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.

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.

NOVEL SPECTRUM SENSING METHOD BASED ON THE SPATIAL SPECTRUM FOR COGNITIVE RADIO SYSTEMS

Spectrum sensing is the key problem for Cognitive Radio(CR) systems.A method based on the Peak-to-Average Amplitude-Ratio(PAAR) of the Spatial Spectrum(SS) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users.The greatest advantage of the new method is that it requires no information of the noise power and is free of the noise power uncertainty.Both the simulation and the analytical results show that the proposed method is robust to noise uncertainty,and greatly outperform the classical Energy Detector(ED) method.

MODELING RADIO RESOURCE MANAGEMENT IN COGNITIVE RADIO SYSTEM BASED ON OFDMA USING COLORED PETRI NET

Cognitive Radio(CR) system based on Orthogonal Frequency Division Multiple Access(OFDMA),such as Wireless Regional Area Networks(WRAN) and Worldwide Interoperability for Microwave Access(WiMAX),often attempt to improve performance via dynamic radio resource management,which is characterized as concurrent processing of different traffic and nondeterministic system capacity.It is essential to design and evaluate such complex system using proper modeling and analysis tools.In the previous work,most of the communication systems were modeled as Markov Chain(MC) and Stochastic Petri Nets(SPN),which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic.In this paper,we develop an executable top-down hier-archical Colored Petri Net(CPN) model for adaptive OFDMA CR system,and analyze its performance using CPN tools.The results demonstrate that the CPN can model different radio resource manage-ment algorithms in CR Systems,and the CPN tools require less computational effort than Markov model using Matlab,with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.

Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

A Robust Cooperative Spectrum Sensing Scheme in Cognitive Radio Systems

Performance of cluster-based cooperative spectrum sensing is more greatly improved than that of the traditional cooperative spectrum sensing. However, the style of clusters is not pointed out on traditional cluster-based method. In view of all the fading in the cooperative spectrum sensing, space diversity, and selective diversity, a robust cooperative spectrum sensing scheme based on clusters is proposed. Simulation results show that the detection performance of this new scheme is more close to that of the ideal cluster-based cooperative spectrum sensing scheme, and the excellent performance with higher reliability is gained relative to the actual cluster-based cooperative spectrum sensing scheme. Finally, the number of users in a cluster of the proposed scheme is discussed. The two users in a cluster are not the optimal solution to the overall implementation of cognitive radio.

Interference cancellation scheme for uplink cognitive radio systems

This paper investigates the interference cancellation （IC） scheme for uplink cognitive radio systems, using the spectrum underlay strategy where the primary users （PUs） and the secondary users （SUs） coexist and operate in the same spectrum. Joint MMSE-based parallel interference cancellation （PIC） and Turbo decoding scheme is proposed to reduce the interference to the PUs, as well as to the SUs, in which the minimum mean square estimation （MMSE） filter is only employed in the first iteration, regarded as the ＂weakest link＂ of the whole detection process, to improve the quality of the preliminary detections results before they are fed to the Turbo decoder. Simulation results show that the proposed scheme can efficiently eliminate the interference to the PUs, as well as to the SUs.

Research on cooperative detection and multiple access for cognitive radio system

As cognitive radio (CR) needs to detect spectrum accurately and utilize the idle spectrum efficiently,a new model of CR is proposed.This model adopts energy detection with auto-adapted threshold and produces spectrum mark vector.The basis function is obtained through making IDFT to the inner product of spectrum and phase vectors,and it is variable with the spectrum states.Data are sent through modulation on the basis function,and it can be estimated by correlation in the receiving end.The cooperative detection and multiple access of this model are also researched in this paper,and the performance is finally analyzed.Simulations show the cooperative detection of this model can obtain higher detection probability,and the disaccord of the basis functions between sending and receiving ends can lower its performance.

A Static Spectrum Aggregation Algorithm in Cognitive Radio System

Cognitive radio ( CR) is known as an intelligent technology allowing a secondary user ( SU) to occupy a primary user's ( PU's) band when it is idle. Appearance of the idle band may be contiguous or non-contiguous. In other words,the PU's band and the idle band are distributed alternately over the spectrum. Apparently the non-contiguous idle band cannot be fully utilized in this context. This paper proposes an algorithm to solve the problem of low utilization of the non-contiguous idle band and to increase the number of the SU who needs access to the spectrum by a static spectrum aggregation ( SSA) method combined with non-contiguous orthogonal frequency division multiplexing ( NC-OFDM) . The simulation results show that the SSA algorithm realizes at least 70% utilization of the idle band which provides access to the spectrum for as many as 30 SUs as expected.

