Cooperative Spectrum Sensing with Censoring of Cognitive Radios and MRC- Based Fusion in Fading and Shadowing Channels

In this chapter, the authors study the performance of Cooperative Spectrum Sensing (CSS) with soft data fusion, given by Maximal Ratio Combining (MRC)-based fusion with Weibull faded channels, and Lognormal shadowed channels. More precisely, they evaluate the performance of a MRC-based CSS with Cognitive Radios (CRs) censored on the basis of the quality of the reporting channels. The performance of CSS with two censoring schemes, namely rank-based and threshold-based, is studied in the presence of Weibull fading, Rayleigh fading, and Log-normal shadowing in the reporting channels, considering MRC fusion. The performance is compared with those of schemes based on hard decision fusion rules. Furthermore, depending on perfect or imperfect Minimum Mean Square Error (MMSE) channel estimation, the authors analyze the impact of channel estimation strategy on the censoring schemes. The performance is studied in terms of missed detection probability as a function of several network, fading, and shadowing parameters. Cooperative Spectrum Sensing with Censoring of Cognitive Radios and MRCBased Fusion in Fading and Shadowing Channels Srinivas Nallagonda National Institute of Technology – Durgapur, India Sanjay Dhar Roy National Institute of Technology – Durgapur, India Sumit Kundu National Institute of Technology – Durgapur, India Gianluigi Ferrari University of Parma, Italy