Cognitive Jammers Assisted Covert Communication in Cognitive Radio Networks

In this work,we investigate the covert communication in cognitive radio(CR)networks with the existence of multiple cognitive jammers(CJs).Specifically,the secondary transmitter(ST)helps the primary transmitter(PT)to relay information to primary receiver(PR),as a reward,the ST can use PT's spectrum to transmit private information against the eavesdropper(Eve)under the help of one selected cognitive jammer(CJ).Meanwhile,we propose three jammer-selection schemes,namely,link-oriented jammer selection(LJS),min-max jammer selection(MMJS)and random jammer selection(RJS).For each scheme,we analyze the average covert throughput(ACT)and covert outage probability(COP).Our simulation results show that CJ is helpful to ST's covert communication,the expected minimum detection error probability and ACT can be significantly improved with the increase of false alarm of CJ.Moreover,the LJS scheme achieves best performance in ACT and COP,followed by RJS scheme,and MMJS scheme shows the worst performance.

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.

Radio resource management in energy harvesting cooperative cognitive UAV assisted IoT networks:A multi-objective approach

Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.

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.

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.

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.

Improving the Reliability of Unmanned Aircraft System Wireless Communications through Cognitive Radio Technology

Unmanned Aircraft System networks are a special type of networks where high speeds of the nodes, long distances and radio spectrum scarcity pose a number of challenges. In these networks, the strength of the transmitted/received signals varies due to jamming, multipath propagation, and the changing distance among nodes. High speeds cause another problem, Doppler Effect, which produces a shifting of the central frequency of the signal at the receiver. In this paper we discuss a modular system based on cognitive to enhance the reliability of UAS networks.

User-Based Discrete-Time Queuing Analysis for Opportunistic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, the spectrum utilization can be improved by cognitive users opportunistically using the idle channels licensed to the primary users. However, the new arrived cognitive users may not be able to use the channel immediately since the channel usage state is random. This will impose additional time delay for the cognitive users. Excessive waiting delay can make cognitive users miss the spectrum access chances. In this paper, a discrete-time Markov queuing model from a macro point of view is provided. Through the matrix-geometric solution theory, the average sojourn time for cognitive users in the steady state before accessing the spectrum is obtained. Given the tolerant delay of cognitive users, the macro-based throughput is derived and an access control mechanism is proposed. The numerical results show the effects of service completion probability on average sojourn time and throughput. It is confirmed that the throughput can be obviously improved by using the proposed access control mechanism. Finally, the performance evaluations based on users are compared to that based on data packets.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

A COLLABORATIVE SPECTRUM SENSING SCHEME USING FUZZY COMPREHENSIVE EVALUATION IN COGNITIVE RADIO

Cognitive Radio(CR) is a promising technology to solve the challenging spectrum scarcity problem.However, to implement CR, spectrum sensing is the groundwork and the precondition.In this paper, a collaborative spectrum sensing scheme using fuzzy comprehensive evaluation is proposed.The final sensing decision of the proposed scheme is based on the combination of distributed sensing results of different Secondary Users(SUs).To improve the reliability of the sensing decision, the combination procedure takes into account the credibility of each SU, which is evaluated using fuzzy comprehensive evaluation.The effect of the presence of malicious SUs and malfunctioning SUs on the performance of the proposed scheme is also investigated.The efficiency of the scheme is validated through analysis and simulation.

QoS-based MAC protocol for cognitive radio networks

In order to improve the throughput performance of the secondary users （SUs） in the cognitive radio （CR） environment, a quality of service （QoS） based media access control （MAC） protocol is proposed. In this protocol, the CR node maps the channel state as a vector, and the transmitter and the receiver obtain the final channel map through an AND operation to prepare for an optional channel set. Data from the upper application layer are classified into two priority levels according to the QoS requirement. The data of each level relate to different contention windows so that the priority of real time data can be guaranteed. A two-dimensional discrete-time Markov chain is utilized to evaluate the system performance, and mathematical expressions of the system throughput are derived. Simulation results show that compared with the IEEE 802. 11 distributed coordination function （DCF）, the proposed MAC protocol can achieve higher throughput.

Narrowband interference suppression in TR-UWB system based on cognitive radio theory

A cognitive radio transmitted reference ultra-wideband（CR-TR-UWB） system is proposed to improve the performance of TR-UWB systems with narrowband interference（NBI） from primary users（PU）.The transmitter of the CR-TR-UWB system detects the band of PU,and then sends prolate spheroidal wave functions（PSWF） pulses with the same limited band as PU＇s to reduce interference with PU.The receiver uses a notch filter before autocorrelation to eliminate NBI from PU.The simulation results show that the bit error rate（BER） performance of the CR-TR-UWB system is close to that of TR-UWB systems without NBI when the system is interfered by single or double NBIs with a signal to interference ratio（SIR） of 0 dB,and if the signal to noise ratio（SNR） is 10 dB and the SIR varies from-20 to 10 dB,BER performance varies no more than an order of magnitude.The system has excellent resistance to NBI,strong robustness BER performance at different SNRs,and smaller interference with the same frequency band PU.

