is mathematically the best model for random deployments with a given node density.
However, previous works in the literature only consider a modified model which shows a discrepancy in the
interference distribution with the more suitable single HPPP model. The main contributions of this paper
are as follows. 1) It presents a new mathematical framework leading to closed form expressions of the
probability of success of both one-way transmissions and handshakes for a deployment modelled by a
single HPPP. Our approach, based on stochastic geometry, can be extended to complex protocols. 2) From
the obtained results, all confirmed by comparison to simulated data, optimal PHY and MAC layer
parameters are determined and the relations between them is described in details. 3) The influence of the
routing protocol on handshake performance is taken into account in a realistic manner, leading to the
confirmation of the intuitive result that the effect of imperfect feedback on the probability of success of a
handshake is only negligible for transmissions to the first neighbour node.
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.
Transferring quantum information through theijngnjournal
Transmission of information in the form of qubits much faster than the speed of light is the important
aspects of quantum information theory. Quantum information processing exploits the quantum nature of
information that needs to be stored, encoded, transmit, receive and decode the information in the form of
qubits. Bosonic channels appear to be very attractive for the physical implementation of quantum
communication. This paper does the study of quantum channels and how best it can be implemented with
the existing infrastructure that is the classical communication. Multiple access to the quantum network is
the requirement where multiple users want to transmit their quantum information simultaneously without
interfering with each others.
Transferring Quantum Information through the Quantum Channel using Synchronou...josephjonse
Transmission of information in the form of qubits much faster than the speed of light is the important aspects of quantum information theory. Quantum information processing exploits the quantum nature of information that needs to be stored, encoded, transmit, receive and decode the information in the form of qubits. Bosonic channels appear to be very attractive for the physical implementation of quantum communication. This paper does the study of quantum channels and how best it can be implemented with the existing infrastructure that is the classical communication. Multiple access to the quantum network is the requirement where multiple users want to transmit their quantum information simultaneously without interfering with each others.
A New Analysis for Wavelength Translation in Regular WDM NetworksVishal Sharma, Ph.D.
We present a new analysis of wavelength translation in
regular, all-optical WDM networks, that is simple, computationally
inexpensive, and accurate for both low and high
network loads. In a network with
k
wavelengths per link,
we model the output link by an auxiliary
M/M/k/k
queueing
system. We then obtain a closed-form expression for
the probability
P succ
that a session arriving at a node at a
random time successfully establishes a connection from its
source node to its destination node. Unlike previous analyses,
which use the link independence blocking assumption,
we account for the dependence between the acquisition of
wavelengths on successive links of the session’s path. Based
on the success probability, we show that the throughput per
wavelength increases superlinearly (as expected) as we increase
the number of wavelengths per link; however, the
extent of this superlinear increase in throughput saturates
rather quickly. This suggests some interesting possibilities
for network provisioning in an all-optical network. We verify the accuracy of our analysis via simulations for the torus
and hypercube networks.
Flexible channel allocation using best Secondary user detection algorithmijsrd.com
This document proposes a flexible channel allocation algorithm for cooperative cognitive radio networks using secondary user detection. It introduces Flexible Channel Cooperation (FLEC) which allows secondary users to optimize their use of resources including channels and time slots from primary users. The document develops efficient resource allocation algorithms for FLEC, including a distributed bargaining algorithm and centralized heuristic algorithm. It evaluates the performance of FLEC and shows it provides throughput improvements of 20-60% over conventional identical channel cooperation. A centralized heuristic algorithm achieves near-optimal performance with only 5% loss compared to the optimal centralized algorithm, providing a good tradeoff between performance and complexity.
OPTIMIZED COOPERATIVE SPECTRUM-SENSING IN CLUSTERED COGNITIVE RADIO NETWORKSijwmn
This document summarizes research on optimized cooperative spectrum sensing in clustered cognitive radio networks. The key points are:
1) Secondary users are grouped into clusters and transmit a power function of their observations to a fusion center on orthogonal channels to detect the presence of primary users.
2) The goal is to maximize the probability of detection for a given false alarm probability by optimizing the number of clusters, power function exponent, and linear combining coefficients at the fusion center.
3) Analytical expressions for the probability of detection and false alarm are derived based on the conditional mean and variance of the combined signal at the fusion center under the two hypotheses.
In order to improve sensing performance when the noise variance is not known, this paper considers a so-called
blind spectrum sensing technique that is based on eigenvalue models. In this paper, we employed the spiked population
models in order to identify the miss detection probability. At first, we try to estimate the unknown noise variance
based on the blind measurements at a secondary location. We then investigate the performance of detection, in terms
of both theoretical and empirical aspects, after applying this estimated noise variance result. In addition, we study the
effects of the number of SUs and the number of samples on the spectrum sensing performance.
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.
This document summarizes a research paper that proposes using two-hop relay with erasure coding to increase message delivery probability in mobile ad hoc networks (MANETs). It develops a finite-state absorbing Markov chain framework to model the message delivery process. Based on this, it derives closed-form expressions for message delivery probability under different message lifetimes and sizes. The key findings are that two-hop relay with erasure coding can improve delivery probability compared to traditional routing, and the probability varies based on message parameters and node density.
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...ijasuc
This paper addresses the maximizing network lifetime problem in wireless sensor networks (WSNs) taking
into account the total Symbol Error rate (SER) at destination. Therefore, efficient power management is
needed for extend network lifetime. Our approach consists to provide the optimal transmission power
using the orthogonal multiple access channels between each sensor. In order to deeply study the
properties of our approach, firstly, the simple case is considered; the information sensed by the source
node passes by a single relay before reaching the destination node. Secondly, global case is studied; the
information passes by several relays. We consider, in the previous both cases, that the batteries are nonrechargeable. Thirdly, we spread our work the case where the batteries are rechargeable with unlimited
storage capacity. In all three cases, we suppose that Maximum Ratio Combining (MRC) is used as a
detector, and Amplify and Forward (AF) as a relaying strategy. Simulation results show the viability of
our approach which the network lifetime is extended of more than 70.72%when the batteries are non
rechargeable and 100.51% when the batteries are rechargeable in comparison with other traditional
method.
