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

arXiv: https://arxiv.org/abs/2506.16072 · HTML: https://mlsanity.com/paper/2506.16072/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 3 of 3)

- [Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management](https://mlsanity.com/paper/1705.09412/) (2017)
- [Rethinking WMMSE: Can Its Complexity Scale Linearly With the Number of BS Antennas?](https://mlsanity.com/paper/2205.06225/) (2022)
- [A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling](https://mlsanity.com/paper/2504.03721/) (2025)

## Built on by (most cited first, 1 of 1)

- [Deep Unfolded Fractional Optimization for Maximizing Robust Throughput in 6G Networks](https://mlsanity.com/paper/2602.06062/) (2026)

## Abstract (arXiv)

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.