一种基于子空间追踪的宽带压缩频谱感知方法

A Wideband Compressive Spectrum Sensing Approach Based on Subspace Pursuit

频谱感知是认知无线电进行动态频谱管理的首要任务。为了克服传统频谱感知算法对采样速率的过高要求,本文将压缩感知理论中的子空间追踪(SP)重构算法应用于协作式宽带压缩频谱感知中,提出了基于SP的宽带压缩频谱感知方法。该方法首先通过SP重构算法恢复出信号频谱,然后根据所恢复的频谱确定认知用户的能量判决门限,进而利用能量检测算法最终判决出可利用的频谱空穴。仿真结果表明,该方法在低压缩比下具有良好的频谱检测能力,且计算复杂度降低。

Spectrum sensing is the fundamental task for dynamic spectrum management in cognitive radio（CR）. In order to overcome high requirement of sampling rate in traditional spectrum sensing algorithms, subspace pursuit（SP） reconstruction method in the compressed sensing（CS） theory was proposed to be applied in collaborative wideband compressive spectrum sensing. The scheme reconstructed signal spectrum via SP, fixed energy thresholds for each cognitive users, and then detected and identified the available spectrum holes by energy detection method. Simulation results show, the proposed method has better spectrum detection performance in the condition of lower compression ratio, and computational complexity is able to be reduced.