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Conference Papers Year : 2021

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

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.

Dates and versions

hal-03393747 , version 1 (21-10-2021)

Identifiers

Cite

Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie S Gorce. Machine Learning-enhanced Receive Processing for MU-MIMO OFDM Systems. SPAWC 2021 - IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications, Sep 2021, Lucca, Italy. pp.1-4, ⟨10.1109/SPAWC51858.2021.9593152⟩. ⟨hal-03393747⟩
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