Sensing nodes selection and data fusion in cooperative spectrum sensing

Cooperative spectrum sensing can overcome hidden terminal and shadowed fading, a novel strategy of improving the effectiveness of cooperative sensing, which is inspired by cross-correlation matrix (CCM) estimation and a linear weighted algorithm, is presented in this study. Namely, the values of channels gain ratio and signal-to-noise ratio are estimated by means of the CCM which is composed of the sampled signals and, therefore, the selection of sensing nodes is achieved by linearly weighting the spectrum sensing data. The subsequent data fusion strategy includes two modes, that is, the full fusion mode in which all sensing nodes data are linearly weighted, and the partial fusion mode where only part of sensing nodes data are linearly weighted. A major advantage of this scheme is that these two modes are realised by only exploiting the sampled signals of sensing nodes without any prior knowledge of primary user. Simulations verify the effectiveness of the proposed CCM estimation and the data fusion strategy.