基于迭代支持检测的分布式压缩频谱感知算法

Distributed Compressive Spectrum Sensing Algorithm Based on Iterative Support Detection

在宽带认知无线电网络中,为了解决频谱感知所面临的诸如高采样速率、信道衰落以及认知无线电用户硬件资源受限等技术挑战,提出一种基于迭代支持检测的分布式压缩频谱感知算法.通过一跳通信的方式,认知无线电用户能够将各自的检测信息扩散到整个网络,并得到全局一致的支持集;同时,认知无线电用户进行本地稀疏信号重构时,利用一致的支持集信息能够保证不同用户的估计结果具有联合稀疏特性,从而得到更为准确的频谱检测结果.仿真结果表明,文中的分布式频谱感知算法能够以低于奈奎斯特准则的采样速率有效地实现频谱检测,并且在不需要融合中心的前提下获得近似最佳的检测性能.

In wideband cognitive radio （CR） networks, high sampling rate, wireless fading channels and limited hardware resources all impact the spectrum sensing. In this paper, a distributed compressive spectrum sensing algorithm based on the iterative support detection is proposed, in which CRs spread their detection information in the whole network through the one-hop communication and the global consensus is achieved on the support set. Then, through a shared support set, the joint sparsity is utilized by CRs during the local sparse signal reconstruction, thus realizing more accurate spectrum detection. Simulation results show that the proposed algorithm helps to achieve effective spectrum detection at a sub-Nyquist sampling rate and possesses near-optimal detection performance in the absence of a fusion center.