Deep Learning-Based AMP for Massive MIMO Detection  

作　　者：Yang Yang Shaoping Chen Xiqi Gao 

机构地区：[1]Hubei Key Laboratory of Intelligent Wireless Communications,South-Central Minzu University,Wuhan 430074,China [2]National Mobile Communications Research Laboratory,Southeast University,Nanjing 210096,China

出　　处：《China Communications》2022年第10期69-77,共9页中国通信（英文版）

基　　金：supported by the National Natural Science Foundation of China under Grants 61801523, 61971452, and 91538203

摘　　要：Low-complexity detectors play an essential role in massive multiple-input multiple-output (MIMO) transmissions. In this work, we discuss the perspectives of utilizing approximate message passing (AMP) algorithm to the detection of massive MIMO transmission. To this end, we need to efficiently reduce the divergence occurrence in AMP iterations and bridge the performance gap that AMP has from the optimum detector while making use of its advantage of low computational load. Our solution is to build a neural network to learn and optimize AMP detection with four groups of specifically designed learnable coefficients such that divergence rate and detection mean squared error (MSE) can be significantly reduced. Moreover, the proposed deep learning-based AMP has a much faster converging rate, and thus a much lower computational complexity than conventional AMP, providing an alternative solution for the massive MIMO detection. Extensive simulation experiments are provided to validate the advantages of the proposed deep learning-based AMP.

关 键 词：approximate message passing CONVERGENCE machine learning 

分 类 号：TN929.5[电子电信—通信与信息系统] TP18[电子电信—信息与通信工程]