Related papers: A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding

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

URL: http://arxiv.org/abs/2601.21897v1
Date: Thu, 29 Jan 2026 15:56:07 GMT
Title: A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding
Authors: Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon, François Leduc-Primeau,
Abstract summary: Massive multiple-input multiple-output (mMIMO) downlink precoding offers high spectral efficiency.<n>Existing deep learning (DL)-based solutions often lack robustness and require retraining for each deployment site.<n>This paper proposes a plug-and-play precoder (PaPP) with a backbone that can be trained for either fully digital (FDP) or hybrid beamforming (HBF) precoding.
Score: 2.3676623211643704
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Massive multiple-input multiple-output (mMIMO) downlink precoding offers high spectral efficiency but remains challenging to deploy in practice because near-optimal algorithms such as the weighted minimum mean squared error (WMMSE) are computationally expensive, and sensitive to SNR and channel-estimation quality, while existing deep learning (DL)-based solutions often lack robustness and require retraining for each deployment site. This paper proposes a plug-and-play precoder (PaPP), a DL framework with a backbone that can be trained for either fully digital (FDP) or hybrid beamforming (HBF) precoding and reused across sites, transmit-power levels, and with varying amounts of channel estimation error, avoiding the need to train a new model from scratch at each deployment. PaPP combines a high-capacity teacher and a compact student with a self-supervised loss that balances teacher imitation and normalized sum-rate, trained using meta-learning domain-generalization and transmit-power-aware input normalization. Numerical results on ray-tracing data from three unseen sites show that the PaPP FDP and HBF models both outperform conventional and deep learning baselines, after fine-tuning with a small set of local unlabeled samples. Across both architectures, PaPP achieves more than 21$\times$ reduction in modeled computation energy and maintains good performance under channel-estimation errors, making it a practical solution for energy-efficient mMIMO precoding.

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