OPTIMIZATION OF SPECTRUM UTILIZATION IN COGNITIVE RADIO SYSTEM

In Cognitive Radio(CR) networks,CR user has to detect the spectrum channel periodically to make sure that the channel is idle during data transmission frame in order to avoid the collisions to the primary users.Hence recent research has been focused on the interference avoidance problem.Quality of Service(QoS) requirement of CR user will affect the time of data transmission in each frame.In this paper,in order to solve the interference avoidance and spectrum utilization problems without cooperation among CR users,a new scheme to obtain the optimal duration of data transmission frame is proposed to maximize the spectrum utilization and guarantee the protection to the primary users.The main advantages of our proposed scheme include the followings:(1) QoS requirement of CR user is concerned;(2) p-persistent Media Access Control(MAC) random access is used to avoid the collisions among CR users;(3) CR network system capacity is considered.We develop a Markov chain of the primary spectrum channel states and an exponential distribution of the CR user's traffic model to analyze the performance of our proposed scheme.Computer simulation shows that there is an optimal data transmission time to maximize the spectrum utilization.However,the regulatory constraint of the collision rate to the primary users has to be satisfied at the expense of spectrum utilization.And also the tradeoff between the spectrum utilization and the capacity of the CR system is taken into account.

AN EFFECTIVE SUBOPTIMAL POWER LOADING SCHEME IN OFDM-BASED COGNITIVE RADIO SYSTEMS

Orthogonal frequency division multiplexing(OFDM) is an attractive modulation candidate for Cognitive Radio(CR) networks.Effective and reliable subcarrier power allocation in OFDM-based Cognitive Radio(CR) networks is a challenging problem.This paper focuses on the power allocation for OFDM-based Cognitive Radio(CR) networks.Our objective is to maximize the total transmission rates of Secondary Users(SU) by adjusting the power of subcarrier while the interference introduced to the Primary User(PU) is within a certain range and the total power of subcarrier is not beyond the total power constraint.We investigate the optimal power allocation algorithm for OFDM-based Cog-nitive Radio(CR) based on convex optimization theory.Then,because of high complexity of the op-timal power allocation algorithm,we propose an effective suboptimal power loading scheme.Theory analysis and simulation results show that the performance of the suboptimal power allocation algorithm is close to the performance of the optimal power allocation algorithm,while the complexity of the suboptimal power allocation algorithm is much lower.

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.

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.

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.

Effective capacity of cognitive radio systems in asymmetric fading channels

As known that the effective capacity theory offers a methodology for exploring the performance limits in delay constrained wireless networks, this article considered a spectrum sharing cognitive radio （CR） system in which CR users may access the spectrum allocated to primary users （PUs）. Particularly, the channel between the CR transmitter （CR-T） and the primary receiver and the channel between the CR-T and the CR receiver （CR-R） may undergo different fading types and arbitrary link power gains. This is referred to as asymmetric fading. The authors investigated the capacity gains achievable under a given delay quality-of-service （QoS） constraint in asymmetric fading channels. The closed-form expression for the effective capacity under an average received interference power constraint is obtained. The main results indicate that the effective capacity is sensitive to the fading types and link power gains. The fading parameters of the interference channel play a vital role in effective capacity for the looser delay constraints. However, the fading parameters of the CR channel play a decisive role in effective capacity for the more stringent delay constraints. Also, the impact of multiple PUs on the capacity gains under delay constraints has also been explored.

Iterative optimization of detection threshold and throughput for secondary users in multiband cognitive radio systems

In cognitive radio （CR） systems, efficient spectrum sensing ensures the secondary user （SU） to successfully access the spectrum hole. Typically, the detection problem has been considered separately from the optimization of transmission strategy. However, in practice, due to non-zero probabilities of miss detection and false alarm, the sensing phase has an impact on the throughput of SUs as well as on the transmission of primary user （PU）. In this paper, using energy detection, we maximize the total throughput of SUs by jointly optimizing the detection threshold and transmission strategy in multiband CR systems. A set of iteration based algorithms are proposed to solve this mix-integer programming problem, which show better performance compared with uniform detection threshold selection algorithm suggested by IEEE 802.22 standard.

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)).

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.

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.

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.