用轻量强化学习加速大规模多用户MIMO系统中的预编码计算。
A Lightweight RL-Driven Deep Unfolding Network for Robust WMMSE Precoding in Massive MU-MIMO-OFDM Systems
- 将迭代算法映射为网络层，结合波束域稀疏性与子载波相关性提速
- 在不完美信道信息下，性能优于现有方法且收敛更快
- 适合追求实时性与低延迟的5G/6G基站预编码场景
加权最小均方误差（WMMSE）预编码因近似最优加权和速率而广受认可，但在大规模多用户（MU）MIMO正交频分复用（OFDM）系统中受限于理想信道状态信息（CSI）假设与高计算复杂度。为此，我们首先提出一种宽带随机WMMSE（SWMMSE）算法，在不完美CSI下迭代最大化遍历加权和速率（EWSR）。基于此，提出一种轻量级强化学习驱动的深度展开网络（RLDDU-Net），将每次SWMMSE迭代映射为网络层。其深度展开模块融合近似技术，利用波束域稀疏性与频率域子载波相关性，显著加速收敛并降低计算开销。此外，强化学习模块自适应调整网络深度，并生成补偿矩阵以缓解近似误差。仿真结果表明，在不完美CSI条件下，RLDDU-Net在EWSR性能上优于现有基线，同时具备更优的计算效率与收敛速度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。