1. The document reviews various techniques for blind channel estimation in orthogonal frequency division multiplexing (OFDM) systems, including subspace-based, iterative, maximum likelihood, and cyclostationarity-based approaches.
2. It compares the performance of blind, semi-blind, and trained channel estimators in terms of speed of convergence and complexity.
3. The review finds that deterministic techniques like maximum likelihood converge faster but have higher complexity than statistical techniques, and that semi-blind estimators can achieve better performance than blind or trained estimators in some scenarios.
ANALYSIS AND STUDY OF MATHEMATICAL MODELS FOR RWA PROBLEM IN OPTICAL NETWORKSIJEEE
Blocking probability has been one of the key performance to solve Routing and Wavelength Assignment problem (RWA) indexes in the design of wavelength-routed all-optical WDM networks. To evaluate blocking probability different analytical model are introduced.
Dynamic bandwidth allocation scheme in lr pon with performance modelling and ...IJCNCJournal
We consider models of telecommunication systems that incorporate probability, dense real-time and data.
We present a new formal abstraction method for computing minimum and maximum reachability
probabilities for such models. Our approach uses strictly local formal abstract steps to reduce both the size
of abstract specifications generated and the complexity of operations needed, in comparison to previous
approaches of this kind. A selection of large case studies are implemented the techniques and evaluate,
which include some infinite-state probabilistic real time models, demonstrating improvements over existing
tools in several cases. The capacity of metro and access networks are extended the reach and split ratio of
the conventional Long - Reach Passive Optical Networks (LR-PONs). The efficient solutions of LR-PONs
are appeared in feeder distances around 100km and high split ratios up to 1000-way . Among many
existing approaches, one of the most effective options to improve network performance in LR-PONs are the
multi-thread based dynamic bandwidth allocation (DBA) scheme where several bandwidth allocation
processes are performed in parallel is considered. Without proper intercommunication between the
overlapped threads, multi-thread DBA may lose efficiency and even perform worse than the conventional
single thread algorithm. Real Time Probabilistic Systems are used to evaluate a typical PON systems
performance. This approach is more convenient, flexible, and lower cost than the former simulation method,
which do not need develop special hardware and software tools. Moreover, how changes in performance
depend on changes in the particular modes can be easily analysis by supplying ranges for parameter values.
The proposed algorithm with traditional DBA is compared, and shows its advantage on average packet
delay. The key parameters of the algorithm are analysed and optimized, such as initiating and tuning
multiple threads, inter -thread scheduling, and fairness among users. The algorithms advantage in
numerical results are decreased the average packet delay and improve network throughput under varying
offered loads.
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.
Spectral Efficient Blind Channel Estimation Technique for MIMO-OFDM Communica...IJAAS Team
With emerge of increasing research in the domain of future wireless communications, massive MIMO (multiple inputs multiple outputs) attracted most of researchers interests. Massive MIMO is high-speed wireless communication standards. A channel estimation technology plays the essential role in the MIMO systems. Efficient channel estimation leads to spectral efficient wireless communications. The critics of Inter-Symbol Interference (ISI) are the challenging tasks while designing the channel estimation methods. To mitigate the challenges of ISI, we proposed the novel blind channel estimation method which based on Independent component analysis (ICA) in this paper. Proposed channel estimation it works for both blind interference cancellation and ISI cancellation. The proposed Hybrid ICA (HICA) method depends on pulse shape filtering and ambiguity removal to improve the spectral efficiency and reliability for MIMO communications. The Kurtosis operation is used to measure the complex data at first to estimate the common signals. Then we exploited the advantages of 3rd and 4th order Higher Order Statistics (HOS) to priorities the common signals during the channel estimation. In this paper, we present the detailed design and evaluation of HICA blind channel estimation method. We showed the simulation results of HICA against the state-of-art techniques for channel estimation using BER, MSE, and PAPR.
ANALYSIS AND STUDY OF MATHEMATICAL MODELS FOR RWA PROBLEM IN OPTICAL NETWORKSIJEEE
Blocking probability has been one of the key performance to solve Routing and Wavelength Assignment problem (RWA) indexes in the design of wavelength-routed all-optical WDM networks. To evaluate blocking probability different analytical model are introduced.
Dynamic bandwidth allocation scheme in lr pon with performance modelling and ...IJCNCJournal
We consider models of telecommunication systems that incorporate probability, dense real-time and data.
We present a new formal abstraction method for computing minimum and maximum reachability
probabilities for such models. Our approach uses strictly local formal abstract steps to reduce both the size
of abstract specifications generated and the complexity of operations needed, in comparison to previous
approaches of this kind. A selection of large case studies are implemented the techniques and evaluate,
which include some infinite-state probabilistic real time models, demonstrating improvements over existing
tools in several cases. The capacity of metro and access networks are extended the reach and split ratio of
the conventional Long - Reach Passive Optical Networks (LR-PONs). The efficient solutions of LR-PONs
are appeared in feeder distances around 100km and high split ratios up to 1000-way . Among many
existing approaches, one of the most effective options to improve network performance in LR-PONs are the
multi-thread based dynamic bandwidth allocation (DBA) scheme where several bandwidth allocation
processes are performed in parallel is considered. Without proper intercommunication between the
overlapped threads, multi-thread DBA may lose efficiency and even perform worse than the conventional
single thread algorithm. Real Time Probabilistic Systems are used to evaluate a typical PON systems
performance. This approach is more convenient, flexible, and lower cost than the former simulation method,
which do not need develop special hardware and software tools. Moreover, how changes in performance
depend on changes in the particular modes can be easily analysis by supplying ranges for parameter values.
The proposed algorithm with traditional DBA is compared, and shows its advantage on average packet
delay. The key parameters of the algorithm are analysed and optimized, such as initiating and tuning
multiple threads, inter -thread scheduling, and fairness among users. The algorithms advantage in
numerical results are decreased the average packet delay and improve network throughput under varying
offered loads.
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.
Spectral Efficient Blind Channel Estimation Technique for MIMO-OFDM Communica...IJAAS Team
With emerge of increasing research in the domain of future wireless communications, massive MIMO (multiple inputs multiple outputs) attracted most of researchers interests. Massive MIMO is high-speed wireless communication standards. A channel estimation technology plays the essential role in the MIMO systems. Efficient channel estimation leads to spectral efficient wireless communications. The critics of Inter-Symbol Interference (ISI) are the challenging tasks while designing the channel estimation methods. To mitigate the challenges of ISI, we proposed the novel blind channel estimation method which based on Independent component analysis (ICA) in this paper. Proposed channel estimation it works for both blind interference cancellation and ISI cancellation. The proposed Hybrid ICA (HICA) method depends on pulse shape filtering and ambiguity removal to improve the spectral efficiency and reliability for MIMO communications. The Kurtosis operation is used to measure the complex data at first to estimate the common signals. Then we exploited the advantages of 3rd and 4th order Higher Order Statistics (HOS) to priorities the common signals during the channel estimation. In this paper, we present the detailed design and evaluation of HICA blind channel estimation method. We showed the simulation results of HICA against the state-of-art techniques for channel estimation using BER, MSE, and PAPR.
