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MBM中基于AMP的多用户检测
引用本文:宋玮.MBM中基于AMP的多用户检测[J].北京理工大学学报,2020,40(9):982-987.
作者姓名:宋玮
作者单位:北京理工大学 信息与电子学院, 北京 100081
摘    要:针对媒介调制(MBM)系统中低复杂度高精度的多用户检测需求,提出了一种基于近似消息传递(AMP)的多用户检测算法.由于MBM自身具有稀疏性,可利用压缩感知的稀疏信号重构方法进行多用户检测.在检测过程中,当观测矩阵满足独立同分布条件时,采用近似消息传递算法进行多用户检测可在保证高精度检测性能的同时进一步降低检测复杂度.同时,针对噪声方差未知的情况,所提出的算法中设计了利用期望最大方法进行估计噪声方差的步骤,从而更加契合实际场景.经仿真测试表明,所提出的基于AMP的多用户检测算法与传统多用户检测方法以及其他具有相似复杂度的多用户检测方法相比具有最佳的多用户检测性能. 

关 键 词:媒介调制    压缩感知    多用户检测    近似消息传递    期望最大
收稿时间:2020/2/29 0:00:00

Approximate Message Passing Based Multiuser Detection for Media Based Modulation
SONG Wei.Approximate Message Passing Based Multiuser Detection for Media Based Modulation[J].Journal of Beijing Institute of Technology(Natural Science Edition),2020,40(9):982-987.
Authors:SONG Wei
Institution:School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Abstract:To meet the demands, low complexity and high accuracy, of multi-user detection in media based modulation (MBM), an approximate message passing (AMP) based multi-user detection algorithm was proposed. Because of the sparsity of MBM, a sparse signal reconstruction method based on compressed sensing was used for multi-user detection. The method was arranged that, when the observation matrix satisfied the condition of independent and identical distribution in the process of multi-user detection, the AMP algorithm could guarantee the performance of high precision detection and further reduce the detection complexity. At the same time, in the case of unknown noise variance, setting up the steps of estimating noise variance based on expectation maximization method, the proposed algorithm was more suitable for the actual scene. The simulation results show that the proposed multi-user detection algorithm based on AMP can provide a best performance compared with traditional multi-user detection methods and other multi-user detection methods with similar complexity.
Keywords:media based modulation (MBM)  compressive sensing  multiuser detection  approximate message passing  expectation maximization
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