一种基于压缩采样的宽带协作频谱感知算法

s an effective technology to reduce the wideband sampling overhead, compressed sampling has been extensively used for wideband spectrum sensing in cognitive radio network. But it has special requirement for the spectrum sparsity which makes it no longer suitable for traditional cooperative spectrum sensing that divides the whole wideband into several narrowbands and then distributes to each sensor node. However, if each sensor node senses the whole wideband and sends the results to the decision fusion center, the overhead will be multiply increased. So, a new cooperative sensing algorithm is proposed in this paper. In this algorithm, sensor nodes estimate the spectrum sparsity cooperatively and then sample separately for fusion decision to combine the compressed sampling and cooperative sensing together. Simulation results represent that the proposed algorithm can increase the detection probability meanwhile save the overhead effectively.