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

• URL: http://arxiv.org/abs/2502.08757v1
• Date: Wed, 12 Feb 2025 20:02:36 GMT
• Title: A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites
• Authors: Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon, François Leduc-Primeau,
• Abstract summary: 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.
• Score: 5.896656636095934
• License:
• 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$\times$.

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