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 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.

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.

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.

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.

Throughput scheduling in cognitive radio networks based on immune optimization

To study the throughput scheduling problem under interference temperature in cognitive radio networks, an immune algorithm-based suboptimal method was proposed based on its NP-hard feature. The problem is modeled as a constrained optimization problem to maximize the total throughput of the secondary users（ SUs）. The mapping between the throughput scheduling problems and the immune algorithm is given. Suitable immune operators are designed such as binary antibody encoding, antibody initialization based on pre-knowledge, a proportional clone to its affinity and an adaptive mutation operator associated with the evolutionary generation. The simulation results showthat the proposed algorithm can obtain about 95% of the optimal throughput and operate with much lower liner computational complexity.

Analysis of cumulative handoff delay for graded secondary users in cognitive radio networks

According to the fact that the secondary users＇ delay requirements for data transmission are not unitary in cognitive radio networks, the secondary users are divided into two classes, denoted by SU1 and SU2, respectively. It is assumed that SU1 has a higher priority to occupy the primary users＇ unutilized channels than SU2. A preemptive resume priority M/G/1 queuing network is used to model the multiple spectrum handoffs processing. By using a state transition probability matrix and a cost matrix, the average cumulative delays of SU1 and SU2 are calculated, respectively. Numerical results show that the more the primary user＇s traffic load, the more rapidly the SU2＇s cumulative handoff delay grows. Compared with the networks where secondary users are unitary, the lower the SUI＇s arrival rate, the more obviously both SUI＇s and SU2＇s handoff delays decrease. The admission access regions limited by the maximum tolerable delay can also facilitate the design of admission control rules for graded secondary users.

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

Adaptive Spectr um Decision Method for Heterogeneous Cognitive Radio Networks

To improve spectrum utilization and minimize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Secondary User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum decision, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the interrupted service of SU as newly incoming and re-decision process is initialized to find available spectrum in a First-Available-First-Access (FAFA) fashion. Effect of sensing error in PHY layer is also analyzed in terms of extended average residual time. Simulation results show that, for relatively low arriving rate of SU traffic, the proposed spectrum decision method yields at least a delay reduction of 39.5% compared with non-adaptive method. The proposed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.

Cooperative Jamming for Enhancing Security of Cognitive Radio Networks with Multiple Primary Users

In cognitive radio networks(CRNs), through recruiting secondary user(SU) as friendly jammer, the secrecy rate obtained by primary user(PU) can be improved. Previous work only considered a simple scenario with a single PU in their frameworks. In this paper, we will consider a more complicated scenario with multiple PUs and try to investigate the cooperative jamming between multiple PUs and a single SU. When there are multiple PUs in CRN, in order to obtain more spectrum for data transmission, SU will cooperate with multiple PUs at the same time. Considering that both PU and SU are rational and selfish individuals, the interaction between PUs and SU is formulated as a multi-leaders and single-follower Stackelberg game, wherein PU is the leader and SU is the follower. And the Stackelberg Equilibrium(SE) is considered as the final decisions accepted by all PUs and SU. Furthermore, we also prove that when a specific condition is satisfied, the existence of SE can be guaranteed. And a Gauss-Jacobi iterative algorithm is proposed to compute a SE. Finally, simulation results are given to verify the performance and demonstrate that both of the PUs' secrecy rate and the SU's transmission rate can be improved through cooperation.

A Jury-Based Trust Management Mechanism in Distributed Cognitive Radio Networks

In the trust management scheme of the distributed cognitive radio networks, the absence of the central control devices cause many problems such as a lack of standardized control for trust computation, and the absence of the decision makers in trust evaluation and collaborative decision making. A trust management mechanism based on the jury system for distributed cognitive radio networks is proposed in this paper. The "jury user" is designed to collaboratively examine the reputation of the cognitive user in the networks and to perform data fusion and spectrum allocation for distributed cognitive radio networks. Simulation analysis results show that the proposed scheme can ensure accuracy and fairness in trust evaluation and improve effectiveness and flexibility of spectrum allocation.

