A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites

TL;DR

This paper introduces a low-complexity deep learning-based precoder for massive MIMO systems that generalizes well across different sites, achieving high sum-rate performance with significantly reduced computational complexity.

Contribution

It presents a novel meta-learning and teacher-student architecture for MIMO precoding that improves generalization and reduces complexity compared to traditional methods.

Findings

Achieves high sum-rate performance in unseen environments.

Reduces computational complexity by at least 73 times.

Outperforms WMMSE after fine-tuning across multiple sites.

Abstract

Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel deep learning-based mMIMO precoder to tackle the complexity challenges of existing approaches, such as weighted minimum mean square error (WMMSE), while leveraging meta-learning domain generalization and a teacher-student architecture to improve generalization across diverse communication environments. When deployed to a previously unseen site, the proposed model achieves excellent sum-rate performance while maintaining low computational complexity by avoiding matrix inversions and by using a simpler neural network structure. The model is trained and tested on a custom ray-tracing dataset composed of several base station locations. The experimental results indicate that our method effectively balances computational efficiency with high sum-rate performance while showcasing strong generalization performance in unseen environments. Furthermore, with fine-tuning, the proposed model outperforms WMMSE across all tested sites and SNR conditions while reducing complexity by at least 73$\times$.