ROUTING IN OPTICAL MESH NETWORKS-A QOS PERSPECTIVEijasuc
Wireless Ad-Hoc Mesh Networks are characterized by static nodes connected in a mesh topology. A routing
protocol discovers and maintains the route for successful transmission of data in a network. The routing
protocol should also provide load balancing and fault tolerance for improved network performance. In
Free Space Optical networks (FSO) line of sight (LOS) should be maintained between the two
communicating nodes. In a multihop scenario maintaining LOS during routing is a challenge. In this paper
we propose a routing protocol Quality of Service-Directional Routing Protocol (QDRP) - which assures a
certain level of performance to a data flow in terms of delay and implemented on FSO MANET. Through
simulations it is observed that QDRP chooses the path with the least delay and performs satisfactorily
under varying node densities and transmission rates achieving end to end delay of .14 s and packet delivery
percentage of 96% when simulated for an area of 1300 m *1300 m for 100 nodes. This work explores the
potential of the proposed routing protocol for free space optical mesh networks. QDRP is compared with
ORRP (Orthogonal Rendezvous Routing Protocol) and AODV (Ad-Hoc on Demand Distance Vector), a
reactive protocol which is also implemented in free space optical environment. We support our conclusions
that QDRP gains in terms of packet delivery percentage, end to end delay and goodput.
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
Congestion Control in Manets Using Hybrid Routing ProtocolIOSR Journals
As the network size increases the probability of congestion occurrence at nodes increases. This is
because of the event driven nature of ad hoc networks that leads to unpredictable network load. As a result
congestion may occur at the nodes which receive more data than that can be forwarded and cause packet losses.
In this paper we propose a hybrid scheme that attempts to avoid packet loss due to congestion as well as reduce
end to end delay in delivering data packets by combining two protocols- Destination sequenced distance vector
routing (DSDV), which is a table driven or proactive protocol and Improved Ad-hoc on demand vector routing
(IAODV) which is an on-demand or reactive protocol that reduces packet loss due to congestion. The strategy
adopted is use DSDV for path selection and if congestion occurs than switch over to IAODV. The routing
performance of this model is then compared with IAODV and DSDV in terms of end to end delay, throughput
and packet delivery fraction
Mobility and Propagation Models in Multi-hop Cognitive Radio Networksszhb
Cognitive radio networks allow unlicensed
(secondary) users to opportunistically utilize the idle
resource of a licensed network for communication
without affecting the quality of service being offered to
the primary or licensed users. This paper investigates
the effect of mobility on performance of multi-hop
cognitive radio network under various propagation
models. MPEG4 video; a bandwidth intensive traffic, is
tested over these network conditions for secondary
users and results are validated using NS2 simulations.
Performance metrics used for evaluation include
throughput, delay variations etc.
Routing in Cognitive Radio Networks - A SurveyIJERA Editor
Cognitive Radio Networks (CRNs) have been emerged as a revolutionary solution to migrate the spectrum
scarcity problem in wireless networks. Due to increasing demand for additional spectrum resources, CRNs have
been receiving significant research to solve issues related with spectrum underutilization. This technology
brings efficient spectrum usage and effective interference avoidance, and also brings new challenges to routing
in multi-hop Cognitive Radio Networks. In CRN, unlicensed users or secondary users are able to use
underutilized licensed channels, but they have to leave the channel if any interference is caused to the primary or
licensed users. So CR technology allows sharing of licensed spectrum band in opportunistic and non-interfering
manner. Different routing protocols have been proposed recently based on different design goals under different
assumptions.
Novel Position Estimation using Differential Timing Information for Asynchron...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
Bit Error Rate Analysis in WiMAX Communication at Vehicular Speeds using mod...IJMER
At high vehicular speeds, rapid changes in surrounding environments, cause severe fading at
the receiver, resulting a drastic fall in throughput and unless any proactive measure is taken to combat
this problem, throughput becomes insufficient to support many applications, particularly those with
multimedia contents. Bit Error Rate (BER) estimation is an integral part of any proactive measure and
recent studies suggest that Nakagami-m model performs better for modelling channel fading in wireless
communications at high vehicular speeds. No work has been reported in literature that estimates BER
at high vehicular speeds in WiMAX communication using Nakagami-m model. In this thesis, we develop
and present an analytical model to estimate BER in WiMAX at vehicular speeds using Nakagami-m
fading model. The proposed model is adaptive and can be used with resource management schemes
designed for fixed, nomadic, and mobile WiMAX communications.
An improved ant colony optimization algorithm for wire optimizationjournalBEEI
Wire optimization has become one of the greatest challenges in today’s circuit design. This paper presents a method for wire optimization in circuit routing using an improved ant colony optimization with Steiner nodes (ACOSN) algorithm. Circuit delay and power dissipation are primarily affected by the length of the routed wire. Thus, the main goal of this proposed algorithm is to find the shortest route from one point to another using an algorithm that relies on the artificial behavior of ants. The algorithm is implemented in the JAVA programming language. The proposed ACOSN algorithm is compared with the conventional ant colony optimization (ACO) algorithm in terms of efficiency and routing performance when applied to three types of circuits: emitter-coupled logic, 741 output and a cascode amplifier. The performance of the proposed method is analyzed based on circuit information such as total wire routing, total number of nets, total wire reduction, terminals per net and total terminals. From the simulation analysis, it is shown that the proposed ACOSN algorithm gives the most benefit to complex circuits, where it successfully reduces the wire length by 21.52% for a cascode amplifier circuit, 14.49% for a 741 output circuit, and 10.43% for emitter-coupled logic circuit.
Fault Tolerant Congestion based Algorithms in OBS NetworkCSCJournals
In Optical Burst Switched networks, each light path carry huge amount of traffic, path failures may damage the user application. Hence fault-tolerance becomes an important issue on these networks. Blocking probability is a key index of quality of service in Optical Burst Switched (OBS) network. The Erlang formula has been used extensively in the traffic engineering of optical communication to calculate the blocking probability. The paper revisits burst contention resolution problems in OBS networks. When the network is overloaded, no contention resolution scheme would effectively avoid the collision and cause blocking. It is important to first decide, a good routing algorithm and then to choose a wavelength assignment scheme. In this paper we have developed two algorithms, Fault Tolerant Optimized Blocking Algorithm (FTOBA) and Fault Tolerant Least Congestion Algorithm (FTLCA) and then compare the performance of these algorithms on the basis of blocking probability. These algorithms are based upon the congestion on path in OBS network and based on the simulation results, we shows that the reliable and fault tolerant routing algorithms reduces the blocking probability.
