Robust Beamforming for Multiuser MIMO Systems with Unknown Channel Statistics: A Hybrid Offline-Online Framework

TL;DR

This paper introduces a hybrid offline-online deep learning framework for robust beamforming in multiuser MIMO systems with unknown channel statistics, enabling rapid adaptation and improved performance under diverse and unseen channel conditions.

Contribution

It proposes a novel hybrid framework combining offline deep learning with online fine-tuning and meta-learning for robust beamforming without prior channel error statistics.

Findings

Outperforms state-of-the-art methods in robustness and accuracy.

Enables rapid online adaptation with minimal gradient updates.

Maintains high performance across diverse and non-stationary channels.

Abstract

Robust beamforming design under imperfect channel state information (CSI) is a fundamental challenge in multiuser multiple-input multiple-output (MU-MIMO) systems, particularly when the channel estimation error statistics are unknown. Conventional model-driven methods usually rely on prior knowledge of the error covariance matrix and data-driven deep learning approaches suffer from poor generalization capability to unseen channel conditions. To address these limitations, this paper proposes a hybrid offline-online framework that achieves effective offline learning and rapid online adaptation. In the offline phase, we propose a shared (among users) deep neural network (DNN) that is able to learn the channel estimation error covariance from observed samples, thus enabling robust beamforming without statistical priors. Meanwhile, to facilitate real-time deployment, we propose a sparse augmented low-rank (SALR) method to reduce complexity while maintaining comparable performance. In the online phase, we show that the proposed network can be rapidly fine-tuned with minimal gradient steps. Furthermore, a multiple basis model-agnostic meta-learning (MB-MAML) strategy is further proposed to maintain multiple meta-initializations and by dynamically selecting the best one online, we can improve the adaptation and generalization capability of the proposed framework under unseen or non-stationary channels. Simulation results demonstrate that the proposed offline-online framework exhibits strong robustness across diverse channel conditions and it is able to significantly outperform state-of-the-art (SOTA) baselines.