ENERGY EFFICIENCY OF MIMO COOPERATIVE NETWORKS WITH ENERGY HARVESTING SENSOR ...ijasuc
This paper addresses the maximizing network lifetime problem in wireless sensor networks (WSNs) taking
into account the total Symbol Error rate (SER) at destination. Therefore, efficient power management is
needed for extend network lifetime. Our approach consists to provide the optimal transmission power
using the orthogonal multiple access channels between each sensor. In order to deeply study the
properties of our approach, firstly, the simple case is considered; the information sensed by the source
node passes by a single relay before reaching the destination node. Secondly, global case is studied; the
information passes by several relays. We consider, in the previous both cases, that the batteries are nonrechargeable. Thirdly, we spread our work the case where the batteries are rechargeable with unlimited
storage capacity. In all three cases, we suppose that Maximum Ratio Combining (MRC) is used as a
detector, and Amplify and Forward (AF) as a relaying strategy. Simulation results show the viability of
our approach which the network lifetime is extended of more than 70.72%when the batteries are non
rechargeable and 100.51% when the batteries are rechargeable in comparison with other traditional
method.
Manets: Increasing N-Messages Delivery Probability Using Two-Hop Relay with E...ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This document discusses quality of service analysis for secondary users in a cognitive radio system using an interweave access scenario. It presents a continuous time Markov chain model to calculate blocking probability and forced termination probability for secondary users. The model is analyzed for both fixed payload length and exponentially distributed payload lengths. Numerical results show that blocking probability and forced termination probability are greater for fixed payload length compared to exponentially distributed payload length.
This document presents a study on analyzing quality of service (QoS) parameters for secondary users in an interweave cognitive radio access scenario using a continuous time Markov chain (CTMC) model. The QoS parameters of blocking probability and force termination probability are calculated for both fixed payload length and exponential distribution payload length. Numerical results show that the blocking probability and force termination probability are greater for fixed payload length compared to exponential distribution payload length. CTMC models are developed to represent the system states and derive closed-form expressions for the QoS parameters for both payload scenarios. Graphs of the analytical results demonstrate the trends in blocking probability and force termination probability as payload length and other parameters are varied.
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.
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.
Using Subspace Pursuit Algorithm to Improve Performance of the Distributed Co...Polytechnique Montreal
This paper applies a compressed algorithm to improve the spectrum sensing performance of cognitive radio technology.
At the fusion center, the recovery error in the analog to information converter (AIC) when reconstructing the
transmit signal from the received time-discrete signal causes degradation of the detection performance. Therefore, we
propose a subspace pursuit (SP) algorithm to reduce the recovery error and thereby enhance the detection performance.
In this study, we employ a wide-band, low SNR, distributed compressed sensing regime to analyze and evaluate the
proposed approach. Simulations are provided to demonstrate the performance of the proposed algorithm.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Capacity Performance Analysis for Decode-and-Forward OFDMDual-Hop SystemPolytechnique Montreal
In this paper, we propose an exact analytical technique to evaluate the average capacity of a dual-hop OFDM relay system with decode-and-forward protocol in an independent and identical distribution (i.i.d.) Rayleigh fading channel. Four schemes, (no) matching “and” or “or” (no) power allocation, will be considered. First, the probability density function (pdf) for the end-to-end power channel gain for each scheme is described. Then, based on these pdf functions, we will give the expressions of the average capacity. Monte Carlo simulation results will be shown to confirm the analytical results for both the pdf functions and average capacities.
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.
Performance of cognitive radio networks with maximal ratio combining over cor...Polytechnique Montreal
This document analyzes the performance of cognitive radio networks using maximal ratio combining over correlated Rayleigh fading channels. It presents a simple analytical method to derive closed-form expressions for the probabilities of detection and false alarm. The key findings are:
1) The detection probability is a monotonically increasing function of the number of antennas, as more antennas provides more diversity gain.
2) Antenna correlation degrades the sensing performance compared to independent antennas. Higher correlation results in lower detection probability.
3) Complementary receiver operating characteristic curves illustrate that both higher signal-to-noise ratio and lower antenna correlation improve detection performance by increasing the detection probability and decreasing the probability of miss at a given false alarm probability.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...
Tech report
1. 1
Fair Channel Allocation and Access Design for
Cognitive Ad Hoc Networks
Le Thanh Tan and Long Bao Le
Abstract—We investigate the fair channel assignment and
access design problem for cognitive radio ad hoc network in
this paper. In particular, we consider a scenario where ad hoc
network nodes have hardware constraints which allow them
to access at most one channel at any time. We investigate a
fair channel allocation problem where each node is allocated a
subset of channels which are sensed and accessed periodically
by their owners by using a MAC protocol. Toward this end,
we analyze the complexity of the optimal brute-force search
algorithm which finds the optimal solution for this NP-hard
problem. We then develop low-complexity algorithms that can
work efficiently with a MAC protocol algorithm, which resolve
the access contention from neighboring secondary nodes. Also,
we develop an throughput analytical model, which is used in
the proposed channel allocation algorithm and for performance
evaluation of its performance. Finally, we present extensive
numerical results to demonstrate the efficacy of the proposed
algorithms in achieving fair spectrum sharing among traffic flows
in the network.
Index Terms—Channel assignment, MAC protocol, cognitive
ad hoc network, fair resource allocation.
I. INTRODUCTION
Cognitive radio has recently emerged as an important re-
search field, which promises to fundamentally enhance wire-
less network capacity in future wireless system. To exploit
spectrum opportunities on a given set of channels of interest,
each cognitive radio node must typically rely on spectrum
sensing and access mechanisms. In particular, an efficient
spectrum sensing scheme aims at discovering spectrum holes
in a timely and accurate manner while a spectrum access
strategy coordinates the spectrum access of different cognitive
nodes so that high spectrum utilization can be achieved. These
research themes have been extensively investigated in many
researchers in recent years [1]-[9]. In [1], a survey of recent
advances in spectrum sensing for cognitive radios has been
reported.
