认知无线电中基于压缩感知的非重构频谱检测算法

基于压缩感知的频谱感知研究多是要重构出原始信号,而重构的精确度却直接影响到频谱检测的效果,为降低由此因素而造成的低检测率以及计算复杂度,提出一种在压缩感知框架下的非重构频谱检测算法,将信道划分为多个小信道,依次感知每个小信道,得出所划分信道组的采样协方差矩阵,再与能量检测算法结合分析最终得到每个小信道中的占用情况。对不同的压缩率、信噪比以及用户数分别进行仿真,仿真的结果表明,当压缩率在30%以上和信噪比在-6 d B以上时检测效果较为理想,且多用户的情况下检测概率更高。相对于重构算法,本算法的检测概率更高,计算复杂度更低,检测时间也更短。

Compressed Sensing Based Non-reconstructed Spectrum Detection Algorithm in Cognitive Radio

The research of spectrum sensing based on compressed sensing mostly needs to reconstruct the original signal, but the accuracy of reconstruction directly affects the effect of spectrum detection. To reduce the low detection rate and computational complexity caused by this factor, a non-reconstructed spectrum detection algorithm based on compressed sensing framework is proposed, the channel is divided into multiple small channels, each small channel is sensed in turn, and the sampling covariance matrix of the divided channel group is obtained, and then combined with the energy detection algorithm to finally obtain each small channel occupation. The different compression ratios, signal-to-noise ratios, and number of users are simulated respectively. The results show that the detection rate is better when the compression ratio is above 30% and the signal-to-noise ratio is above -6dB, and the detection probability is higher in the case of multi-users. Compared with the reconstructed algorithm, the algorithm has higher detection probability, lower computational complexity and shorter detection time.