Implementation of a bpsk modulation based cognitive radio system using the en...csandit
We present in this work an energy detection algorit
hm, based on spectral power estimation, in
the context of cognitive radio. The algorithm is ba
sed on the Neyman-Pearson test where the
robustness of the appropriate spectral bands identi
fication, is based, at one hand, on the
‘judicious’ choice of the probability of detection
(P
D
) and false alarm probability (P
F
). First, we
accomplish a comparative study between two techniqu
es for estimation of PSD (Power Spectral
Density): the periodogram and Welch methods. Also,
the interest is focused on the choice of the
optimal duration of observation where we can state
that this latter one should be inversely
proportional to the level of the SNR of the transmi
tted signal to be sensed. The developed
algorithm is applied in the context of cognitive ra
dio. The algorithm aims to identify the free
spectral bands representing, reserved for the prima
ry user, of the signal carrying information,
issued from an ASCII encoding alphanumeric message
and utilizing the BPSK modulation,
transmitted through an AWGN (Added White Gaussian N
oise) channel. The algorithm succeeds
in identifying the free spectral bands even for low
SNR levels (e.g. to -2 dB) and allocate them
to the informative signal representing the secondar
y user.
Estimation of bit error rate in 2×2 and 4×4 multi-input multioutput-orthogon...IJECEIAES
Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-tonoise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the i th transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track timevarying channels rapidly.
ROUTING IN OPTICAL MESH NETWORKS-A QOS PERSPECTIVEijasuc
Wireless Ad-Hoc Mesh Networks are characterized by static nodes connected in a mesh topology. A routing
protocol discovers and maintains the route for successful transmission of data in a network. The routing
protocol should also provide load balancing and fault tolerance for improved network performance. In
Free Space Optical networks (FSO) line of sight (LOS) should be maintained between the two
communicating nodes. In a multihop scenario maintaining LOS during routing is a challenge. In this paper
we propose a routing protocol Quality of Service-Directional Routing Protocol (QDRP) - which assures a
certain level of performance to a data flow in terms of delay and implemented on FSO MANET. Through
simulations it is observed that QDRP chooses the path with the least delay and performs satisfactorily
under varying node densities and transmission rates achieving end to end delay of .14 s and packet delivery
percentage of 96% when simulated for an area of 1300 m *1300 m for 100 nodes. This work explores the
potential of the proposed routing protocol for free space optical mesh networks. QDRP is compared with
ORRP (Orthogonal Rendezvous Routing Protocol) and AODV (Ad-Hoc on Demand Distance Vector), a
reactive protocol which is also implemented in free space optical environment. We support our conclusions
that QDRP gains in terms of packet delivery percentage, end to end delay and goodput.
A SURVEY ON OPTIMIZATION BASED SPECTRUM SENSING TECHNIQUES TO REDUCE ISI AND ...IJNSA Journal
Cognitive radio is emerging technologies in OFDM based wireless systems which are very important for spectrum sensing. By using cognitive radio (CR) high data can be transferred with low bit error rate. The key idea of OFDM is to split the total transmission bandwidth into the subcarriers which further reduce the intersymbol interference (ISI) and peak to average power ratio(PAPR) in the signal. There are many optimization based spectrum sensing techniques are existing for efficient sensing purpose but each has its own advantages and disadvantages. This leads to start the comprehensive study for reducing PAPR and ISI(Intersymbol interference) in terms of FPGA based partial configuration. In the first part of review OFDM characteristics of the signal has compared with several optimizations based ISI reduction techniques. The second part is to compare the various spectrum sensing techniques in cognitive radio engine and its application in FPGA.
Congestion Control in Manets Using Hybrid Routing ProtocolIOSR Journals
As the network size increases the probability of congestion occurrence at nodes increases. This is
because of the event driven nature of ad hoc networks that leads to unpredictable network load. As a result
congestion may occur at the nodes which receive more data than that can be forwarded and cause packet losses.
In this paper we propose a hybrid scheme that attempts to avoid packet loss due to congestion as well as reduce
end to end delay in delivering data packets by combining two protocols- Destination sequenced distance vector
routing (DSDV), which is a table driven or proactive protocol and Improved Ad-hoc on demand vector routing
(IAODV) which is an on-demand or reactive protocol that reduces packet loss due to congestion. The strategy
adopted is use DSDV for path selection and if congestion occurs than switch over to IAODV. The routing
performance of this model is then compared with IAODV and DSDV in terms of end to end delay, throughput
and packet delivery fraction
Mobility and Propagation Models in Multi-hop Cognitive Radio Networksszhb
Cognitive radio networks allow unlicensed
(secondary) users to opportunistically utilize the idle
resource of a licensed network for communication
without affecting the quality of service being offered to
the primary or licensed users. This paper investigates
the effect of mobility on performance of multi-hop
cognitive radio network under various propagation
models. MPEG4 video; a bandwidth intensive traffic, is
tested over these network conditions for secondary
users and results are validated using NS2 simulations.
Performance metrics used for evaluation include
throughput, delay variations etc.
Routing in Cognitive Radio Networks - A SurveyIJERA Editor
Cognitive Radio Networks (CRNs) have been emerged as a revolutionary solution to migrate the spectrum
scarcity problem in wireless networks. Due to increasing demand for additional spectrum resources, CRNs have
been receiving significant research to solve issues related with spectrum underutilization. This technology
brings efficient spectrum usage and effective interference avoidance, and also brings new challenges to routing
in multi-hop Cognitive Radio Networks. In CRN, unlicensed users or secondary users are able to use
underutilized licensed channels, but they have to leave the channel if any interference is caused to the primary or
licensed users. So CR technology allows sharing of licensed spectrum band in opportunistic and non-interfering
manner. Different routing protocols have been proposed recently based on different design goals under different
assumptions.
Novel Position Estimation using Differential Timing Information for Asynchron...IJCNCJournal
Positioning techniques have been a common objective since the early development of wireless networks. However, current positioning methods in cellular networks, for instance, are still primarily focused on the use of the Global Navigation Satellite System (GNSS), which has several limitations, like high power drainage and failure in indoor scenarios. This study introduces a novel approach employing standard LTE signaling in order to provide high accuracy positioning estimation. The proposed technique is designed in analogy to the human sound localization system, eliminating the need of having information from three spatially diverse Base Stations (BSs). This is inspired by the perfect human 3D sound localization with two ears. A field study is carried out in a dense urban city to verify the accuracy of the proposed technique, with more than 20 thousand measurement samples collected. The achieved positioning accuracy is meeting the latest Federal Communications Commission (FCC) requirements in the planner dimension.
