Collaborative spectrum sensing optimisation algorithms for cognitive radio networks

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance

Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks

Spectrum sensing, which aims at detecting spectrum holes, is the precondition for the implementation of cognitive radio (CR). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage. Due to hardware limitations, each cognitive radio node can only sense a relatively narrow band of radio spectrum. Consequently, the available channel sensing information is far from being sufficient for precisely recognizing the wide range of unoccupied channels. Aiming at breaking this bottleneck, we propose to apply matrix completion and joint sparsity recovery to reduce sensing and transmitting requirements and improve sensing results. Specifically, equipped with a frequency selective filter, each cognitive radio node senses linear combinations of multiple channel information and reports them to the fusion center, where occupied channels are then decoded from the reports by using novel matrix completion and joint sparsity recovery algorithms. As a result, the number of reports sent from the CRs to the fusion center is significantly reduced. We propose two decoding approaches, one based on matrix completion and the other based on joint sparsity recovery, both of which allow exact recovery from incomplete reports. The numerical results validate the effectiveness and robustness of our approaches. In particular, in small-scale networks, the matrix completion approach achieves exact channel detection with a number of samples no more than 50% of the number of channels in the network, while joint sparsity recovery achieves similar performance in large-scale networks.Comment: 12 pages, 11 figure

Low Complexity Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Networks

Clustering approach is considered a management technology that arranged the distributed cognitive radio users into logical groups to improve the sensing performance of the network. A lot of works in this area showed that cluster-based spectrum sensing (CBSS) technique efficiently tackled the trade-off between performance and overhead issue. By employing the tree structure of the cluster, a multilevel hierarchical cluster-based spectrum sensing (MH-CBSS) algorithm was proposed to compromise between the gained performance and incurred overhead. However, the MH-CBSS iterative algorithm incurs high computational requirements. In this thesis, an energy-efficient low computational hierarchical cluster-based algorithm is proposed which reduces the incurred computational burden. This is achieved by predetermining the number of cognitive radios (CRs) in the cluster, which provides an advantage of reducing the number of iterations of the MH-CBSS algorithm. Furthermore, for a comprehensive study, the modified algorithm is investigated over both Rayleigh and Nakagami fading channels. Simulation results show that the detection performance of the modified algorithm outperforms the MH-CBSS algorithm over Rayleigh and Nakagami fading channels. In addition, a conventional energy detection algorithm is a fixed threshold based algorithm. Therefore, the threshold should be selected properly since it significantly affects the sensing performance of energy detector. For this reason, an energy-efficient hierarchical cluster-based cooperative spectrum sensing algorithm with an adaptive threshold is proposed which enables the CR dynamically adapts its threshold to achieve the minimum total cluster error. Besides, the optimal threshold level for minimizing the overall cluster detection error rate is numerically determined. The detection performance of the proposed algorithm is presented and evaluated through simulation results

SPECTRUM SHARING IN COGNITIVE RADIO NETWORKS WITH QUALITY OF SERVICE AWARENESS

The goal of this thesis is to study performance of cognitive radio networks in terms of total spectrum utilization and throughput of secondary networks under perfect and imperfect sensing for Additive White Gaussian Noise (AWGN) and fading channels. The effect of imperfect sensing was studied by applying non-collaborative and collaborative sensing techniques using energy detecting and square law combining techniques, respectively. Spectrum allocation for heterogeneous networks in cognitive radio networks was discussed and a new sharing algorithm that guarantee Quality of Service (QoS) for different secondary users’ applications was proposed. The throughput degradation of secondary users due to the activities of the primary users was explored by varying the arrival rate of the primary users in a given spectrum band. Computer simulation showed that increasing the primary user’s activity will increase the total spectrum utilization but decreases the secondary users’ throughput simultaneously. The effect of the received Signal to Noise Ratio (SNR) of the primary user on the cognitive radio network performance is studied in which, a high SNR of primary users led to a higher throughput of secondary network in AWGN channels compared to Nakagami fading channels. The effect of applying cooperative sensing is also presented in this thesis. As we increased the number of cooperating sensors, the network throughput increased which proves the advantage of applying cooperative sensing. A spectrum allocation algorithm for heterogeneous network model is developed to study the QoS assurance of secondary users in cognitive radio networks. The system performance of the heterogeneous network was investigated in terms of the total spectrum utilization. It is found that, higher number of secondary users, better channel’s condition and low required QoS of applications would increase the spectrum utilization significantly. vii In this thesis, the proposed allocation algorithm was applied to the heterogeneous cognitive radio model and its performance was compared to the First Come First Served (FCFS) algorithm in both AWGN and fading channels. The proposed algorithm provided a higher average SNR and spectrum utilization than FCFS algorithm and guaranteed the QoS requirement for applications of secondary users. The effect of imperfect sensing on the system performance was investigated, and it was shown that, as the probability of detection increases the total applications’ data rate increases significantly. The proposed algorithm guaranteed the QoS requirement for each application of secondary users. The effect of imperfect sensing on the system performance was investigated, and it was shown that, as the probability of detection increases the total data rate increases significantly