Optimal entropy quantization for maximum likelihood estimation based cooperative spectrum sensing

This paper focuses on the quantization of soft decision based cooperative spectrum sensing (CSS). The soft data fusion CSS schemes in previous research works provide considerable enhancement in the probability of detection, but at the expense of increased bandwidth required for transmitting the sensing measurements to the Fusion Center (FC). In this paper, Maximum likelihood Estimation (MLE) statistics are quantized and sent to the FC as an alternative of the quantized decision statistics of Log-Likelihood Ratios (LLRs) which assume that the distribution of the received primary user (PU) signal is known. Uniform and optimal entropy quantization's are proposed to reduce the reporting channel overheads and a low complex overhead is proposed which helps speed up the PU signal sensing process. This can be significant in high data rate applications. Simulation results illustrate that the scheme can obtain a high detection rate and a reduction in the reporting channel bandwidth.