Bit Error Rate Analysis in WiMAX Communication at Vehicular Speeds using mod...IJMER
At high vehicular speeds, rapid changes in surrounding environments, cause severe fading at
the receiver, resulting a drastic fall in throughput and unless any proactive measure is taken to combat
this problem, throughput becomes insufficient to support many applications, particularly those with
multimedia contents. Bit Error Rate (BER) estimation is an integral part of any proactive measure and
recent studies suggest that Nakagami-m model performs better for modelling channel fading in wireless
communications at high vehicular speeds. No work has been reported in literature that estimates BER
at high vehicular speeds in WiMAX communication using Nakagami-m model. In this thesis, we develop
and present an analytical model to estimate BER in WiMAX at vehicular speeds using Nakagami-m
fading model. The proposed model is adaptive and can be used with resource management schemes
designed for fixed, nomadic, and mobile WiMAX communications.
An improved ant colony optimization algorithm for wire optimizationjournalBEEI
Wire optimization has become one of the greatest challenges in today’s circuit design. This paper presents a method for wire optimization in circuit routing using an improved ant colony optimization with Steiner nodes (ACOSN) algorithm. Circuit delay and power dissipation are primarily affected by the length of the routed wire. Thus, the main goal of this proposed algorithm is to find the shortest route from one point to another using an algorithm that relies on the artificial behavior of ants. The algorithm is implemented in the JAVA programming language. The proposed ACOSN algorithm is compared with the conventional ant colony optimization (ACO) algorithm in terms of efficiency and routing performance when applied to three types of circuits: emitter-coupled logic, 741 output and a cascode amplifier. The performance of the proposed method is analyzed based on circuit information such as total wire routing, total number of nets, total wire reduction, terminals per net and total terminals. From the simulation analysis, it is shown that the proposed ACOSN algorithm gives the most benefit to complex circuits, where it successfully reduces the wire length by 21.52% for a cascode amplifier circuit, 14.49% for a 741 output circuit, and 10.43% for emitter-coupled logic circuit.
Fault Tolerant Congestion based Algorithms in OBS NetworkCSCJournals
In Optical Burst Switched networks, each light path carry huge amount of traffic, path failures may damage the user application. Hence fault-tolerance becomes an important issue on these networks. Blocking probability is a key index of quality of service in Optical Burst Switched (OBS) network. The Erlang formula has been used extensively in the traffic engineering of optical communication to calculate the blocking probability. The paper revisits burst contention resolution problems in OBS networks. When the network is overloaded, no contention resolution scheme would effectively avoid the collision and cause blocking. It is important to first decide, a good routing algorithm and then to choose a wavelength assignment scheme. In this paper we have developed two algorithms, Fault Tolerant Optimized Blocking Algorithm (FTOBA) and Fault Tolerant Least Congestion Algorithm (FTLCA) and then compare the performance of these algorithms on the basis of blocking probability. These algorithms are based upon the congestion on path in OBS network and based on the simulation results, we shows that the reliable and fault tolerant routing algorithms reduces the blocking probability.
Implementation of a bpsk modulation based cognitive radio system using the en...csandit
We present in this work an energy detection algorit
hm, based on spectral power estimation, in
the context of cognitive radio. The algorithm is ba
sed on the Neyman-Pearson test where the
robustness of the appropriate spectral bands identi
fication, is based, at one hand, on the
‘judicious’ choice of the probability of detection
(P
D
) and false alarm probability (P
F
). First, we
accomplish a comparative study between two techniqu
es for estimation of PSD (Power Spectral
Density): the periodogram and Welch methods. Also,
the interest is focused on the choice of the
optimal duration of observation where we can state
that this latter one should be inversely
proportional to the level of the SNR of the transmi
tted signal to be sensed. The developed
algorithm is applied in the context of cognitive ra
dio. The algorithm aims to identify the free
spectral bands representing, reserved for the prima
ry user, of the signal carrying information,
issued from an ASCII encoding alphanumeric message
and utilizing the BPSK modulation,
transmitted through an AWGN (Added White Gaussian N
oise) channel. The algorithm succeeds
in identifying the free spectral bands even for low
SNR levels (e.g. to -2 dB) and allocate them
to the informative signal representing the secondar
y user.
Estimation of bit error rate in 2×2 and 4×4 multi-input multioutput-orthogon...IJECEIAES
Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems with multiple input antennas and multiple output antennas in dynamic environments face the challenge of channel estimation. To overcome this challenge and to improve the performance and signal-tonoise ratio, in this paper we used the Kalman filter for the correct estimation of the signal in dynamic environments. To obtain the original signal at the receiver end bit error rate factor plays a major role. If the signal to noise ratio is high and the bit error rate is low then signal strength is high, the signal received at the receiver end is almost similar to the i th transmitted signal. The dynamic tracking characteristic of Kalman filter is used to establish a dynamic space-time codeword and a collection of orthogonal pilot sequences to prevent interference among transmissions in this paper. Using the simulation, the Kalman filter method can be compared to the other channel estimation method presented in this paper that can track timevarying channels rapidly.
Channel Estimation in MIMO OFDM Systems with Tapped Delay Line ModelIJCNCJournal
The continuous increase in the user demands fornew-generation communication systems, is making the wireless channel more complex and challenging for estimation, developing a simulation model for the channel,and evaluating the performance of different MIMO systems. In this work, a simulation model for multipath fading channels in wireless communication is performed. The model includes a selection of typical Tapped-Delay-Line channel models that can be implemented to reproduce the effects of representative channel distortion and interference. Based on the simulation results, the proposed method exhibits accurate channel estimation performance for frequency-selective fading channels. The proposed work employed LS, MMSE, and ML methods for channel estimation, using 16 and 32 pilots and fixed pilot locations in each frame. Results are obtained for 4x4, 8x8, 16x16, 16x8, and 16x4 MIMO systems and tapped delay line systems.
Channel Estimation in MIMO OFDM Systems with Tapped Delay Line ModelIJCNCJournal
The continuous increase in the user demands fornew-generation communication systems, is making the wireless channel more complex and challenging for estimation, developing a simulation model for the channel,and evaluating the performance of different MIMO systems. In this work, a simulation model for multipath fading channels in wireless communication is performed. The model includes a selection of typical Tapped-Delay-Line channel models that can be implemented to reproduce the effects of representative channel distortion and interference. Based on the simulation results, the proposed method exhibits accurate channel estimation performance for frequency-selective fading channels. The proposed work employed LS, MMSE, and ML methods for channel estimation, using 16 and 32 pilots and fixed pilot locations in each frame. Results are obtained for 4x4, 8x8, 16x16, 16x8, and 16x4 MIMO systems and tapped delay line systems.