Power Control and Routing Selection for Throughput Maximization in Energy Harvesting Cognitive Radio Networks

This paper investigates the power control and routing problem in the communication process of an energy harvesting(EH)multi-hop cognitive radio network(CRN).The secondary user(SU)nodes(i.e.,source node and relay nodes)harvest energy from the environment and use the energy exclusively for transmitting data.The SU nodes(i.e.,relay nodes)on the path,store and forward the received data to the destination node.We consider a real world scenario where the EH-SU node has only local causal knowledge,i.e.,at any time,each EH-SU node only has knowledge of its own EH process,channel state and currently received data.In order to study the power and routing issues,an optimization problem that maximizes path throughput considering quality of service(QoS)and available energy constraints is proposed.To solve this optimization problem,we propose a hybrid game theory routing and power control algorithm(HGRPC).The EH-SU nodes on the same path cooperate with each other,but EH-SU nodes on the different paths compete with each other.By selecting the best next hop node,we find the best strategy that can maximize throughput.In addition,we have established four steps to achieve routing,i.e.,route discovery,route selection,route reply,and route maintenance.Compared with the direct transmission,HGRPC has advantages in longer distances and higher hop counts.The algorithm generates more energy,reduces energy consumption and increases predictable residual energy.In particular,the time complexity of HGRPC is analyzed and its convergence is proved.In simulation experiments,the performance(i.e.,throughput and bit error rate(BER))of HGRPC is evaluated.Finally,experimental results show that HGRPC has higher throughput,longer network life,less latency,and lower energy consumption.

Optimal spectrum allocation of primary users in light-handed cognitive radio networks

In traditional cognitive radio(CR) network,secondary users(SUs) are always assumed to obey the rule of"introducing no interference to the primary users(PUs) ".However,this assumption may be not realistic as the CR devices becoming more and more intelligent nowadays.In this paper,with the concept of lighthanded CR,which is proposed to deal with the above mentioned problem by enforcing"punishment"to illegal CR transmissions,the action decisions of primary users(PUs) are modeled as a partially observable Markov decision process(POMDP),and the optimal spectrum allocation scheme with the objective of maximizing their reward is proposed,which is defined by the utility function.Furthermore,a reduced scheme with much smaller state space has been proposed in this paper for lower computational complexity.Extensive simulation results show that the proposed schemes improve the reward significantly compared to the existing scheme.

Energy-Efficient Target Channel Sequence Design for Spectrum Handoffs in Cognitive Radio Networks

Cognitive radio is considered as an efficient way to improve the spectrum efficiency. As one of its key technologies,spectrum handoff can guarantee the transmission continuity of secondary users(SUs). In this paper,we address a new and more generalized spectrum handoff problem in cognitive radio networks(CRNs),by considering simultaneously energy efficiency,multiple spectrum handoffs and multiple channels. Furthermore,effects of the primary users'(PUs')arrival and service rate on the target channel sequence selection are also considered. In order to obtain the energy-efficient target channel sequence,we firstly analyze the energy consumption and the number of delivered bits per hertz in the spectrum handoff process,and formulate a ratio-type energy efficiency optimization problem,which can be transformed into a parametric problem by utilizing fractional programming. Then,we propose an algorithm combining dynamic programming with bisection(DPB)algorithm to solve the energy efficiency optimization problem. Our simulation results verify that the designed target channel sequence has better performance than the existing algorithms in terms of energy efficiency.

