Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA

Performance analysis of spectrum sensing techniques for future wireless networks

In this thesis, spectrum sensing techniques are investigated for cognitive radio (CR) networks in order to improve the sensing and transmission performance of secondary networks. Specifically, the detailed exploration comprises of three areas, including single-node spectrum sensing based on eigenvalue-based detection, cooperative spectrum sensing under random secondary networks and full-duplex (FD) spectrum sensing and sharing techniques. In the first technical chapter of this thesis, eigenvalue-based spectrum sensing techniques, including maximum eigenvalue detection (MED), maximum minimum eigenvalue (MME) detection, energy with minimum eigenvalue (EME) detection and the generalized likelihood ratio test (GLRT) eigenvalue detector, are investigated in terms of total error rates and achievable throughput. Firstly, in order to consider the benefits of primary users (PUs) and secondary users (SUs) simultaneously, the optimal decision thresholds are investigated to minimize the total error rate, i.e. the summation of missed detection and false alarm rate. Secondly, the sensing-throughput trade-off is studied based on the GLRT detector and the optimal sensing time is obtained for maximizing the achievable throughput of secondary communications when the target probability of detection is achieved. In the second technical chapter, the centralized GLRT-based cooperative sensing technique is evaluated by utilizing a homogeneous Poisson point process (PPP). Firstly, since collaborating all the available SUs does not always achieve the best sensing performance under a random secondary network, the optimal number of cooperating SUs is investigated to minimize the total error rate of the final decision. Secondly, the achievable ergodic capacity and throughput of SUs are studied and the technique of determining an appropriate number of cooperating SUs is proposed to optimize the secondary transmission performance based on a target total error rate requirement. In the last technical chapter, FD spectrum sensing (FDSS) and sensing-based spectrum sharing (FD-SBSS) are investigated. There exists a threshold pair, not a single threshold, due to the self-interference caused by the simultaneous sensing and transmission. Firstly, by utilizing the derived expressions of false alarm and detection rates, the optimal decision threshold pair is obtained to minimize total error rate for the FDSS scheme. Secondly, in order to further improve the secondary transmission performance, the FD-SBSS scheme is proposed and the collision and spectrum waste probabilities are studied. Furthermore, different antenna partitioning methods are proposed to maximize the achievable throughput of SUs under both FDSS and FD-SBSS schemes

On the performance of cooperative spectrum sensing in random cognitive radio networks

This paper investigates the performance of cooperative spectrum sensing in cognitive radio networks using the stochastic geometry tools. In order to cope with the diversity of received signal-to-noise ratios at secondary users, a practical and efficient cooperative spectrum sensing model is proposed and investigated based on the generalized likelihood ratio test detector. In order to investigate the cooperative spectrum sensing system, the theoretical expressions of the probabilities of false alarm and detection of the local decision are derived. The optimal number of cooperating secondary users is then investigated to achieve the minimum total error rate of the final decision by assuming that the secondary users follow a homogeneous Poisson point process. Moreover, the theoretical expressions for the achievable ergodic capacity and throughput of the secondary network are derived. Furthermore, the technique of determining an appropriate number of cooperating secondary users is proposed in order to maximize the achievable ergodic capacity and throughput of the secondary network based on a target total error rate requirement. The analytical and simulation results validate the chosen optimal number of collaborating secondary users in terms of spectrum sensing, achievable ergodic capacity, and throughput of the secondary network