Channel Estimation Techniques in MIMO-OFDM LTE SystemsCauses and Effects of C...IJERA Editor
There is an increasing demand for high data transmission rates with the evolution of the very large scale integration (VLSI) technology. The multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems are used to fulfill these requirements because of their unique properties such as high spectral efficiency, high data rate and resistance towards multipath propagation. MIMO-OFDM systems are finding their applications in the modern wireless communication systems like IEEE 802.11n, 4G and LTE. They also offer reliable communication with the increased coverage area. The bottleneck to the MIMO-OFDM systems is the estimation of the channel state information (CSI). This can be estimated with the help of any one of the Training Based, Semiblind and Blind Channel estimation algorithms. This paper presents various channel estimation algorithms, optimization techniques and their effective utilization in MIMO-OFDM for modern wireless LTE systems.
A SEMI BLIND CHANNEL ESTIMATION METHOD BASED ON HYBRID NEURAL NETWORKS FOR UP...ijwmn
The paper describes how to improve channel estimation in Single Carrier Frequency Division Multiple
Access (SC-FDMA) system, using a Hybrid Artificial Neural Networks (HANN). The 3rd Generation
Partnership Project (3GPP) standards for uplink Long Term Evolution Advanced (LTE-A) uses pilot based
channel estimation technique. This kind of channel estimation method suffers from a considerable loss
ofbitrate due to pilot insertion; all data frame sent contains reference signal. The HANN converts data
aided channel estimator to semi blind channel estimator. To increase convergence speed, HANN uses some
channel propagation Fuzzy Rules to initialize Neural Network parameters before learning instead of a
random initialization, so its learning phase ismore rapidly compared to classic ANN.HANN allows more
bandwidth efficient and less complexity. Simulation results show that HANN has better computational
efficiency than the Minimum Mean Square Error (MMSE) estimator and has faster convergence than
classic Neural Networks estimators.
ESTIMATION OF SYMBOL TIMING AND CARRIER FREQUENCY OFFSET USING SYNCHRONIZATI...Michael George
OFDM/OQAM is preferred as multicarrier system which operates over a multipath channel. By using the multipath channel the signal-to-noise ratio. In earlier, sub carriers are used to transmit the signals. Nowadays, FFT and DFT are used for transmitting the signals based upon the bit values. AWGN is a channel used to identify the noise produced at the output by adding the noise in the blind signal. By reducing subcarriers the noise and timing are reduced. FFT bit value was increased which provides better performance. In the multicarrier system, the error and noise was reduced by increasing the bit value.
Reducing the Peak to Average Power Ratio of Mimo-Ofdm SystemsIJCNCJournal
In this paper, we proposed a particle swarm optimization (PSO) based partial transmit sequence (PTS)
technique in order to achieve the lowest Peak-to-Average Power Ratio(PAPR) in Multiple Input Multiple
Output- Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Our approach consist of
applying the PSO based PTS on each antenna of the system in order to find the optimal phase factors,
which is a straightforward method to get the minimum PAPR in such a system. The simulation results
demonstrate that the PSO based PTS algorithm when applied to MIMO-OFDM systems with a wide range
of phase factors, tends to give a high performance. In addition, there is no need to increase the number of
particles of the PSO algorithm to enhance the performance of the system. As a result of this, the complexity
of finding the minimum PAPR is kept at a reasonable level.
EFFICIENT ANALYSIS OF THE ERGODIC CAPACITY OF COOPERATIVE NON-REGENERATIVE RE...ijwmn
In this paper, we proposed a novel efficient method of analyzing the ergodic channel capacity of the
cooperative amplify-and-forward (CAF) relay system. This is accomplished by employing a very tight
approximate moment generating function (MGF) of end-to-end signal-to-noise ratio of 2-hop multi-relay
system, which is In this paper, we proposed a novel efficient method of analyzing the ergodic channel
capacity of the cooperative amplify-and-forward (CAF) relay system. This is accomplished by employing a
very tight approximate moment applicable to myriad of fading environments including mixed and
composite fading channels. Three distinct adaptive source transmission policies were considered in our
analysis namely: (i) constant power with optimal rate adaptation (ORA); (ii) optimal joint power and rate
adaptation (OPRA); and (iii) fixed rate with truncated channel inversion (TCIFR). The proposed frame
work based on the novel approximate MGF method is sufficiently general to encapsulate all types of fading
environments (especially for the analysis of the mixed fading case)and provides significant advantage to
model wireless system for mixed and composite fading channel. In addition to simplifying computation
complexity of ergodic capacity for CAF relaying schemes treated in literature, we also derive closed form
expressions for the above three adaptive source transmission policies under Nakagami-m fading with i.n.d
statistics. The accuracy of our proposed method has been validated with existing MGF expressions that are
readily available for specific fading environments in terms of bounds, and via Monte Carlo simulations.
EFFICIENT ANALYSIS OF THE ERGODIC CAPACITY OF COOPERATIVE NON-REGENERATIVE RE...ijwmn
In this paper, we proposed a novel efficient method of analyzing the ergodic channel capacity of the
cooperative amplify-and-forward (CAF) relay system. This is accomplished by employing a very tight
approximate moment generating function (MGF) of end-to-end signal-to-noise ratio of 2-hop multi-relay
system, which is In this paper, we proposed a novel efficient method of analyzing the ergodic channel
capacity of the cooperative amplify-and-forward (CAF) relay system. This is accomplished by employing a
very tight approximate moment applicable to myriad of fading environments including mixed and
composite fading channels. Three distinct adaptive source transmission policies were considered in our
analysis namely: (i) constant power with optimal rate adaptation (ORA); (ii) optimal joint power and rate
adaptation (OPRA); and (iii) fixed rate with truncated channel inversion (TCIFR). The proposed frame
work based on the novel approximate MGF method is sufficiently general to encapsulate all types of fading
environments (especially for the analysis of the mixed fading case)and provides significant advantage to
model wireless system for mixed and composite fading channel. In addition to simplifying computation
complexity of ergodic capacity for CAF relaying schemes treated in literature, we also derive closed form
expressions for the above three adaptive source transmission policies under Nakagami-m fading with i.n.d
statistics. The accuracy of our proposed method has been validated with existing MGF expressions that are
readily available for specific fading environments in terms of bounds, and via Monte Carlo simulations.
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
PERFORMANCE ANALYSIS OF BARKER CODE BASED ON THEIR CORRELATION PROPERTY IN MU...ijistjournal
Spread-spectrum communication, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. They have very beneficial and tempting features, like Antijam, Security, and Multiple accesses. This thesis basically deals with the pseudo codes used in spread spectrum communication system. The cross-correlation and auto-correlation properties of the long Barker Code are analyzed. It has been seen that the length of the code, autocorrelation and cross-correlation properties can help us to determine the best suitable code for any particular communication environment. We have tried to find out the code with suitable auto-correlation properties along with low cross-correlation values. Barker code has good auto-correlation properties and we have found the pairs with the low cross- correlation so that they can be used in multi-user environment.
