Optimal design of weighted hard decision for cooperative spectrum sensing under noise uncertainty

Abstract

In this thesis, we studied the weighted hard decision for cooperative spectrum sensing under noise uncertainty. Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A weighted hard decision algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of conventional two regions. The final decision is made by weighted combination at the fusion center. The key idea is the full utilization of the information contained in the uncertainty region. The paper studied that the weight factor and the normal detection threshold, which determines the two thresholds, are also optimized to minimize the Bayes risk in different channels conditions (AWGN and Rayleigh fading channel). Simulation result illustrate that proposed algorithm can significantly improve the detection performance, and is more powerful to noise uncertainty than the existing algorithms. The optimal weight factor and normal detection threshold are decided in accordance with uncertainty, cost of false alarm and missed detection and channel conditions.