There is also a rich literature on MAC protocol design
and analysis under different network and QoS provisioning
objectives. In [2], a joint spectrum sensing and scheduling
scheme is proposed where each cognitive user is assumed to
possess two radios. A beacon-based cognitive MAC protocol
is proposed in [4] to mitigate the hidden terminal problem
while effectively exploiting spectrum holes. Synchronized and
channel-hopping based MAC protocols are proposed in [5] and
[6], respectively. Other multi-channel MAC protocols [7], [8]
are developed for cognitive multihop networks. However, these
existing papers do not consider the setting where cognitive
radios have access constraints that we investigate in this paper.
The authors are with INRS-EMT, University of Quebec, Montr´eal, Qu´ebec,
Canada. Emails: {lethanh,long.le}@emt.inrs.ca.
In [9], we have investigated the channel allocation problem
considering this access constraint for a collocated cognitive
network where each cognitive node can hear transmissions
from other cognitive nodes (i.e., there is a single contention
domain). In this paper, we make several fundamental contribu-
tions beyond [9]. First, we consider the large-scale cognitive
ad hoc network setting in this paper where there can be
many contention domains. In addition, the conflict constraints
become much more complicated since each secondary node
may conflict with several neighboring primary nodes and vice
versa. These complex constraints indeed make the channel
assignment and the throughput analysis very difficult. Second,
we consider a fair channel allocation problem under the max-
min fairness criterion [13] while throughput maximization is
investigated in [9]. Third, we propose optimal brute-force
search and low-complexity channel assignment algorithms and
analyze their complexity. Finally, we develop a throughput
analytical model, which is used in the proposed channel
allocation algorithms and for performance analysis.
II. SYSTEM MODEL AND PROBLEM FORMULATION
A. System Model
We consider a cognitive ad-hoc network where there are
Ms flows exploiting spectrum opportunities in N channels for
their transmissions. Each secondary flow corresponds to one
cognitive transmitter and receiver and we refer to secondary
flows as secondary users (SU) in the following. We assume
there are Mp primary users (PU) each of which can transmit
their own data on these N channels. We assume that each SU
can use at most one channel for his/her data transmission. In
addition, time is divided fixed-size cycle where SUs perform
sensing on assigned channels at the beginning of each cycle to
explore available channels for communications. For simplicity,
we assume that there is no sensing error although the analysis
presented in this paper can be extended to consider sensing
errors. It is assumed that SUs transmit at a constant rate which
is normalized to 1 for throughput calculation purposes.
To model the interference among SUs in the secondary
network, we form a contention graph G = {N, L}, where
N = {1, 2, . . . , Ms} is the set of nodes (SUs) representing
SUs and the set of links L = {1, 2, . . . , L} representing
contention relationship among SUs. In particular, there is a
link between two SUs in L if these SUs cannot transmit
packet data on the same channel at the same time, which is
illustrated in Fig. 1. To model the activity of PUs on each
channel, let us define pp
ij as the probability that PU i does
not transmit on channel j. We stack these probabilities and
define Pi = (pp
i1, . . . , pp
iN ), i ∈ [1, Mp], which captures the
activity of PU i on all channels. In addition, let us define
2. 2
Fig. 1. The contention graph.
Pp
=
(
P1, . . . , PMp
)
where Pi is the vector representing
activities of PU i.
We now model the contention relationship among SUs and
between PUs and SUs. Specifically, we assume that Un
i be the
set of neighboring SUs that conflict with SU i (i.e., there is
a link connecting each SU in Un
i to SU i in the contention
graph). Also, assume that SU k has a set of neighboring PUs
denoted as Up
k , which is the subset of 1, . . . , Mp so that if
any PU in the set Up
k transmit on a particular channel then
SU k is not allowed to transmit on this channel to protect the
primary transmission. Assuming that the activities of different
PUs on any channel are independent then the probability that
channel j is available for SU k indicates can be written as
pkj =
∏
i∈Up
k
pp
ij since channel j is available for SU k if all
conflicting PUs in Up
k do not use channel j.
B. Problem Formulation
We are interested in performing channel assignment that
maximizes the minimum throughput among all SUs (i.e., max-
min fairness [13]). Let Ti denote the throughput achieved by
SU i. Let xij describe the channel assignment decision where
xij = 1 if channel j is assigned to SU i and xij = 0,
otherwise. Then, the max-min channel assignment problem
can be written as
max
x
min
i
Ti (1)
where x is the channel assignment vector whose elements are
xij. For the case where each SU is allocated a distinct set of
channels, i.e., we have
Ms∑
i=1
xij = 1, for all j. Under this non-
overlapping channel assignments, let Si be the set of channels
assigned to SU i. Recall that pij is the probability that channel
j is available at SU i. Then, Ti can be calculated as Ti =
1 −
∏
j∈Si
pij = 1 −
N∏
j=1
(¯pij)
xij
where pij = 1 − pij is the
probability that channel j is not available for SU i [9]. In
fact, 1 −
∏
j∈Si
pij is the probability that there is at least one
channel available for SU i. Because each SU can use at most
one available channel, its maximum throughput is 1.
In general, it would be beneficial if each channel is allocated
to several SUs in a common neighborhood to exploit the multi-
user diversity. Under both non-overlapping and overlapping
channel assignments, it can be observed that the channel
assignment problem with the objective defined in (1) is a non-
linear integer program, which is a NP-hard problem (interest
readers can refer to [12] for detailed treatment of this hardness
result).
C. Optimal Algorithm and Its Complexity
We describe a brute-force search (i.e., exhaustive search) to
determine the optimal channel assignment solution. Specifi-
cally, we can enumerate all possible channel assignment solu-
tions then determine the best one by comparing their achieved
throughput. While throughput can be calculated quite easily for
the non-overlapping channel assignments as being presented in
Section II-B, developing a throughput analytical model for an
overlapping channel assignment solution is indeed challenging
task, which is performed in Section III-B2 of this paper.
We now quantify the complexity of the optimal brute-force
search algorithm. Let us consider SU i (i.e., i ∈ {1, . . . , Ms}).
Suppose we assign it k channels where k ∈ {1, . . . , N}).
Then, there are Ck
N ways to do so. Since k can take any
values in k ∈ {1, . . . , N}, the total number of ways to assign
channels to SU i is
N∑
k=1
Ck
N ≈ 2N
. Hence, the total number
of ways to assign channels to all SUs is
(
2N
)Ms
= 2NMs
.
Recall that we need to calculate the throughputs achieved
by Ms SUs for each potential assignment to determine the
best one. Therefore, the complexity of the optimal brute-
force search algorithm is O(2NMs
). Given the exponentially
large complexity of this brute-force search, we will develop
low-complexity channel assignment algorithms, namely non-
overlapping and overlapping assignment algorithms.
III. CHANNEL ALLOCATION AND ACCESS DESIGN
A. Non-overlapping Channel Assignment
We develop a low-complexity algorithm for non-overlapping
channel assignment in this section. Recall that Si is the set of
channels assigned for secondary user i. In the non-overlapping
channel assignment scheme, we have Si ∩ Sj = ∅, i ̸= j
where SUs i and j are neighbors of each other (i.e., there
is a link connecting them in the contention graph G). Note
that one particular channel can be assigned to SUs who are
not neighbors of each other. This aspect makes the channel
assignment different from the collocated network setting con-
sidered in [9]. Specifically all channels assigned for different
SUs should be different in [9] under non-overlapping channel
assignment since there is only one contention domain for the
collocated network investigated in [9].
The greedy channel assignment algorithm iteratively allo-
cates channels to one of the minimum-throughput SUs so that
we can achieve maximum increase in the throughput for the
chosen SU. Detailed description of the proposed algorithm is
presented in Algorithm 1. In each channel allocation iteration,
each minimum-throughput SU i calculates its increase in
throughput if the best available channel (i.e., channel j∗
i =
arg max
j∈Sa
pij) is allocated. This increase in throughput can be
calculated as ∆Ti = Ta
i − Tb
i = pij∗
i
∏
j∈Si
(1 − pij) [9].
In step 4, there may be several SUs achieving the minimum
throughput. We denote this set of minimum-throughput SUs
3. 3
Algorithm 1 NON-OVERLAPPING CHANNEL ASSIGNMENT
1: Initialize SU i’s set of available channels, Sa
i :=
{1, 2, . . . , N} and Si := ∅ for i = 1, 2, . . . , Ms where
Si denotes the set of channels assigned for SU i.
2: continue := 1
3: while continue = 1 do
4: Find the set of SUs who currently achieve the min-
imum throughput Smin
= argmin
i
Tb
i where Smin
=
{i1, . . . , im} ⊂ {1, . . . , Ms} is the set of minimum-
throughput SUs.
5: if OR
il∈Smin
(
Sa
il
̸= ∅
)
then
6: For each SU il ∈ Smin
and channel jil
∈ Sa
il
,
find ∆Til
= Ta
il
− Tb
il
where Ta
il
and Tb
il
are the
throughputs after and before assigning channel jil
;
and we set ∆Til
= 0 if Sa
il
= ∅
7:
{
i∗
l , j∗
i∗
l
}
= argmax
il∈Smin,jil
∈Sa
il
∆Til
(jil
)
8: Assign channel j∗
i∗
l
to SU i∗
l .
9: Update Si∗
l
= Si∗
l
∪ j∗
i∗
l
and Sa
k = Sa
k j∗
i∗
l
for all
k ∈ Un
i∗
l
.
10: else
11: Set continue := 0
12: end if
13: end while
as Smin
. Then, we assign the best channel that results in
the maximum increase of throughput among all SUs in the
set Smin
. We update the set of available channels for each
SU after each allocation. Note that only neighboring SUs
compete for the same channel; hence, the update of available
channels for the chosen minimum-throughput SU is only
performed for its neighbors. This means that we can exploit
spatial reuse in a large cognitive ad hoc network. It can be
verified that if the number of channels is sufficiently large (i.e.,
N>>maxi |Un
i |), then the proposed non-overlapping channel
assignment achieves throughput close to 1 for all SUs.
B. Overlapping Channel Assignment
1) MAC Protocol: Overlapping channel assignment can
improve the minimum throughput but we need to design a
MAC protocol to resolve access contention among different
SUs. Note that a channel assignment solution needs to be
determined only once while the MAC protocol operates re-
peatedly using the chosen channel assignment solution in each
cycle. Let Si be the set of channels solely assigned for SU i
and Scom
i be the set of channels assigned for SU i and some
other SUs. These two sets are referred to as separate set and
common set in the following. Let denote Stot
i = Si ∪ Scom
i ,
which is the set of all channels assigned to SU i.
Assume that there is one control channel, which is always
available and used for access contention resolution. We con-
sider the following MAC protocol run by any particular SU
i, which belongs the class of synchronized MAC protocol
[11].1
The MAC protocol operates a cyclic manner where
1Since we focus on the channel assignment issue in this paper, we do not
attempt different alternative MAC protocol designs. Interest readers can refer
to [11] for detailed treatment of this issue.
synchronization and sensing phases are employed before the
channel contention and transmission phase in each cycle.
After sensing the assigned channels in the sensing phase, if
a particular SU i finds at least one channel in Si available,
then it chooses one of these available channels randomly for
communication. If this is not the case, SU i will choose
one available channel in Scom
i randomly (if any). Then, it
chooses a random backoff value which is uniformly distributed
in [0, W − 1] (i.e., W is the contention window) and starts
decreasing its backoff counter while listening on the control
channel.