Performance evaluation of high mobility OFDM channel estimation techniques IJECEIAES
In wireless communication, Orthogonal Frequency Division Multiplexing (OFDM) has been adopted due to its robustness to multipath fading and high data rate transmissions. At the other hand, the performance of OFDM systems severely degraded due to multi-path fading and Doppler frequency shifts in mobile systems, which causes inter-carrier-interference (ICI). Thus, Estimation of channel parameters is required at the receiver using a pre designed estimator where pilot tones are inserted in each OFDM symbol. In this paper, a random pilot data are generated and inserted in each OFDM symbol at equally spaced locations. The performance test of Least Square (LS) and Linear Minimum Mean Square (LMMSE) estimation methods are proposed with Discrete Fourier Transform (DFT) based on both LS and LMMSE, where different ITU channel models are considered in order to compare their performance for data transmission in high mobile systems with different Doppler frequencies exceeds 200 Hz and minimal number of pilots.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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subcarriers, transmitted in parallel, divide the available transmission bandwidth. The separation of the
subcarriers is theoretically optimal such that there is a very compact spectral utilization.
Figure 1. OFDM block diagram
Channel Estimation is the process of characterizing the effect of the physical medium on the input
sequence. It is an important and necessary function for wireless systems. Even with a limited knowledge of
the wireless channel properties, a receiver can gain insight into the data sent over by the transmitter. The
main goal of Channel Estimation is to measure the effects of the channel on known or partially known set of
transmissions. Orthogonal Frequency division multiplexing (OFDM) Systems are especially suited for
channel estimation. The subcarriers are closely spaced. While the system is generally used in high speed
applications that are capable of computing channel estimates with minimum delay.
Although ISI can be avoided, via the use of cyclic prefix in OFDM modulation, the phase and gain
of each sub channel is needed for coherent symbol detection. An estimate of these parameters can be
obtained with pilot/training symbols, at the expense of bandwidth. Blind channel estimation methods avoid
the use of pilot symbols, which makes them good candidates for achieving high spectral-efficiency. Existing
blind channel estimation methods for OFDM systems can be classified as Statistical & Deterministic.
The statistical methods explore the cyclostationarity that the cyclic prefix induces to the transmitted
signal. They recover the channel using cyclic statistics of the received signal, or subspace decomposition of
the correlation matrix of the pre-DFT received blocks. The deterministic methods process the post DFT
received blocks, and exploit the finite alphabet property of the information bearing symbols. Maximum
likelihood and iterative Bayesian methods are two examples. Taking into account, specific properties of M-
PSK or QAM signals, while utilizing an exhaustive search. In comparison to the statistical methods, the
deterministic ones converge much faster, however, they involve high complexity, which becomes even
higher as the constellation order increases.
The work for the Blind channel detection and estimation started by using fast blind trellis search
techniques [1] over linear distortive .This relied on the principle of finding the best possible channel fit for a
noisy channel output sequence but this technique suffered fro a shortcoming of non instrument able due to
the exponential growth in its complexity with data but has the feature of fast convergence. The proposed
algorithm analyzed and compared between trellis decoding and channel estimation algorithm using Recursive
least squares or LMS. Further refinements were made by [2] which analyzed the blind identification by
determining FIR parameters from systems output which were excited from discreet alphabets.
This approach utilized the discrete alphabet property of applied inputs and has an advantage of
robustness to noise structure but sensitive to noise level. It yielded exact impulse response for noise less
systems. This work on blind channel estimation also concluded the dependence of convergence time on
system order and size of input alphabet. Also the proposed algorithm showed insensitivity to noise
probability distribution. In [3] Maximum likelihood approach was used in combination with self adaptive
technique and this new technique overcome the problem of estimating the most likely state sequence of
discreet time finite state Markov process with known parameters. This ML scheme is for both input
sequences and parameters for estimation of state sequences. This work had also the feature of asymptotically
differing from the conventional MLSE with known coefficients having low probability.
Further research in this direction was estimation using blind trellis search techniques [4]. It covered
the ML estimation fro data transmitted over unknown linear channels without any prior requirement for the
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initial sequence as required by earlier conventional methods. Simultaneously both channel and data are
estimated by obtaining least square channel estimate between received and data sequence and henceforth
selection of the optimal. Complexity of this algorithm was higher than the viterbi algorithm for more than
two alphabets. It also led to the convergence over a wide range of SNR within 100 symbols. A step ahead
was taken in this direction by Blind estimation of multiple co channel digital signals by utilizing antenna
array [5].This work had an extra feature of separating and estimating multiple co channel digital signals. It
utilized the temporal structure of digital signals for determining array response and bit sequence for each
signal.
Another novel approach for blind identification of Multichannel systems was taking FIR filters
which utilized the concept of orthogonality between signals and noise sub space for creating a quadratic form
which after minimization yields desired estimates. This work had some certain advantages over the previous
works and technologies proposed as it being computationally efficient is have no apriori requirement of
emitted symbol correlation. This method has advantage over the previous that it leads to precise estimates of
channel coefficients for short data frame and used for fast convergence. Another approach in the same
direction was made by [7] for synchronous co channel digital signals using antenna array. This work takes
into account ILSE and ILSP and concludes that ILSE converges to a fixed point with finite number of
iterations. It can be also utilized for asynchronous transmission and multipath channels with large delay
spread.
Besides exploring the methods for blind separation this work [8] also highlighted the semi blind
methods for estimation of channels. Being a subspace based approach it utilized the cyclic prefix and has
advantage of maintaining the classical OFDM based on CP insertion which makes it applicable to all
standard multicarrier systems .It is very precise in cases where channel frequency response has no zeros
located on a subcarrier. Another approach [9] of Blind channel estimation was also followed in the same year
which utilized the concept of Cyclostationarity as an alternative to fractional sampling for blind channel
estimation in OFDM systems. Here even channels with equi spaced unit circle zeros are identifiable in
presence of any non zero CP .Also it highlighted that the impulse response shortening varies with channel
and can be altered by changing the shortening parameters.
All the previous works assumed a perfect Channel state Information (CSI) to extract symbol
estimates which resulted in a loss of performance which was not optimal hence an iterative Maximum
likelihood sequential estimation was used [10] which used an EM (Expectation maximization) approach
which converges rapidly for quasi static and non static fading channels while performance was similar to NL
approach with perfect CSI. In the further course of time the ML approach of detection and decoding was
extended to various Interference conditions [11] and information transfer from STC detector to an error
correcting channel decoder. All the previous works assumed the channels to be stochastic but this work make
itself distinct by conclusion of a deterministic Channel model which has advantages in receiver design by
developing interference resistant algorithms.
