中文摘要：

频谱感知的第一步就是采集无线信号进行分析，越来越高的采样率成为宽带频谱感知研究中的难点。实际通信中主用户占用频谱具有稀疏特性，符合压缩感知理论的前提条件。因此，本文利用分布式压缩感知实现宽带频谱感知，提出基于差分信号分布式压缩感知（DS_DCS）的加权宽带频谱感知算法。该算法针对宽带频谱采样率高的问题，利用压缩感知技术降低采样率，同时引入差分处理方法降低计算复杂度；又针对单点检测带来的深衰落、隐节点以及抗噪声能力差等问题，采用分布式感知系统进行多节点协同检测并利用信噪比的估计对信号进行加权处理。仿真证明，该算法能有效降低各节点采样率，大幅提高系统检测概率，显著改善系统对噪声的鲁棒性。

英文摘要：

The first step of spectrum sensing is to collect radio signals for analysis.However,increasingly high sampling rates become the difficulty in broadband spectrum sensing studies.In actual communication,the wide band occupied by primary users possesses sparse features in frequency domain,which conforms to the prerequisite of the Compressed Sensing Theory.Therefore,the work proposes a weighted algorithm for broadband spectrum sensing based on Differential Signal Distributed Compressed Sensing （DS_DCS ）by utilizing distributed compressed sensing to achieve broadband spectrum sensing.This algorithm aims to solve the problem of high sampling rate in broadband spectrum sampling.It used Compressed Sensing Technology to reduce sampling rate and differential processing techniques to lower algorithm complexity.To circumvent problems,such as deep fading,hidden terminals and poor noise immunity caused by single node detection,it applied distributed compressed sensing system to conduct cooperative multi-node detection and estimated SNR to weigh signals.Corroborating simulation results show that this algorithm can effectively reduce sampling rates at each node,substantially increase system detection probability and saliently improve system robustness against noise.