If it overhears transmissions of RTS/CTS from any other
SUs, it will freeze from decreasing its backoff counter until
the control channel is free again. As soon as a SU’s backoff
counter reaches zero, its transmitter and receiver exchange
RTS/CTS messages containing the chosen available channel
for communication. If the RTS/CTS message exchange fails
due to collisions, the corresponding SU will quit the contention
and wait until the next cycle. In addition, by overhearing
RTS/CTS messages of neighboring SUs, which convey infor-
mation about the channels chosen for communications, other
SUs compared these channels with their chosen ones. Any SU
who has his/her chosen channel coincides with the overheard
channels quits the contention and waits until the next cycle.
Note that in the considered cognitive ad hoc setting each SU
i only competes with its neighbors in the set Un
i , which is
different from the setting investigated in [9].
2) Throughput Analysis: To analyze the throughput
achieved by one particular SU i, we consider all possible
sensing outcomes for the considered SU i on its assigned
channels. We will consider the following cases.
• Case 1: If there is at least one channel in Si available,
then SU i will exploit this available channel and achieve
the throughput of one. Here, we have
Ti {Case 1} = Pr {Case 1} = 1 −
∏
j∈Si
¯pij.
• Case 2: We consider scenarios where all channels in Si
are not available; there is at least one channel in S com
i
available, and SU i chooses the available channel j for
transmission. Suppose that channel j is shared by SU i
and MSj neighboring SUs (i.e., MSj = |Uj| where
Uj denotes the set of these MSj neighboring SUs).
Recall that all MSj SUs conflict with SU i (i.e., they
are not allowed to transmit data on the same channel
with SU i). There are four possible groups of SUs ik,
k = 1, . . . , MSj sharing channel j, which are described
in the following
– Group I: channel j is not available for SU ik.
– Group II: channel j is available for SU ik and SU
ik has at least 1 channel in Sik
available.
– Group III: channel j is available for SU ik, all
channels in Sik
are not available and there is another
channel j′
in Scom
ik
available for SU ik. In addition,
SU ik chooses channel j′
̸= j for transmission in
the contention stage.
– Group IV: channel j is available for SU ik, all chan-
nels in Sik
are not available. Also, SU ik chooses
4. 4
channel j for transmission in the contention stage.
Hence, SU ik competes with SU i for channel j.
Let Up
j,i be the set of PUs who are neighbors of SUs in Uj.
Then, the throughput achieved by SU i can be written as
Ti ( Case 3) = (1 − δ)Θi
MSj
∑
A1=0
MSj −A1
∑
A2=0
MSj −A1−A2
∑
A3=0
1
1 + A4
C
A1
MSj
∑
c1=1
C
A2
MSj −A1
∑
c2=1
C
A3
MSj −A1−A2
∑
c3=1
ΘjΦ1(A1)Φ2(A2)Φ3(A3)
where A4 = MSj − A1 − A2 − A3 and δ denotes the MAC
protocol overhead, which will be derived in Section III-B4. In
this derivation, we consider all possible cases where SUs in
Uj are divided into four groups defined above with sizes A1,
A2, A3, and A4, respectively. For one such particular case,
let Up,1
j,i be the set of PUs who are only neighbors of SUs
in group I with size A1 and Up,2
j,i = Up
j,iUp,1
j,i be the set of
remaining PUs in Up
j,i. In addition, let Up,3
j,i be the set of PUs
who are neighbors of SUs in group III and IV with sizes A3
and A4, respectively. The terms Θi , Θj, Φ1(A1), Φ2(A2),
and Φ3(A3) in the above derivation are
• Θi is the probability that all channels in Si are not
available and SU i chooses an available channel j in Scom
i
for transmission.
• Θj is the probability that all PUs in Up,2
j,i do not use
channel j.
• Φ1(A1) denotes the total probability of all cases for PUs
in Up,1
j,i such that channel j is not available for all A1
SUs in group I.
• Φ2(A2) represents the probability that there is at least
one available channel in the separate set for each of the
A2 SUs in Group II.
• Φ3(A3) describes the total probability of all cases for
PUs in Up,3
j,i such that each SU in group III chooses
other available channel j
′
̸= j for transmission, each SU
in group IV chooses channel j for transmission; and all
channels in the separate sets of users in group III and
group IV are not available.
In this formula, we have considered all possible events and
combinations that can happen for neighboring SUs of the
underlying SU i. Note that only A4 SUs in Group IV compete
with SU i for channel j by using the proposed MAC pro-
tocol. Therefore, SU i wins this contention with probability
1/(1 + A4). In addition, the throughput is reduced by a factor
1 − δ where δ is the MAC protocol overhead.
We derive these probabilities in the following. First, we can
calculate the probability Θi as
Θi =
[
∏
k∈Si
pik
]
.
Hi∑
Bi=1
C
Bi
Hi∑
h=1
∑
j∈Ψh
i
1
Bi
∏
j1∈Ψh
i
pij1
∏
j2∈Scom
i Ψh
i
pij2
(2)
where Hi denotes the number of channels in Scom
i . The first
product term in (2) represents the probability that all channels
in Si are not available for SU i. The second term in (2) de-
scribes the probability that SU i chooses an available channel j
among Bi available channels in Scom
i for transmission. Here,
we consider all possible cases where there are Bi available
channels in Scom
i and Ψh
i describes one particular set of Bi
available channels. Next, we can express the probability Θj
as
Θj =
∏
l∈Up,2
j,i
pp
lj. (3)
To calculate Φ1(A1), let Ωp
1 be an index set capturing all
possible cases for which channel j is not available for all A1
SUs in group I. Then, the probability Φ1(A1) can be expressed
as
Φ1(A1) =
∑
h∈Ωp
1
∏
i1∈Λp
h
pp
i1j
∏
i2∈Up,1
j,i Λp
h
pp
i2j (4)
where pp
ij = 1 − pp
ij; and Λp
h ⊆ Up,1
j,i denotes the set of PUs
in Up,1
j,i , which do not transmit on channel j for the particular
case h ∈ Ωp
1. Furthermore, the probability Φ2(A2) can be
calculated as
Φ2(A2) =
∏
i∈A2
Li∑
Ci=1
C
Ci
Li∑
h=1
∑
k∈Ωh
i
∏
j1∈Λh
k
pij1
∏
j2∈SiΛh
k
pij2
(5)
where Li denotes the total number of channels in the separate
set Si of SU i. In this equation, we consider all possible cases
for which each SU i ∈ A2 has Ci ≥ 1 channels available in
their separate set. In addition, Λh
k denotes a particular set (i.e.,
a subset of Si) with Ci available channels. Finally, we can
express the probability Φ3(A3) as
Φ3(A3) =
[
∏
i∈A3∪A4
∏
n∈Si
pin
]
.
