LAMANet: A Real-Time, Machine Learning-Enhanced Approximate Message Passing Detector for Massive MIMO

Abstract

Model-driven machine learning for signal detection in the physical layer of mobile communication systems combines well-known detector structures with learned parameters. Recent work has shown high detection performance in massive multiple-input–multiple-output (MIMO) detection; however, thorough complexity analysis and real-time processing hardware are lacking. This work proposes a novel machine learning enhanced approximate message passing (AMP) algorithm named LAMANet and its hardware implementation. The algorithm solves some major challenges of previous proposals such as the complete loss of performance in untrained detectors and the still high computational complexity compared to traditional massive MIMO detection methods. We provide a comprehensive complexity comparison, simulations of the symbol error rate (SER) performance over realistic channel models, and a field-programmable gate array (FPGA) implementation capable of processing LAMANet in real time. The results show a similar detection performance of LAMANet to previous machine learning-enhanced algorithms, while the computational effort is reduced to a level where real-time computation in hardware becomes comparable to traditional detection methods.