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

TL;DR

This paper introduces a transformer-based foundation model for massive MIMO precoding that reduces energy consumption, adapts to user rate needs, and outperforms traditional methods with less complexity, even in data-scarce scenarios.

Contribution

The paper presents a novel foundation model for mMIMO precoding that minimizes energy use, adapts to user rates, and includes a data augmentation technique for limited data environments.

Findings

Zero-shot deployment outperforms zero forcing at equal energy.

Approaches weighted MMSE performance with 8x less complexity.

Data augmentation improves model adaptation in data-scarce settings.

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.