Dynamic overlapped spectrum allocation based on potential game in cognitive radio networks

In order to make full use of wireless spectrum resources,the behavior of cognitive radio(CR)for dynamic spectrum allocation is analyzed based on the game theoretic framework.The traditional spectrum allocation schemes consider the spectrum allocation among independent frequency bands only,without taking into account mutually overlapped frequency bands.For this reason,an optimal allocation etiquette is defined to promote the cross characteristic of the frequency bands in a dynamic spectrum allocation model.New interference operator and interference temperature constraints are introduced in order to realize calculation of the interference,and the corresponding spectrum allocation scenario can be further formulated as a potential game.Based on the characteristic of dynamic selection using the game theory and the interference avoidance rule of interference temperature,the robustness of CR networks is increased and the scenario is more suitable for the dynamic changing of actual wireless communication and energy saving communication systems.Simulation results show that the signal to interference and noise ratio(SINR) level can be significantly improved through the optimal allocation of any available spectrum.The utilization rate of spectrum and throughput of overall CR networks are increased by fully utilizing the spectrum resources in the dynamic spectrum allocation model.

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.

Distributed optimal relay selection for spectral efficiency improvement in underlay-paradigm based cognitive radio networks

A distributed best-relay node selection scheme is investigated for cooperative communications with adaptive modulation and coding （MAC） strategy over underlay-paradigm based cognitive radio （CR） networks. The scheme aims to maximize the average spectral efficiency and meanwhile to guarantee that the primary link is provided with a minimum-rate for a certain percentage of time. Simulation results demonstrate that the proposed scheme can significantly improve the spectral efficiency compared with other existing schemes.

SPECTRUM SHARING IN ITERATED PRISONER'S DILEMMA GAME BASED ON EVOLUTIONARY STRATEGIES FOR COGNITIVE RADIOS

We study a spectrum sharing problem where multiple systems coexist and interfere with each other. First, an analysis is proposed for distributed spectrum sharing based on Prisoners' Dilemma (PD) in Cognitive Radios (CRs). In one-shot game, selfish and rational CRs greedily full spread their own spectrum space in order to maximize their own rates, which leads to Nash Equilibrium (N.E.). But with long term interaction, i.e., Iterated Prisoner's Dilemma (IPD), CRs can come to cooperate and acquire the social optimal point by using different evolutionary strategies such as Tit For Tat (TFT), Generous TFT (GTFT), etc. Also we compare the performances of the different evolutionary strategies in noise-free and noisy environments for two-player games. Finally, N-player IPD (N-IPD) is simulated to verify our conclusions that TFT is a good strategy for spectrum sharing in CRs.

Subcarrier and power allocation in OFDM-based cognitive radio networks

We formulate the subcarrier and power allocation problem in cognitive radio networks employing orthogonal frequency division multiplexing （OFDM） as a non-linear optimization problem with the objective of maximizing sum capacity under constraints of available subcarriers, interference temperature, power budget, etc. A close-to-optimal solution with much reduced complexity is proposed to separate the problem into two steps, which also considers fairness among secondary users. A fair al- gorithm for subcarrier allocation （FA_SA） is firstly presented. Secondly, a fast iterative water-filling algorithm for power allocation （FIWFA_PA） is also proposed to maximize the sum capacity. Exten- sive simulation results show that sum capacity performance of our low-complexity solution is very close to the optimal one, while significantly improving fairness and reducing computation complexity compared with the existing solutions.

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios

A parallel algorithm for statistical-fairness-based spectrum allocation of cognitive radios is proposedin this paper. The key idea of the algorithm is to pursue the maximum total spectrum utilization of thesystem by adopting a parallel technique in every spectrum allocation, and to ensure the statistical fairnessrule by deploying a particular scheme during a series of allocations. The simulation results show that theproposed algorithm not only achieves a fairer and more efficient allocation of spectrum resources, but alsohas much shorter allocation duration than the color sensitive graph coloring (CSGC) algorithm.

Optimal subcarrier allocation for cognitive radio with multi-user OFDM and distributed antenna

The subcarrier allocation problem in cognitive radio（CR）networks with multi-user orthogonal frequency-division multiplexing（OFDM）and distributed antenna is analyzed and modeled for the flat fading channel and the frequency selective channel,where the constraint on the secondary user（SU）to protect the primary user（PU）is that the total throughput of each PU must be above the given threshold instead of the ＂interference temperature＂.According to the features of different types of channels,the optimal subcarrier allocation schemes are proposed to pursue efficiency（or maximal throughput）,using the branch and bound algorithm and the 0-1 implicit enumeration algorithm.Furthermore,considering the tradeoff between efficiency and fairness,the optimal subcarrier allocation schemes with fairness are proposed in different fading channels,using the pegging algorithm.Extensive simulation results illustrate the significant performance improvement of the proposed subcarrier allocation schemes compared with the existing ones in different scenarios.

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.