As mentioned above the approach of Space time codes was analyzed using the Trained, Blind and
semi blind detection schemes [12]. It compared and considered three estimation and detection schemes and
obtained a new blind scheme without pilot symbol transmission for channel estimation. Assuming an I.I.D
Gaussian random variable with zero mean and unit variance it concluded that blind detector is worse than
semi blind as only one symbol was used for extracting information but for large values of block and small
SNR blind detector performed better. Also BER of semi blind was found to be better then trained detector.
Further taking forward the approach based on semi blind channel Identification and equalization [13] by the
use of sparsity, maximum delay spread and apriori statistical information imposed by channel I addition with
the pilot, cyclic prefix and finite alphabet constraints of transmitter required to reduce number of pilots
needed fro channel and data recovery receiver utilizes the pilots for estimation.
Focusing more towards Blind estimation schemes low complexity Blind frequency offset estimation
for OFDM over ISI channels [14].Prior to this work all CFO estimation techniques rely on assumption of CP
greater than the channel. This work has the relevance due to its feature of low complexity and channel
utilization by decreasing the length of CP. Another approach of Blind and semi blind channel estimation[15]
explored the idea of sub space based estimation .It utilized the concept of redundancy introduced by cyclic
prefix for channel identification and the best part of this method was that its compatibility with existing
OFDM systems. Also it can work in completely blind approach without any initialization. But this algorithm
has its limitation in terms of incapability to ensure the distinctness of channel estimation until complete noise
subspace is considered.
Another approach for blind channel estimation followed the approach of second order
cyclostationarity statistics [16] which utilized the pre coding and yielding estimates of channel. The
performance of the estimation was independent of the structure of noise but it does require an apriori
information on the upper bound of channel length. Another subspace based approach for blind channel
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estimation by using virtual carriers [17] which can be also used for existing OFDM systems with and without
CP time dispersive channel. As stated in [14] the reduction in the length of CP leads to higher channel
utilization than previous estimators, estimation accuracy and speed of convergence.
This further research on blind channel estimation analyzed the performance over rapidly varying
mobile radio channels.[18] It utilized the ML based approach and avoids the use of second and higher order
statistics to improve convergence .It also used a combination of modulation schemes with which absolute
phase of channel transfer function can be resolved. As done earlier in [16] which made use of pre coding
techniques the further work also aimed at blind channel OFDM estimation using simple linear pre coding[20]
where a simple transformation is applied on each block prior to its entry in OFDM system. This transform
results in a correlation structure on transmitted blocks which are utilized at receiver for channel recovery It
has the feature of fast convergence in addition to simplicity. This also outperforms the training based scheme
used in IEEE 802.11a wireless standards.
In order to analyze and compare the performance of a blind channel with a semi blind channel
scenario the work [21] made the estimates assuming a semi blind channel for channel response of multiuser
antennas for a scalar matrix. For the number of users less than the number of symbols in pilot symbol block,
the single pilot can remove ambiguity .The only requirement for estimation was an upper bound as in [16]for
orders which are obtained from some apriori knowledge of propagation. In the same year another approach
for blind channel estimation in combined sense by data detection and channel estimation via sphere decoding
using ML principle. Over frequency selective fading channels. Here both V-Blast [22] and sphere decoding
were used and can be also used for MIMO OFDM systems for fast fading channels.
In the next few years another work [23] on Blind maximum likelihood detection of orthogonal space
time block codes was performed which concerns the problem of unknown CSI( channel state information on
binary and quaternary PSK constellations. It utilized two approaches SDR (semi definite approach) which
leads to a suboptimal but accurate blind ML detection. By using sphere decoding an exact ML blind detection
algorithm was developed but its was computationally expensive.SDR approach leads to better results than
sphere decoding in worst case. Under I.I.D. Rayleigh distribution stochastic blind ML Orthogonal space time
block codes is equivalent to deterministic Blind ML OSTBC detector.
Another approach for blind channel estimation foe fractionally sampled FIR Channels [24] came
into existence in the same phase which used the concept of residue polynomials for FIR channels. If received
signal is mixed with inverse of transmitted signal the resulting transform renders channel transfer function in
absence of additive noise. For an FIR samples of recovered impulse response must be zero in zeros region of
channel impulse response. It was found to be more efficient than subspace based as it is deterministic and no
requirement of auto correlation of received signal.
Further moving on lines of blind channel estimation the approach was made in the direction of Blind
adaptive [25] estimation. It adopted the principle of zero padding OFDM and blind adaptive algorithm for
finding impulse response of multipath signal. It uses RLS and LMS for obtaining fast convergence rate with
minimum complexity. Both RLS and LMS were used for modification of orthogonal iteration for
determining singular vectors.
The methodology of Blind channel estimation using cyclic prefixed single carrier systems using real
symbol characteristics [26]. The property of second order statistics which were present in the transmitted data
block. It was a simple method using virtual carriers or redundant coding resulting in bandwidth efficiency.
also another distinguishing feature of this approach was reduction in phase ambiguity and which is converted
to symbol ambiguity only. It was generally preferred for SISO but can be extended to SIMO and MIMO. In
the same phase the blind ML detection of OSTBC [27] was performed with emphasis on binary PSK and
QPSK. This work also classified a category called non rotatable OSTBC which were known as UIUTS-
Uniquely identified up to a sign almost certainly with few assumptions. For an Independently distributed
Rayleigh with any number of receiver antennas, a non rotatable OSTBC can be UIUTS with unit probability.
For the case of MIMO OFDM Blind channel estimation [28] by combining the two systems of
MIMO with the OFDM high data rates can be achieved over broadband wireless channels. It integrates and
generalizes the existing subspace methods for blind channel estimation in SISO OFDM to estimation for two
different MIMO OFDM distributions according to the number of transmitting and receiving antennas and can
be also applied to MIMO OFDM without CP regardless of the presence of virtual carrier hence improving the
transmission efficiency.
Further approaches for blind channel estimation for MIMO OFDM systems by the use of non
redundant linear pre coding [29] was analyzed by assuming transmitted symbols to be I.I.D. considering a
subspace based approach. It can be efficiently used where number of receiving antennas is less than
transmitting antenna i.e. MISO using second order statistical analysis. Further extension of the work carried
in [29] was extended in [30] for non redundant pre coding for Semi blind and Blind channel estimation. It
considers the combination of MIMO with block transmission using cyclic prefix which results in high data
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rates and simplification of Channel estimation and equalization .This approach can be used in cases where
number of transmitting are more than receiving and leads to improved MSE and BER by taking large value at
low SNR and very small value at high SNR.
Another approach for MIMO OFDM systems dealt with the subspace based blind channel
estimation with emphasis on short averaging periods[31].It again utilized the orthogonality property of the
noise and signal subspaces by applying a signal noise space decomposition to correlation matrix of received
signal. The work laid emphasis on desired correlation matrix by educing number of OFDM blocks for time
averaging. It has fast convergence by utilizing the frequency correlation between adjacent subcarrier by
subcarrier groupings.