∑
i3∈A3
|Si3 |−1
∑
ki3 =1
∑
i4∈A4
|Si4 |−1
∑
ki4 =0
(6)
∏
i3∈A3
1
1 + ki3
∏
i4∈A4
1
1 + ki4
C
ki3
|Si3
|−1
∑
h3=1
C
ki4
|Si4
|−1
∑
h4=1
(7)
∏
j∈Scom
A3∪A4
∏
i1∈Λp,j
h3,h4
pp
i1j
∏
i2∈Up,1
j,i Λp,j
h3,h4
pp
i2j (8)
where A3 and A4 denote the sets of SUs belonging to groups
III and IV described above, respectively. The product term
inside [.] in (6) captures the probability that all SUs in A3 ∪A4
have no channel available in their separate sets. In addition,
we consider all possible cases where each SU i3 ∈ A3 chooses
channel j′
̸= j for transmission while each SU i4 ∈ A4
chooses channel j among available channels for transmission.
For each particular case represented by indices h3 and h4,
there are a set of PUs Λp,j
h3,h4
which do not use channel
j ∈ SA3∪A4 where Scom
A3∪A4
=
∪
i∈A3∪A4
Scom
i .
Summarizing all considered cases, the throughput achieved
by SU i is given as
Ti = Ti {Case 1} + Ti {Case 2} . (9)
This throughput derivation is used for channel assignment and
performance evaluation of the proposed algorithms.
3) Configuration of Contention Window: We show how to
calculate contention window W so that collision probabilities
among contending SUs are sufficiently small. Note that the
probability of the first collision among potential collisions is
largest because the number of contending SUs decreases for
5. 5
successive potential collisions. Derivation of these collision
probabilities for the cognitive ad-hoc networks is more com-
plicated than that for collocated networks considered in [9]
since the interference constraints are more complicated.
We calculate contention window Wk for each SU k consid-
ering the contention with its neighbors. Let us calculate Pc,k
as a function of Wk assuming that there are m secondary SUs
in the contention phase. Without loss of generality, assume
that the random backoff times of m SUs are ordered as
r1 ≤ r2 ≤ . . . ≤ rm. The conditional probability of the first
collision if there are m SUs in the contention stage can be
written as
P
(m)
c,k =
m∑
j=2
Pr (j users collide)
=
m∑
j=2
Wk−2∑
l=0
Cj
m
(
1
Wk
)j (
Wk − l − 1
Wk
)m−j
(10)
where each term in the double-sum represents the probability
that j users collide when they choose the same backoff value
equal to l. Hence, the probability of the first collision can be
calculated as
Pc,k =
Mn
k∑
m=2
P
(m)
c,k × Pr {m users contend} , (11)
where Mn
k = |Un
k | + 1 is the total number of SUs (in-
cluding SU k and its neighbors), P
(m)
c,k is given in (10) and
Pr {m users contend} is the probability that m SUs contend
with SU k in the contention phase. To compute Pc,k, we now
derive Pr {m users contend}.
We can divide the set of neighbors of SU k into two groups.
In particular, there are m SUs contending with SU k while
the remaining Mn
k − m SUs do not join the contention phase.
There are Cm
Mn
k
such combinations for a particular value of m
where it happens with the following probability
Pr {m users contend} =
Cm
Mn
k∑
n=1
P(n)
con (12)
where P
(n)
con is the probability of one particular case where m
SUs contend with SU k. We can divide the set of remaining
Mn
k − m SUs who do not join the contention into two
subgroups, namely SUs who could not find any available
channels in their allocated channels Stot
i2
(first subgroup) and
SUs who find some available channels in their separate sets
Si1
(second subgroup).
Now, let Λn be one particular set of m SUs in the first
group and A1 denote the number of SUs in the first subgroup
of the remaining Mn
k − m SUs. Then, we can calculate P
(n)
con
as follows:
P(n)
con =
∏
i1∈Λn
∏
l1∈Si1
pi1l1
(13)
Mn
k −m
∑
A1=0
C
A1
Mn
k
−m
∑
c1=1
∏
i2∈Ω
(1)
c1
∏
l2∈Si2
pi2l2
∏
i3∈Ω
(2)
c1
1 −
∏
l3∈Si3
pi3l3
(14)
β(1)
∑
n(1)=1
Cn(1)
β(1)
∑
q(1)=1
. . .
β(m)
∑
n(m)=1
Cn(m)
β(m)
∑
q(m)=1
∏
i4∈Up
c1
∏
l4∈Λ
(1)
c1
pp
i4l4
∏
l5∈Λ
(2)
c1
pp
i4l5
. (15)
The term inside [.] in (13) represents the probability that all
channels in the separate sets Si1 for all SUs i1 ∈ Λn are
not available so that these SUs contend to access available
channels in Scom
i1
. The term in (14) denotes the probability
that each of A1 SUs in the first subgroup (i.e., in the set Ω
(1)
c1 )
find no available channels in their separate sets and each of
the Mn
k −m−A1 SUs in the second subgroup (i.e., in the set
Ω
(2)
c1 ) find at least one available channel in their separate sets
(therefore, these SUs will not perform contention). Here, c1 is
the index of one particular case where there are A1 SUs in the
first subgroup and a particular set Λn. The last term in (15)
denotes the probability of the event representing the status of
all PUs who are neighbors of SUs in the set Un
k (i.e., neighbors
of SU k) so that there are exactly m contending SUs in the
set Λn and A1 SUs in the first subgroup. In (15) we consider
all possible scenarios where for each SU i ∈ Λn, there are
n(i)
available channels among β(i)
= |Scom
i | channels in the
set Scom
i where q(i)
represents the index of one such particular
case. Corresponding to such (n(i)
, q(i)
), Up
c1
denotes the set
of PUs who are neighbors of SUs in Un
k so that indeed m
underlying SUs perform contention.
