基于加权平均一致算法的宽带压缩频谱感知

Compressed wideband spectrum sensing based on weighted average consensus

在认知无线电(CR)网络中进行频谱共享接入,首要的任务是进行频谱感知,并发现频谱空洞。基于认知无线网络中信号频域的固有稀疏性,本文结合了压缩感知(CS)技术与加权平均一致(weighted averageconsensus)算法,建立了分布式宽带压缩频谱感知模型。频谱感知分为两个阶段,在感知阶段,各个CR节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并做出本地频谱估计;在信息融合阶段,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,并得到最终的频谱估计结果,获得分集增益。仿真结果表明,结合压缩感知与加权平均一致算法增强了频谱感知的性能,比在相同的CR网络中使用平均一致算法时有了性能上的提升。

In cognitive radio（CR） networks with dynamic spectrum sharing,the first and most important task is spectrum sensing and detection of spectrum holes.On the basis that the wireless signal of primary user in cognitive radio network is inherently sparse in frequency domain,this paper develops a distributed compressed wideband spectrum sensing approach which combines compressed sensing technology and weighted average consensus algorithm.Spectrum sensing takes two stages.At sensing stage,compressed sensing is performed at each CR nodes to sample the received time domain wideband signal at practical complexity and cost,and then each CR nodes locally reconstruct the frequency domain signal.At fusion stage,to take different individual situation of each CR nodes into consideration,a weighted soft measurement combining scheme without a fusion center is adopted to combine the local spectrum sensing results of each CRs in a distributed way to get the final estimation of the frequency domain signal,using weighted average consensus algorithm.Simulation results show that spectrum sensing performance is enhanced with the using of both compressed sensing and weighted average consensus algorithm,and the performance of using weighted average consensus algorithm is better than the performance of using average consensus algorithm when in the same CR network.