Working on the same lines of Blind channel estimation a need for robust subspace method was felt
for CP MIMO OFDM [32] which used the re modulation on received cannel blocks. The most promising part
of this work was its compatibility with existing as well the future 4G based communication systems. Another
approach of cholesky factorization makes the covariance matrix a special structure for channel estimation for
blind FIR channel identification [33].It is used for small samples and computationally efficient. Blind
Maximum likelihood detection for SIMO systems with low complexity [34] for general constellations
proposed the use of a sequential decoder for exact joint ML solution shown by joint maximum likelihood
channels estimation. As preprocessing required in Chloe sky or QR decomposition there is no such
requirement. As SNR grows the complexity of the algorithm approaches a constant time data length.
Earlier methods of Blind ML detection relied on static nature of channels. The work considers semi
blind ML detection of OSTBC OFDM with single blocks [35]. The main advantage of this methodology was
to accommodate channels with short coherence time considering BPSK or QPSK implementation which
reduced complexity by detection using sub channel grouping. Also in case of identifiability analysis it
ensures a unit probability condition by using less number of pilots and tries to achieve large scale
optimization. For orthogonally coded OFDM MIMO systems the blind channel estimation again used the
SDR (semi definite relaxation) approach [36] as in [23] which uses the certain properties of OSTBC for
estimation of FIR in time domain instead of frequency domain independently of each subcarrier. The Semi
definite approach has advantages over conventional methods.
Further developments in sub space based blind channel estimation for OFDM MIMO systems [37]
which again utilized the orthogonality of noise and signal spaces of correlation matrix of received signal.
Besides orthogonality it also used the reduced time averaging using frequency correlation among adjacent
carriers in MIMO OFDM and also required less number of time samples. Blind channel estimation approach
came to a new platform by adopting an iteration based approach for OFDM systems [38].This approach
differs from the previous approaches in the sense that instead of using pilots it makes primary estimates of
data symbols for each subcarrier then these estimates are applied to optimal MMSE estimation which
requires only one value of time frequency correlation of channel transfer function. As compared to known
decision based Kalman estimation and two pilot aided OFDM schemes this scheme performs better from mid
to high SNR range. And its performance shows small degradation for mismatching.
Blind channel estimation techniques were also analyzed in light of enhanced data recovery using
cyclic prefix by using output symbol and cyclic prefix transmitted over block fading channel. This work[39]
proposed the iterative methods for reduction in complexity. This work also concluded that data recovery is
possible only with output data irrespective of channel zeros locations for which Newton’s method performed
better for all values of SNR for moderately high number of carriers.
Taking into account all the previous works done on all the possible approaches for blind channel
estimation this work [40] aimed at a low complexity blind equalization for OFDM systems with general
constellations. This work also took into account the variation in channel on symbol by symbol basis which is
suited for fast fading channels. This feature of the work made it distinct from all the previous works carried
out for blind channel estimation as it takes in to account the symbol by symbol variation for fat fading
channels. Complexity of this algorithm is low at high SNR and it recovers data from output observations only
without any information of channel statistics hence the most reliable with respect to previous approaches.
3. RESULTS AND ANALYSIS
The work [40] carried considered an OFDM system with, or 64 subcarriers and a CP of length
L=N/4. The uncoded data symbols are modulated using BPSK, 4-QAM, or 16-QAM. The constructed
OFDM signal then passes through a channel of length ,which is assumed to be block fading (i.e., constant
over one OFDM symbol but fades independently from one symbol to another) and whose taps follow an
exponential decay profile, Comparison of the performance of algorithm was made against the following
receivers: the subspace-based blind receiver, the sphere decoding based receiver, a receiver that acquires the
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channel through training with pilots and a priori channel correlation, the ML receiver that acquires data
through exhaustive search. The simulations were averaged over 500 Monte-Carlo runs.
Figure 2 compares the BER performance of algorithm with the aforementioned algorithms for an
OFDM system with subcarriers and BPSK data symbols. In work the blind algorithm outperforms both the
subspace and sphere decoding algorithms and almost matches the performance of the exhaustive search
algorithm for low and high SNR, which confirms the ML nature of the algorithm. Figure 3, which considers
the 4-QAM case, shows the same trends observed for the BPSK case of Figure 2.
Figure 2. BER vs SNR for BPSK OFDM over a Rayleigh channel with and L=3
Figure 3. BER vs SNR for 4-QAM OFDM over a Rayleigh channel with N=16 and L=3
Figure 4 considers a more realistic OFDM symbol length, symbols drawn from a 4-QAM
constellation and allows the SNR to grow to 45 dB. The proposed blind algorithm [40] shows no error, which
is characteristic of non-ML methods. Furthermore, the algorithm beats the training-based method and follows
closely the performance of the perfect channel knowledge case. Figure 5 shows the results with subcarriers
and 16-QAM data symbols for SNR as large as 50 dB.
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Figure 4. BER vs SNR for 4-QAM OFDM over a Rayleigh channel with N=64 and L=15
Figure 5. BER vs SNR for 16-QAM OFDM over a Rayleigh channel with N=64 and L=15
4. CONCLUSION
In this paper several works on blind channel estimation and equalization have been analyzed. The
approaches on blind channel estimation can also be broadly classified into maximum-likelihood (ML)
methods and non-ML methods. The non-ML methods include approaches based on subspace techniques,
second-order statistics, Cholesky factorization, iterative methods, virtual carriers, real signal characteristics
and linear pre coding. The paper analyzed that subspace-based methods have lower complexity, but suffer
from slow convergence as they require many OFDM symbols to provide an accurate estimate of the channel
autocorrelation matrix. Whereas Blind methods based on second-order statistics also require the channel to
be strictly stationary over several OFDM blocks. Methods based on Cholesky factorization and iterative
techniques have demerit in terms of high computational complexity. These features make these algorithms
suitable for block fading with short channel coherence times. Generally suboptimal approximations are
utilized for reduction of the computational complexity of ML-based methods. These methods reduce the
complexity of the exhaustive ML search, they still results in high computational cost. A few ML-based
algorithms allow the channel to change on a symbol-by-symbol basis [40] which has an advantage in terms
of a low-complexity by utilizing the structural features of the partial fast Fourier transform (FFT) matrices
and aimed at complexity minimization in the high SNR regime.
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BIOGRAPHIES OF AUTHORS
Mr.Vivek Kumar Gupta,Research received B.Tech Degree from CCSU university,after that he
received his M.Tech degree from Uttarakhand technical university,Dehradun,Uttarakhand.
Dr.Sandip Vijay is an eminent Professor of ICFAI University and has guided many scholars in
Ph.d,also he has publication in many international journals of repute.