By substituting P
(n)
con calculated above into (12), we can
calculate the collision probability in Pc,k in (11). From this,
we can determine Wk as follows:
Wk = min {Wk such that Pc,k(Wk) ≤ ϵPk
} (16)
where ϵPk
controls the collision probability and overhead
tradeoff and for clarity we denote Pc,k(Wk), which is given
in (11) as a function of Wk. Then, we will determine the
contention window for all SUs as W = maxk Wk.
4) Calculation of MAC Protocol Overhead: Let r be the
average value of the backoff value chosen by any SU. Then,
we have r = (W − 1)/2 because the backoff counter value
is uniformly chosen in the interval [0, W − 1]. As a result,
average overhead can be calculated as follows:
δ (W) =
[W − 1] θ/2 + tRTS + tCTS + 3tSIFS + tSEN + tSYN
Tcycle
where θ is the time corresponding to one backoff unit; tRTS,
tCTS, tSIFS are the corresponding time of RTS, CTS and SIFS
(i.e., short inter-frame space) messages; tSEN is the sensing
time; tSYN is the transmission time of the synchronization
message; and Tcycle is the cycle time.
5) Overlapping Channel Assignment Algorithm: In the
overlapping channel assignment algorithm described in Algo-
rithm 2, we run Algorithm 1 to obtain the non-overlapping
channel assignment solution in the first phase and perform
overlapping channel assignments by allocating channels that
have been assigned to a particular SU to other SUs in the sec-
ond phase. We calculate the increase-of-throughput metric for
all potential channel assignments that can improve the through-
put of minimum-throughput SUs. To calculate the increase-
of-throughput, we use the throughput analytical model in
Subsection III-B2, where the MAC protocol overhead, δ < 1
is derived from III-B4. After running Algorithm 1 in the first
6. 6
Algorithm 2 OVERLAPPING CHANNEL ASSIGNMENT
1: After running Algorithm 1, each SU i has Si, Scom
i = ∅
and Sn
i , i = 1, . . . , Ms.
2: continue := 1.
3: while continue = 1 do
4: Find Tmin and i∗
= argmin
i∈{1,...,Ms}
Tb
i .
5: SUni
i∗ =
∪
l∈Un
i∗
Stot
l .
6: SSep
i∗ = SETXOR
l∈Un
i∗
(Stot
l ).
7: SInt
i∗ = SUni
i∗ SSep
i∗ Scom
i∗ .
8: Find all minimum-throughput SUs and find the best
channels from either SSep
i∗ or SInt
i∗ for these minimum-
throughput SUs to improve the overall minimum
throughput.
9: if
∪
i∈{1,...,Ms}
Scom,temp
i ̸= ∅ then
10: Assign Scom
i = Scom,temp
i and Si = Stemp
i .
11: else
12: Set continue := 0.
13: end if
14: end while
phase, each SU i has the set of assigned non-overlapping
channels, Si, and it initiates the set of overlapping channels
as Scom
i = ∅, i = 1, . . . , Ms. Recall that the set of all assigned
channels for SU i is Stot
i = Si ∪ Scom
i . Let SUni
i∗ is the set of
all channels that have been assigned for SU i∗
’s neighboring
SUs. Also, let SSep
i∗ be the set of all channels assigned solely
for each individual neighbor of SU i∗
(i.e., each channel in
SSep
i∗ is allocated for only one particular SU in Un
i∗ ). Therefore,
SInt
i∗ defined in step 7 of Algorithm 2 is the set of “intersecting
channels”, which are shared by at least two neighbors of SU
i∗
. Here, SETXOR(A,B) would return the set of all elements
in A or B but not the common elements of both A and B.
In each iteration, we determine the set of SUs which achieve
the minimum throughput. Then, we need to search over two
sets SSep
i∗ or SInt
i∗ to find the best channel for each of these
minimum-throughput SUs. Note that allocation of channels
in SInt
i∗ to minimum-throughput SUs can indeed decrease
the achievable throughput of their owners (i.e., SUs which
own these channels before the allocation). Therefore, channel
allocations in step 8 are only performed if the minimum
throughput can be improved. In step 9, Scom,temp
i is the
potential set of channels for SU i. Algorithm 2 terminates
when there is no assignment that can improve the minimum
throughput. Detailed description of step 8 of Algorithm 2 is
given in Algorithm 3.
6) Complexity Analysis: In each iteration of Algorithm 1,
the number of minimum-throughput SUs is at most Ms and
there are at most N channel candidates which can be allocated
for each of them. Therefore, the complexity involved in each
iteration is upper bounded by MsN. We can also determine
an upper bound for the number of iterations, which is MsN.
This is simple because each SU can be allocated at most N
channels and there are Ms SUs. Therefore, the complexity of
Algorithm 1 is upper bounded by M2
s N2
. In Algorithm 2, we
run Algorithm 1 in the first phase and perform overlapping
Algorithm 3 SEARCH POTENTIAL CHANNEL ASSIGNMENT
1: (*Search potential channel assignment from separating
sets*)
2: for j ∈ SSep
i∗ do
3: Find SU i′
where j ∈ Stot
i′ and i′
∈ Sn
i∗ (j ∈ Si′ or
Scom
i′ ).
4: for l = 0 to |Sn
i∗ | − 1 do
5: for k = 1 to Cl
|Sn
i∗ |−1
do
6: Find Ta
i∗ , Ta
i′ and Ta
m m∈Ul
j
, where Ul
j is the set
of l new SUs sharing channel j.
7: if min
(
Ta
i∗ , Ta
i′ , Ta
m m∈Ul
j
)
> Tmin then
8: Temporarily assign channel j to SU i∗
, all SUs
m and i′
: Scom,temp
i∗ = Scom
i∗ ∪ j, Scom,temp
m =
Scom
m ∪j; and if j ∈ Si′ then Scom,temp
i′ = Scom
i′ ∪j,
Stemp
i′ = Si′ j.
