Sampling rate dynamic adjustment strategy in adaptive compressed spectrum sensing
Due to the Nyquist-Shannon theorem,the primary problem in wideband spectrum sensing(WBSS)is the acqui-sition of wideband signals.According to the principle of variable-step adaptive filtering algorithm,this paper proposes a dy-namically adjustable sampling rate method for adaptive compressed spectrum sensing(DASR-ACSS).The structure of mod-ulated wideband converter is adopted and a step dynamic adjustment strategy is designed to solve the fixed step size problem of traditional ACSS.For the problem of acquiring the original signal,the stop criterion using the correlation of channel occu-pancy state is designed by statistically analyzing the change trend of the channel occupancy states correlation and detection rate.Through the experimental analysis of the stop criterion,the segmentation function is designed as a step adjustment strategy based on the sigmoid function,and the step compensation factor is designed to improve the performance.Simulation results show that compared with the traditional ACSS method,DASR-ACSS can achieve a high detection rate more rapidly,and can balance the real-time performance and sample rate compression ratio performance.When the SNR is between-30 dB and 30 dB,DASR-ACSS exhibits better detection performance.
wideband spectrum sensingcompressed sensingadaptive spectrum sensingdynamic adjustment strategy of sampling
万方数据
维普
