Deep Unfolding-based Detection for Quantized Massive MU-MIMO-OFDM Systems

This paper explores the difficulties of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) detection with low-precision quantization. To solve these problems, we propose QMMO-Net, a novel deep unfolding (DU)-based detection scheme that fuses the architecture specialized for quantized MIMOOFDM detection with data-driven techniques. To handle the severe distortions from coarse quantization, we add multiple trainable parameters to increase the model flexibility. With the help of the proposed differentiable proximal operator and DU tools, these parameters including a vector can be jointly optimized. Simulation results demonstrate that QMMO-Net outperforms traditional and DU-based detection algorithms in coarsely quantized MU-MIMO-OFDM systems. By combining the power of domain knowledge with data, our QMMO-Net has strong robustness to the non-linear effects of coarse quantization and the co-channel interference caused in high user load scenarios.