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Weighted Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates the optimal weighting scheme for cooperative spectrum sensing in cognitive radio networks, under the constraint of equal probabilities of false alarm and miss detection. The derived optimal weights are determined by individual signal-to-noise-ratio (SNR) of each cooperative sensing node. However, to obtain the precise realistic measurement of each node's SNR is somehow impractical or even infeasible in implementation. Reference matrices are therefore proposed to adaptively record primary user's history behaviors and scavenge the indispensable individual SNR information hidden behind them for setting the weights. Our analyses and numerical results illustrate that the proposed cooperation scheme can achieve significant improvements in spectrum sensing performance.