Stochastic resonance can improve the signal-to-noise ratio of digital baseband signals. However, the output of SR system needs some time for evolution to achieve global steady-state. This paper first analyzes the evolution time of SR systems, which is an important factor for digital baseband signal processing based on SR. This investigation shows that the sampling number per symbol should be rather large, and the minimum sampling number per symbol is deduced according to the evolution time of SR system.

This paper proposes a SNR estimation scheme based on Gibbs sampler. This scheme can estimate SNR using a very short received sequence, and does not require any prior information of the transmitted symbol. Compared with the existing estimators, the performance of this method is better when real SNR is larger than 5 dB in both single path channel and multi-path channel.

Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.