西安电子科技大学学报（自然科学版）2018，Vol.45Issue(2)：13-18,25,7.DOI:10.3969/j.issn.1001-2400.2018.02.003
采用压缩感知的贝叶斯信道估计算法
Compressed sensing-based Bayesian channel estimation algorithm
摘要
Abstract
The high-order multiple-input multiple-output system can improve the energy efficiency and transmission reliability.However,it is difficult to perform channel estimation because of the large number of antennas.Although the SABMP (Support Agnostic Bayesian Matching Pursuit) algorithm can estimate the channel accurately,the complexity is too high.To address this issue,an EPMP (Expectation Prune Matching Pursuit) algorithm is proposed in the paper.At each sparsity level of the channel,an expanded support set is given by adding some positions corresponding to the atoms that have a larger inner product value with the current residual signal. Then the best support set is obtained by removing the wrong positions in the expanded support set.The estimated channel and the relative probability of the best support set at each sparse level are calculated.Finally,the expectation of the channel is calculated and regarded as the estimation of the channel.Compared with the SABMP algorithm,the EPMP algorithm can reduce the computational complexity while maintaining the estimation accuracy. The effectiveness of the EPMP algorithm is validated by simulation results.关键词
贝叶斯估计/压缩感知/稀疏重建/信道估计Key words
Bayesian estimation/compressed sensing/sparse reconstruction/channel estimation分类
信息技术与安全科学引用本文复制引用
吕治国,李颖..采用压缩感知的贝叶斯信道估计算法[J].西安电子科技大学学报（自然科学版）,2018,45(2):13-18,25,7.基金项目
国家自然科学基金资助项目(61671345) （61671345）
