Related papers: A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff

A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff

URL: http://arxiv.org/abs/2507.18587v1
Date: Thu, 24 Jul 2025 17:10:06 GMT
Title: A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff
Authors: Jérôme Emery, Ali Hasanzadeh Karkan, Jean-François Frigon, François Leduc-Primeau,
Abstract summary: We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-user rate requirements.<n>At equal energy consumption, zero-shot deployment of the proposed foundation model significantly outperforms zero forcing, and approaches weighted minimum mean squared error performance with 8x less complexity.<n>Our work enables the implementation of DL-based solutions in practice by addressing challenges of data availability and training complexity.
Score: 4.8310710966636545
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propagation environment. However, training such a model requires high-quality, local datasets at the deployment site, which are often difficult to collect. We propose a transformer-based foundation model for mMIMO precoding that seeks to minimize the energy consumption of the transmitter while dynamically adapting to per-user rate requirements. At equal energy consumption, zero-shot deployment of the proposed foundation model significantly outperforms zero forcing, and approaches weighted minimum mean squared error performance with 8x less complexity. To address model adaptation in data-scarce settings, we introduce a data augmentation method that finds training samples similar to the target distribution by computing the cosine similarity between the outputs of the pre-trained feature extractor. Our work enables the implementation of DL-based solutions in practice by addressing challenges of data availability and training complexity. Moreover, the ability to dynamically configure per-user rate requirements can be leveraged by higher level resource allocation and scheduling algorithms for greater control over energy efficiency, spectral efficiency and fairness.

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