Detection of Random Signal Based on Unreconstructed Sequential Compressive Sensing and its Analysis in Cognitive Wireless Network

 In order to prevent signal distortion, according to the conventional Nyquist sampling theorem, the sampling rate should not be less than twice the Nyquist sampling rate. However, with the increasing use of bandwidth, high-speed sampling rate required is difficult to achieve under the current technology level. Compressive sampling can maintain the structure and information of the original sparse signal far below the Nyquist sampling rate. The existing literatures all discussed about the compressive detection of known signal. Focusing on the detection of sparse random signal , we propose a sequential compressive sensing scheme. Then we discuss the performance of detection and use it in distribute collaboration spectrum sensing. Theoretical analysis and simulation results show that sequential compressive detection can significantly save the number of measurements under a given detection performance. This algorithm reduces the detection time, and also avoids the reconstruction of original signal, of which computer complexity is very high.