A low-complexity AMP detection algorithm with deep neural network for massive mimo systems  

作　　者：Zufan Zhang Yang Li Xiaoqin Yan Zonghua Ouyang 

机构地区：[1]School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]International College,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [3]College of Engineering,Informatics,and Applied Sciences,Northern Arizona University,Arizona,AZ86011,USA

出　　处：《Digital Communications and Networks》2024年第5期1375-1386,共12页数字通信与网络(英文版)

基　　金：supported by Major Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant No.KJZD-M201900601);China Postdoctoral Science Foundation(Grant No.2021MD703932);Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education,China(Grant No.cqupt-mct-202006)。

摘　　要：Signal detection plays an essential role in massive Multiple-Input Multiple-Output(MIMO)systems.However,existing detection methods have not yet made a good tradeoff between Bit Error Rate(BER)and computational complexity,resulting in slow convergence or high complexity.To address this issue,a low-complexity Approximate Message Passing(AMP)detection algorithm with Deep Neural Network(DNN)(denoted as AMP-DNN)is investigated in this paper.Firstly,an efficient AMP detection algorithm is derived by scalarizing the simplification of Belief Propagation(BP)algorithm.Secondly,by unfolding the obtained AMP detection algorithm,a DNN is specifically designed for the optimal performance gain.For the proposed AMP-DNN,the number of trainable parameters is only related to that of layers,regardless of modulation scheme,antenna number and matrix calculation,thus facilitating fast and stable training of the network.In addition,the AMP-DNN can detect different channels under the same distribution with only one training.The superior performance of the AMP-DNN is also verified by theoretical analysis and experiments.It is found that the proposed algorithm enables the reduction of BER without signal prior information,especially in the spatially correlated channel,and has a lower computational complexity compared with existing state-of-the-art methods.

关 键 词：Massive MIMO system Approximate message passing(AMP)detection algorithm Deep neural network(DNN) Bit error rate(BER) LOW-COMPLEXITY 

分 类 号：TN9[电子电信—信息与通信工程]