Achievement of Interference Alignment in General Underlay Cognitive Radio Networks: Scenario Classification and Adaptive Spectrum Sharing

Interference alignment(IA) is suitable for cognitive radio networks(CRNs).However, in IA spectrum sharing(SS) process of general underlay CRNs, transmit power of cognitive radio transmitters usually should be reduced to satisfy interference constraint of primary user(PU), which may lead to low signalto-noise-ratio at cognitive radio receivers(CRRs). Consequently, sum rate of cognitive users(CUs) may fall short of the theoretical maximum through IA. To solve this problem,we propose an adaptive IA SS method for general distributed multi-user multi-antenna CRNs. The relationship between interference and noise power at each CRR is analyzed according to channel state information, interference requirement of PU, and power budget of CUs. Based on the analysis, scenarios of the CRN are classified into 4 cases, and corresponding IA SS algorithms are properly designed. Transmit power adjustment, CU access control and adjusted spatial projection are used to realize IA among CUs. Compared with existing methods, the proposed method is more general because of breaking the restriction that CUs can only transmit on the idle sub-channels. Moreover, in comparison to other five IA SS methods applicable in general CRN, the proposed method leads to improved achievable sum rate of CUs while guarantees transmission of PU.

Evolution Handoff Strategy for Real-Time Video Transmission over Practical Cognitive Radio Networks

The transmission delay of realtime video packet mainly depends on the sensing time delay(short-term factor) and the entire frame transmission delay(long-term factor).Therefore,the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization.In this paper,we focus on the issue of combining these two optimization models,and propose a novel Evolution Spectrum Handoff(ESH)strategy to minimize the expected transmission delay of real-time video packet.In the microoptimized model,considering the tradeoff between Primary User's(PU's) allowable collision percentage of each channel and transmission delay of video packet,we propose a mixed integer non-linear programming scheme.The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time.In the macro-optimized model,using the optimal stopping time as reward function within the partially observable Markov decision process framework,the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission.Meanwhile,the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions,i.e.,secondary user's mobility,PU's random access,imperfect sensing information,etc..Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

Delay optimization scheduling algorithm in cognitive radio networks

In cognitive radio networks,delay scheduling optimization has attracted an increasing attention in recent years. Numerous researches have been performed on it with different scenarios. However,these approaches have either high computational complexity or relatively poor performance. Delay scheduling is a constraint optimization problem with non-deterministic polynomial（ NP） hard feathers. In this paper,we proposed an immune algorithm-based suboptimal method to solve the problem. Suitable immune operators have been designed such as encoding,clone,mutation and selection. The simulation results show that the proposed algorithm yields near-optimal performance and operates with much lower computational complexity.

Hierarchical Cognition Cycle for Cognitive Radio Networks

Cognitive radio(CR) can bring about remarkable improvement in spectrum utilization.Different cognition cycles have been proposed in recent years.However,most of the existing works only emphasize functional or operational aspects of cognition cycle,regardless of other indispensable aspects and the connection between them.To deal with the emerging situation of "data rich,information vague,knowledge poor" in cognitive radio networks(CRNs),we propose the hierarchical cognition cycle(HCC) as a new transdisciplinary research field in this paper.HCC investigates a fundamental problem,which is how to manage available resources in the complex environment to meet various demands in CRN.A comprehensive theoretical framework of HCC is established in terms of the core,the essence loop,the function loop,the operation loop,and the external loop of HCC.The reduction of uncertainty in CRN is studied and several new metrics in HCC are defined.Furthermore,a few research challenges ahead are presented as well.

Energy Efficiency-Based Decision Making in Cognitive Radio Networks

As a constraint for smart devices,energy consumption has attract people's attention for a long time period. How to get higher resource utilization with less energy consumption is a challenge for cognitive radio networks. Secondary users have to participate in spectrum sensing at the cost of energy and access idle spectrum without interfering primary users. However,not all participating secondary users can access idle spectrum. How to ensure the participation users access spectrum efficiently with a larger probability is an urgent problem to be solved. We propose an Energy Efficiency-based Decision Making(EEDM) for cognitive radio networks,which fully considers residual energy and probability of obtaining spectrum resources. Simulation and analysis show that the proposed scheme can maximize proportion of allocated users under the premise of ensuring the accuracy of spectrum sensing,then balance users' energy consumption and access efficiency,so as to effectively improve the utilization of spectrum resources.