A Novel Algorithm to Optimize Sampling Rate for Compressed Sensing

The fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge for the radio environment cognition. Traditional spectrum sensing technique is neither efficient nor necessary, wasting the spectrum access opportunities on the vacant spectrum holes of primary users (PUs). Considering the sparse signal feature, a novel compressed sensing technique is proposed by using the minimal sampling rate to detect spectrum holes, which is more efficient than the Nyquist sampling rate and traditional compressed sampling rate that is required to reconstruct the original signal. The proposed compressed sensing process is divided into two stages called approaching stage and monitoring stage. The first stage is to gradually approach the minimal sampling rate required to achieve the spectrum detection performance by using the feedback mechanism. And the second stage is to monitor the status of PU according to the threshold using the sampling rate from the first stage. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm. Numerous results show that the proposed compressed sensing technique can reduce the sampling rate to 35%, with acceptable detection probability over 0.9.