A Lightweight RL-Driven Deep Unfolding Network for Robust WMMSE Precoding in Massive MU-MIMO-OFDM Systems

TL;DR

This paper introduces a lightweight reinforcement learning-based deep unfolding network that enhances WMMSE precoding in massive MU-MIMO-OFDM systems, improving robustness and efficiency under imperfect CSI.

Contribution

It develops a novel RL-driven deep unfolding network that maps each WMMSE iteration to a network layer, integrating approximation and domain-specific techniques for faster, more robust precoding.

Findings

Outperforms existing methods in ergodic weighted sum-rate under imperfect CSI

Achieves faster convergence and lower computational complexity

Demonstrates robustness and efficiency in large-scale MIMO-OFDM simulations

Abstract

Weighted Minimum Mean Square Error (WMMSE) precoding is widely recognized for its near-optimal weighted sum rate performance. However, its practical deployment in massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems is hindered by the assumption of perfect channel state information (CSI) and high computational complexity. To address these issues, we first develop a wideband stochastic WMMSE (SWMMSE) algorithm that iteratively maximizes the ergodic weighted sum-rate (EWSR) under imperfect CSI. Building on this, we propose a lightweight reinforcement learning (RL)-driven deep unfolding (DU) network (RLDDU-Net), where each SWMMSE iteration is mapped to a network layer. Specifically, its DU module integrates approximation techniques and leverages beam-domain sparsity as well as frequency-domain subcarrier correlation, significantly accelerating convergence and reducing computational overhead. Furthermore, the RL module adaptively adjusts the network depth and generates compensation matrices to mitigate approximation errors. Simulation results under imperfect CSI demonstrate that RLDDU-Net outperforms existing baselines in EWSR performance while offering superior computational and convergence efficiency.