9: Update Tmin = min
(
Ta
i∗ , Ta
i′ , Ta
m m∈Ul
j
)
.
10: Reset all temporary sets of other SUs to be
empty.
11: end if
12: end for
13: end for
14: end for
(*Search potential channel assignment from separating
sets*)
15: for j ∈ SInt
i∗ do
16: Find the subset of SUs i′
, SUse
who use channel j as a
non-overlapping or overlapping channel (i.e., j ∈ Stot
i′ );
and SUse
⊂ Sn
i∗ .
17: for l = 0 to |Sn
i∗ | − SUse
do
18: for k = 1 to Cl
|Sn
i∗ |−|SUse|
do
19: Find Ta
i∗ , Ta
i′ |i′∈SUse and Ta
m m∈Ul
j
, where Ul
j is
the set of l new SUs sharing channel j.
20: if min
(
Ta
i∗ , Ta
i′ |i′∈SUse , Ta
m m∈Ul
j
)
> Tmin then
21: Temporarily assign channel j to SU i∗
, all
SUs m and all SUs i′
: Scom,temp
i∗ = Scom
i∗ ∪ j,
Scom,temp
m = Scom
m ∪ j; and if j ∈ Si′ , then
Scom,temp
i′ = Scom
i′ ∪ j, Stemp
i′ = Si′ j.
22: Update Tmin =
min
(
Ta
i∗ , Ta
i′ |i′∈SUse , Ta
m m∈Ul
j
)
.
23: Reset all temporary sets of other SUs to be
empty.
24: end if
25: end for
26: end for
27: end for
channel assignments in the second phase. The complexity
of this second phase can also be upper-bounded by M2
s N2
.
Therefore, the complexity of both Algorithms 1 and 2 can be
upper-bounded by O
(
M2
s N2
)
, which is much lower than that
of the brute-force search algorithm presented in Section II-C.
IV. NUMERICAL RESULTS
To obtain numerical results, we choose the length of control
packets as follows: RTS including PHY header 288 bits, CTS
7. 7
Fig. 2. The scenario with 3 SUs and 2 PUs.
2 3 4 5 6
0.4
0.5
0.6
0.7
0.8
0.9
1
Number of channels (N)
Throughput(T)
Non−The
Non−Sim
Ove−The
Ove−Sim
Opt−The
Opt−Sim
p
p
ij
= 0.8
p
p
ij
= 0.6
(a)
5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
Number of channels (N)
Throughput(T)
Non−The
Non−Sim
Ove−The
Ove−Sim
p
p
ij
= 0.6
p
p
ij
= 0.8
(b)
Fig. 3. Throughput versus the number of channels, pp
ij = 0.6 and 0.8,
Non: Non-overlapping, Ove: Overlapping, The: Theory, Sim: Simulation,
Opt:Optimal.(a) Mp = 2, Ms = 3 (b) Mp = 5, Ms = 8
0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
0.2
0.4
0.6
0.8
0.9
pp
ij
Throughput(T)
Non−The, N = 7
Non−Sim, N = 7
Ove−The, N = 7
Ove−Sim, N = 7
Non−The, N = 9
Non−Sim, N = 9
Ove−The, N = 9
Ove−Sim, N = 9
(a)
1 2 3 4 5 6 7 8
0
0.2
0.4
0.6
0.8
1
SU
Throughput(Ti)
Non−The
Non−Sim
Ove−The
Ove−Sim
(b)
Fig. 4. (a) Throughput versus pp
ij, N = 7 and 9. (b) Throughput achieved
by each SU, Mp = 5, Ms = 8, pp
ij = 0.8, N = 8.
including PHY header 240 bits, which correspond to tRTS =
48µs, tCTS = 40µs for transmission rate of 6 Mbps, which is
the basic rate of 802.11a/g standards [14]. Other parameters
are chosen as follows: cycle time Tcycle = 3ms; θ = 20µs,
tSIFS = 28µs, target collision probability ϵP = 0.03; tSEN and
tSYN are assumed to be negligible so they are ignored. Note
that these values of θ and tSIFS are typical (e.g., see [10]).
To compare the performance of optimal brute-force search
and our proposed algorithms, we consider a small network
shown in Fig. 2 where we choose Ms = 3 SUs, Mp = 2 PUs
and pp
ij = 0.6 and 0.8. Fig. 3(a) shows that the minimum
throughputs achieved by Algs 2 are very close to that obtained
the optimal search, which confirms the merit of this low-
complexity algorithm. Also, the simulation results match the
analytical results very well, which validates the proposed
throughput analytical model. Figs. 3(b), 4(a), and 4(b) il-
lustrate the minimum throughputs achieved by our proposed
algorithms for a larger network shown in Fig. 1. In particular,
Fig. 3(b) shows the minimum throughput versus the number
of channels for pp
ij equal to 0.6 and 0.8. This figure confirms
that Alg. 2 achieves significantly larger throughput than that
due to Alg. 1 thanks to overlapping channel assignments.
Fig. 4(a) illustrates the minimum throughput versus pp
ij. It
can be observed that as pp
ij increases, the minimum achievable
throughput indeed increases. This figure also shows that the
minimum throughput for N = 9 is greater than that for
N = 7. This means our proposed algorithms can efficiently
exploit available spectrum holes. In Fig. 4(b), we illustrate
the throughputs achieved by different SUs to demonstrate the
fairness performance. It can be observed that the differences
between the maximum and minimum throughputs under Alg.
2 is much smaller than that due to Alg. 1. This result implies
that Alg. 2 not only achieves better throughput but also results
in improved fairness compared to Alg. 1.
V. CONCLUSION
We have investigated the fair channel allocation problem
in cognitive ad hoc networks. Specifically, we have pre-
sented both optimal brute-force search and low-complexity
algorithms and analyzed their complexity and throughput
performance through analytical and numerical studies.
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