Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Cognitive interference decision method for air defense missile fuze based on reinforcement learning

To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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.

Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be managed.Furthermore,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical area.Hence,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable threshold.Hence,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical model.In the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second stage.The main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost function.Finally,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).

Intelligent Covert Communication Design for Cooperative Cognitive Radio Network

Extensive research attentions have been devoted to studying cooperative cognitive radio networks(CCRNs),where secondary users(SU)providing cooperative transmissions can be permitted by primary users(PU)to use spectrum.In order to maximize SU’s utility,SU may transmit its own information during the period of cooperative transmission,which stimulates the use of covert transmission against PU’s monitoring.For this sake,this article reviews the motivations of studying covert communications in CCRN.In particular,three intelligent covert transmission approaches are developed for maximizing SU’s utility in CCRNs,namely,intelligent parasitic covert transmission(IPCT),intelligent jammer aided covert transmission(IJCT)and intelligent reflecting surface assisted covert transmission(IRSC).Further,some raw performance evaluations are discussed,and a range of potential research directions are also provided.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks(CRNs)have become a successful platform in recent years for a diverse range of future systems,in particularly,industrial internet of things(IIoT)applications.In order to provide an efficient connection among IIoT devices,CRNs enhance spectrum utilization by using licensed spectrum.However,the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection.Specifically,the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User(PU)activity and create a robust routing path.This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain.Thus,a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method,namely,Channel Availability Probability.Moreover,a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique.This protocol combines lower layer(Physical layer and Data Link layer)sensing that is derived from the channel estimation model.Also,it periodically updates and stores the routing table for optimal route decision-making.Moreover,in order to achieve higher throughput and lower delay,a new routing metric is presented.To evaluate the performance of the proposed protocol,network simulations have been conducted and also compared to the widely used routing protocols,as a benchmark.The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio(with an improved margin of around 5–20%approximately)under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks(MCRNs).Moreover,the cross-layer routing protocol successfully achieves high routing performance in finding a robust route,selecting the high channel stability,and reducing the probability of PU interference for continued communication.

Secure Transmission in Cognitive Radio Networks Using Full-Duplex Technique with Outdated CSI

This paper investigates the effects of the outdated channel state information(CSI)on the secrecy performance of an underlay spectrum sharing cognitive radio networks(CRNs),where the secondary user(SU)source node(Alice)aims to transmit the trusted messages to the full-duplex(FD)aided SU receiver(Bob)with the assistance of cooperative relay(Relay).Considering the impact of feedback delay,outdated CSI will aggravate the system performance.To tackle such challenge,the collaborative zero-forcing beamforming(ZFB)scheme of FD technique is further introduced to implement jamming so as to confuse the eavesdropping and improve the security performance of the system.Under such setup,the exact and asymptotic expressions of the secrecy outage probability(SOP)under the outdated CSI case are derived,respectively.The results reveal that i)the outdated CSI of the SU transmission channel will decrease the diversity gain from min(NANR,NRNB)to NRwith NA,NRand NBbeing the number of antennas of Alice,Relay and Bob,respectively,ii)the introduction of FD technique can improve coding gain and enhance system performance.

Robust beamforming for IRS-aided SWIPT in cognitive radio networks

Intelligent reflecting surface(IRS)is widely recognized as a promising technique to enhance the system perfor-mance,and thus is a hot research topic in future wireless communications.In this context,this paper proposes a robust BF scheme to improve the spectrum and energy harvesting efficiencies for the IRS-aided simultaneous wireless information and power transfer(SWIPT)in a cognitive radio network(CRN).Here,the base station(BS)utilizes spectrum assigned to the primary users(PUs)to simultaneously serve multiple energy receivers(ERs)and information receivers(IRs)through IRS-aided multicast technology.In particular,by assuming that only the imperfect channel state information(CSI)is available,we first formulate a constrained problem to maximize the minimal achievable rate of IRs,while satisfying the harvesting energy threshold of ERs,the quality-of-service requirement of IRs,the interference threshold of PUs and transmit power budget of BS.To address the non-convex problem,we then adopt triangle inequality to deal with the channel uncertainty,and propose a low-complexity algorithm combining alternating direction method of multipliers(ADMM)with alternating optimi-zation(AO)to jointly optimize the active and passive beamformers for the BS and IRS,respectively.Finally,our simulation results confirm the effectiveness of the proposed BF scheme and also provide useful insights into the importance of introducing IRS into the CRN with SWIPT.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

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.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

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.

Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited applications.CRWSN is widely utilized by future Internet of Things(IoT)applications.Since a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity issue.Generally,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy limited.Hierarchical clusterrelated techniques for overall network management can be suitable for the scalability and stability of the network.This paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for CRWSN.The MDMO-EAC technique mainly intends to group the nodes into clusters in the CRWSN.Besides,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the work.In addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network efficiency.The presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying measures.The comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

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

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.

A Cooperative Cognitive Radio Spectrum Sensing Based on Correlation Sum Method with Linear Equalization

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal Communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

A Spectrum Occupancy Model for Primary Users in Cognitive Radio Systems

There are abundant research results related to cognitive radio systems (CR systems), but using queueing models to portray CR systems is a new research trend. In this paper, a single-server retrial cognitive radio system with a linear retrial rate has been considered. The system has two types of users: primary users and secondary users. Secondary users have no effect on primary users because primary users have preemptive precedence. As a result, our purpose is to examine some performance indicators such as the expected queue length for primary users, the probability of the system being idle or occupied by a secondary user, and the probability of the system being busy. This paper begins by deriving the expressions for the generating functions based on the balance equations, so that we can calculate our goal conveniently.

Design of Clustering Techniques in Cognitive Radio Sensor Networks

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.

Research on User Pairing Techniques Based on NOMA in Cognitive Radio Networks

This paper outlined a Non-Orthogonal Multiple Access (NOMA) grouping transmission scheme for cognitive radio networks. To address the problems of small channel gain difference of the middle part users caused by the traditional far-near pairing algorithm, and the low transmission rate of the traditional Orthogonal Multiple Access (OMA) transmission, a joint pairing algorithm was proposed, which provided multiple pairing schemes according to the actual scene. Firstly, the secondary users were sorted according to their channel gain, and then different secondary user groups were divided, and the far-near pairing combined with (Uniform Channel Gain Difference (UCGD) algorithm was used to group the secondary users. After completing the user pairing, the power allocation problem was solved. Finally, the simulation data results showed that the proposed algorithm can effectively improve the system transmission rate.

Cooperative Spectrum Sensing Exploiting a Certain Number of CR Users Based on Multi- Antennas in Cognitive Radio Networks

In this paper,a cooperative spectrum sensing scheme,which is based on cooperation of a certain number of secondary users and cooperative diversity under multi-antenna scenario,is proposed.Under multi-antenna scenario,we set a targeted detection probability and optimize the false alarm probability of the network by choosing a certain number of secondary users with the highest primary user’s signal to noise ratio.The detection performance of the network is also evaluated when all the secondary users are cooperating to illustrate the benefits of the proposed scheme as a contrast.In addition,how to choose the detection threshold of the secondary user is analyzed for the purpose of decreasing the average risk.Theory analysis and simulation results show that the optimum false alarm probability can be derived by cooperating a certain number of secondary users rather than all the secondary users and the detection performance of the network can be further improved if secondary users are equipped with multiple antennas.Also,a minimum average risk can be obtained by optimizing the detection threshold.

Multi-criteria user selection scheme for learning-based multiuser MIMO cognitive radio networks

For multiuser multiple-input-multiple-output （MIMO） cognitive radio （CR） networks a four-stage transmiision structure is proposed. In learning stage, the learning-based algorithm with low overhead and high flexibility is exploited to estimate the channel state information （ CSI ） between primary （PR） terminals and CR terminals. By using channel training in the second stage of CR frame, the channels between CR terminals can be achieved. In the third stage, a multi-criteria user selection scheme is proposed to choose the best user set for service. In data transmission stage, the total capacity maximization problem is solved with the interference constraint of PR terminals. Finally, simulation results show that the multi-criteria user selection scheme, which has the ability of changing the weights of criterions, is more flexible than the other three traditional schemes and achieves a tradeoff between user fairness and system performance.

Impacts of feedback delay and estimation error on secrecy performance of MISO single eavesdropper cognitive radio networks

Due to the broadcast nature of wireless transmission medium,security threats may hinder propagation of cognitive radio systems for commercial and military data application. This paper sets a channel error analytical framework and studies the joint impact of estimation errors and feedback delay on secrecy performance in cognitive radio networks. Under the assumption that system applies beamforming and jamming scheme,a multi-antenna cognitive base station( CBS) sends confidential signals to a secondary user( SU) in the presence of M primary users( PUs) and an eavesdropper. Assuming only imperfect channel state information( CSI) about the receivers is available,secrecy rate,outage probability,secrecy throughput are deduced to obtain a closed-form expression. It is shown that while the transmit power increases,secrecy throughput would reach to a constant. Simulation results show that feedback delay adversely impacts on secrecy rate,connection outage probability and secrecy throughput,while estimation error causes more impact on secrecy outage probability. Furthermore,the secrecy rate could increase progressively with the transmit power only if there exists no feedback delay.