Improving Massive MIMO Message Passing Detectors With Deep Neural Network

Abstract

In this paper, deep neural network (DNN) is utilized to improve message passing detectors (MPDs) for massive multiple-input multiple-output (MIMO) systems. A general framework to construct DNN architecture for MIMO detection is first introduced by unfolding iterative MPDs. DNN MIMO detectors are then proposed based on modified MPDs including damped belief propagation (BP), max-sum (MS) BP, and simplified channel hardening-exploiting message passing (CHEMP). The correction factors are optimized via deep learning for better performance. Numerical results demonstrate that, compared with state-of-the-art (SOA) detectors including minimum mean-squared error (MMSE), BP, and CHEMP, the proposed DNN detectors can achieve better bit-error-rate (BER) and improve robustness against various antenna and channel conditions with similar complexity. The DNN is required to be trained only once and can be reused for multiple detections, which assures its high efficiency. The corresponding hardware architecture is also proposed. Implementation results with 65 nm CMOS technology approve the efficiency and flexibility of the proposed DNN framework.