A Sparse Reconstruction Algorithm with Hierarchical Bayesian Analysis for Wideband Spectrum Detection

摘要：

Bayesian Compressive Sensing (BCS) theory with hierarchical Bayesian analysis model is investigated in the process of wideband spectrum detection and data fusion for Cognitive Wireless Sensor Network (C-WSN). A sparse Bayesian reconstruction method is proposed, which is based on the spatialtemporal correlation structure of real non-stationary spectrum signals sensed by multiple cognitive sensor nodes. Novel wideband spectrum detection and data recovery algorithm are implemented by hierarchical Bayesian analysis model, with higher detection probability and lower reconstruction Mean- Square Errors (MSE). Numerical results confirm our theoretical derivations. It is indicated that, compared with Orthogonal Matched Pursuit (OMP) reconstruction algorithm which is based on greedy algorithm, the proposed Tree Structured Wavelet (TSW) BCS reconstruction scheme has advanced detection performance and lower MSE during data recovery process. Meanwhile, fast convergence could be realized in lower compression rate, which provides the effectiveness of our algorithm and proves that it is suitable for wideband spectrum sensing and data sparse reconstruction in large-scale Cognitive WSN.

关键词： Cognitive WSN Hierarchical Bayesian analysis Sparse reconstruction Wideband spectrum detection Tree structured wavelet Bayesian compressive sensing (TSW BCS)

作者: Xiaorong Xu Jianwu Zhang Baoyu Zheng Junrong Yan

作者单位: College of Telecommunication Engineering Hangzhou Dianzi UniversityHangzhou 310018, P. R. ChinaZheji College of Telecommunication Engineering Hangzhou Dianzi UniversityHangzhou 310018, P. R. China College of Telecommunication Engineering Hangzhou Dianzi University Hangzhou 310018, P. R. China Zhe

会议类型: 国际会议

会议名称: 2011年无线通信与信号处理国际会议(WCSP 2011)

会议地点: 南京

会议语种:英文

页码: 1-5

在线出版日期: 2011-11-09（万方平台首次上网日期，不代表论文的发表时间）