Collaborative spectrum sensing in a cognitive radio system with non-Gaussian noise

• Main Author: Feng, Tan
• Format: Others
• Language: English
• Published: University of British Columbia 2009
• Online Access: http://hdl.handle.net/2429/7591

With the deployment of new wireless communication devices and services, the demand for radio spectrum continues to grow. Spectrum utilization can he improved using the Cognitive radio, concept which allows secondary users to opportunistically access the unused licensed spectrum bands without causing undue interference to licensed users. Most works on spectrum sensing assume a Gaussian noise model; however, in some situations, an impulsive noise model may be more appropriate. In this thesis, we consider the mixture Gaussian noise and the Laplacian noise model. Approximate closed-form expressions for the probability density functions and cumulative distribution functions of the output of an energy detector with Laplacian noise were obtained using the Pearson approximation technique. An optimal detection scheme based on the likelihood ratio test (I.RT) for mixture Gaussian and Laplacian noise models was studied. Two sub-optimal algorithms, namely DFC detection and EFC detection, are also evaluated. The results show that in contrast to the Gaussian noise case, EFC detection does not always outperform DFC detection and 1-out-of-N fusion rule does not always provide the lowest Pm for a given Pf among K-out-of-N rules in a non-Gaussian noise environment. An algorithm, in which large magnitude SU energy measurements are eliminated at the FC, is proposed to improve the detection performance in impulsive noise, it is shown that substantial detection performance can be achieved. In addition, we study a system model in which the reporting channels between the SUs and the FC, and the channels between any two SUs within the cluster experience Rayleigh fading. The results show that in contrast to the Gaussian noise case, the cluster-based schemes do not always outperform the conventional DFC detection. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate