A Low Complexity Online Learning Approximate Message Passing Detector for Massive MIMO

Recent research has shown that in massive multiple-input multiple-output (MIMO) detection, the model-driven machine learning (ML) detection algorithm with online training method can adapt to channel variations in real application scenarios and has high detection performance. However, the on-device training hardware of the ML-enhanced detector has not yet been addressed in the current literature. In this article, the architecture for the targeted hardware is designed through optimization on both the algorithms and the hardware. We first introduce a magnitude-based pruning strategy and then propose efficient MMNet (EMMNet)-type algorithms. In the improved algorithms, several algorithmic transformations or approximations are incorporated to reduce computational complexity. For instance, the exponential operation is replaced by a linear fitting function, and division is converted into hardware-efficient shift and subtraction operations. Moreover, to improve energy efficiency, the EMMNet-type algorithms are quantized with fixed-point (FXP) data formats and adopt a hardware-friendly stochastic gradient descent (SGD)-momentum optimizer. Based on the proposed algorithms, a low-complexity and high-throughput training architecture with reusable units is developed, which can support modulations from QPSK to QAM64. Compared with the MMNet, the presented EMMNet detector exhibits a 48% reduction in the number of multiplications without sacrificing detection performance, demonstrating remarkable hardware efficiency.