Optimal Cooperative Sensing and Its Robustness to Decoding Errors

Based on the Neyman-Pearson theorem, the optimal cooperative sensing for distributed sensors with time independent signals is derived. It is shown that the optimal scheme is simply a linearly combined energy detection and the combining coefficient is a simple function of the signal to noise ratio (SNR). To reduce the required information at the fusion center and simplify the decision-making process and threshold setting, an approximated optimal cooperative sensing is proposed and compared with some other sub-optimal methods. Finally the impact of decoding error in the reported results is analyzed. Based on the closed-form expression for the performance, it is proved that the impact of decoding error is equivalent to the reduction of sensing time. Simulations are provided to support the results.