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Intelligent reflecting surface-assisted cognitive radio-inspired rate-splitting multiple access systems

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Digital Communications and Networks 2023年第3期9卷 655-666页

作者： Peixu Liu Gang Jing Hongwu Liu Liang Yang Theodoros A.Tsiftsis School of Information Science and Electrical Engineering Shandong Jiaotong UniversityJinan250357China School of Transportation and Logistics Engineering Shandong Jiaotong UniversityJinan250357China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhai519070China

Intelligent reflecting surface(IRS)has been widely regarded as a promising technology for configuring wireless propagation environments.In this paper,we utilize IRS to assist transmission of a secondary user(SU)in a cognitive radio-inspired rate-splitting multiple access(CR-RSMA)system in which a primary user's(PU's)quality of service(QoS)requirements must be guaranteed.Without introducing intolerable interference to deteriorate the PU's outage performance,the SU conducts rate-splitting to transmit its signal to the base-station through the direct link and IRS reflecting channels.For the IRS-assisted CR-RSMA(IRS-CR-RSMA)scheme,we derive the optimal transmit power allocation,target rate allocation,and successive interference cancellation decoding order to enhance the outage performance of the SU.The closed-form expression for the SU's outage probability achieved by the IRS-CR-RSMA scheme is derived.Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed IRS-CR-RSMA scheme over the CR-RSMA scheme.

关键词： Rate-splitting multiple access(RSMA) Intelligent reflecting surface(IRS) Successive interference cancellation(SIC) cognitive radio

Design of Clustering Techniques in cognitive radio Sensor Networks

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Computer Systems Science & Engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu D.Hemanand V.Amudha S.Sugumaran Department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia Department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia Department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia Department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)interfer-ence.The cognitive radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence time.In this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their connectivity.In this research,multiple random Sec-ondary Users(SUs),and PUs are considered for implementation.Hence,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization algo-rithms.Experimental results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing algorithms.Similarly,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing algorithms.Probability of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB values.The proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary detection.Simulation results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

A hybrid model for energy efficient spectrum sensing in cognitive radio

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International Journal of Intelligent Computing and Cybernetics 2022年第2期15卷 165-183页

作者： Mahua Bhowmik P.Malathi PES’s Modern College of Engineering SPPUPuneIndia D.Y.Patil College of Engineering PuneIndia

Purpose-cognitive radio(CR)plays a very important role in enabling spectral efficiency in wireless communication networks,where the secondary user(SU)allows the licensed primary users(PUs).The purpose of this paper is to develop a prediction model for spectrum sensing in CR.Design/methodology/approach-This paper proposes a hybrid prediction model,called krill-herd whale optimization-based actor critic neural network and hidden Markov model(KHWO-ACNN-HMM).The spectral bands are determined optimally using the proposed hybrid prediction model for allocating the spectrum bands to the PUs.For better sensing,the eigenvalue based on cooperative sensing used in CR.Finally,a hybrid model is designed by hybridizing KHWO-ACNN and HMM to enhance the accuracy of sensing.The predicted results of KHWO-ACNN and HMM are combined by a fusion model,for which a weighted entropy fusion is employed to determine the free spectrum available in CRs.Findings-The performance of the prediction model is evaluated based on metrics,such as probability of detection,probability of false alarm,throughput and sensing time.The proposed spectrum sensing method achieves maximum probability of detection of 0.9696,minimum probability of false alarm rate as 0.78,minimum throughput of 0.0303 and the maximum sensing time of 650.08 s.Research implications-The proposed method is useful in various applications,including authentication applications,wireless medical networks and so on.Originality/value-A hybrid prediction model is introduced for energy efficient spectrum sensing in CR and the performance of the proposed model is evaluated with the existing models.The proposed hybrid model outperformed the other techniques.

关键词： Actor critic neural network cognitive radio Spectrum sensing Optimization Hidden Markov Model

A Double Threshold Energy Detection-Based Neural Network for cognitive radio Networks

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Computer Systems Science & Engineering 2023年第4期45卷 329-342页

作者： Nada M.Elfatih Elmustafa Sayed Ali Maha Abdelhaq Raed Alsaqour Rashid A.Saeed Department of Electrical and Electronics Engineering Red Sea UniversityPort Sudan33311Sudan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics Engineering Sudan University of Science and TechnologyKhartoum11111Sudan

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

关键词： cognitive radio spectrum sensing energy detection double threshold neural network machine learning optimization quality of service

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in cognitive radio Networks

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientradio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the cognitive radio (CR) spectrum, by developing the AmBC.Novel Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in cognitive radio Networks

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Computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed spectrum.However,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed channels.cognitive users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy consumption.Energy consumption can be reduced by predicting the future activity of primary users.However,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory space.Moreover,many of these schemes lack methods to deal with the very busy network environments.In this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary activity.Based on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy efficiency.Simulation results show the effectiveness of the proposed algorithm.

关键词： cognitive radio tri-training duty-cycle intermediate node energy efficiency

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

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Intelligent and Converged Networks 2023年第1期4卷 50-75页

作者： Kagiso Rapetswa Ling Cheng School of Electrical and Information Engineering University of the WitwatersrandJohannesburg 0001South Africa

The adoption of the Fifth Generation(5G)and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment.Although resource-constrained,the cognitive radio(CR)has been identified as a key enabler of distributed 5G and beyond networks due to its cognitive abilities and ability to access idle spectrum opportunistically.Reinforcement learning is well suited to meet the demand for learning in 5G and beyond 5G networks because it does not require the learning agent to have prior information about the environment in which it operates.Intuitively,CRs should be enabled to implement reinforcement learning to efficiently gain opportunistic access to spectrum and co-exist with each other.However,the application of reinforcement learning is straightforward in a single-agent environment and complex and resource intensive in a multi-agent and multi-objective learning environment.In this paper,(1)we present a brief history and overview of reinforcement learning and its limitations;(2)we provide a review of recent multi-agent learning methods proposed and multi-agent learning algorithms applied in cognitive radio(CR)networks;and(3)we further present a novel framework for multi-CR reinforcement learning and conclude with a synopsis of future research directions and recommendations.

关键词： cognitive radio multi-agent reinforcement learning deep reinforcement learning mean field reinforcement learning organic computing

cognitive radio resource allocation based on coupled chaotic genetic algorithm

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Chinese Physics B 2010年第11期19卷 704-711页

作者：俎云霄周杰曾昶畅 School of Electronic Engineering Beijing University of Posts and Telecommunications

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

关键词： cognitive radio chaotic genetic algorithm resource allocation coupled Logistic map

Optimization of cognitive radio System Using Enhanced Firefly Algorithm

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Intelligent Automation & Soft Computing 2023年第9期37卷 3159-3177页

作者： Nitin Mittal Rohit Salgotra Abhishek Sharma Sandeep Kaur SSAskar Mohamed Abouhawwash University Center for Research and Development Chandigarh UniversityMohaliPunjab140413India Faculty of Physics and Applied Computer Science AGH University of Science&TechnologyKrakowPoland Faculty of Information Technology Middle East UniversityAmman11813Jordan Department of Computer Engineering and Applications GLA UniversityMathura281406India Department of Computer Engineering&Technology Guru Nanak Dev UniversityAmritsarPunjab143005India Department of Statistics and Operations Research College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Computational Mathematics Science and EngineeringCollege of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

关键词： Firefly algorithm cognitive radio bit error rate genetic algorithm simulated annealing biogeography-based optimization