Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems

TL;DR

This paper introduces a hybrid ML-based MU-MIMO OFDM receiver that enhances traditional processing by exploiting OFDM structure, leading to improved performance especially at high speeds.

Contribution

It proposes a novel hybrid receiver design that integrates ML components into conventional processing, improving demapping and channel error estimation without assuming perfect CSI.

Findings

Outperforms practical baselines in all tested scenarios.

Achieves significant gains at high mobility speeds.

Enhances specific receiver parts using ML while maintaining interpretability.

Abstract

Machine learning (ML) can be used in various ways to improve multi-user multiple-input multiple-output (MU-MIMO) receive processing. Typical approaches either augment a single processing step, such as symbol detection, or replace multiple steps jointly by a single neural network (NN). These techniques demonstrate promising results but often assume perfect channel state information (CSI) or fail to satisfy the interpretability and scalability constraints imposed by practical systems. In this paper, we propose a new strategy which preserves the benefits of a conventional receiver, but enhances specific parts with ML components. The key idea is to exploit the orthogonal frequency-division multiplexing (OFDM) signal structure to improve both the demapping and the computation of the channel estimation error statistics. Evaluation results show that the proposed ML-enhanced receiver beats practical baselines on all considered scenarios, with significant gains at high speeds.