Demonstrating Interoperable Channel State Feedback Compression with Machine Learning

TL;DR

This paper presents a practical, interoperable machine learning approach for channel state feedback compression in wireless networks, demonstrating real-world accuracy and throughput improvements without sharing ML models between devices and networks.

Contribution

It introduces a novel confidential training method for interoperable ML models, validated with prototype UEs and base stations in real-world scenarios.

Findings

ML-based feedback achieves high accuracy in channel reconstruction

Significant downlink throughput gains observed with ML feedback

No need for sharing ML models between user equipment and base stations

Abstract

Neural network-based compression and decompression of channel state feedback has been one of the most widely studied applications of machine learning (ML) in wireless networks. Various simulation-based studies have shown that ML-based feedback compression can result in reduced overhead and more accurate channel information. However, to the best of our knowledge, there are no real-life proofs of concepts demonstrating the benefits of ML-based channel feedback compression in a practical setting, where the user equipment (UE) and base station have no access to each others' ML models. In this paper, we present a novel approach for training interoperable compression and decompression ML models in a confidential manner, and demonstrate the accuracy of the ensuing models using prototype UEs and base stations. The performance of the ML-based channel feedback is measured both in terms of the accuracy of the reconstructed channel information and achieved downlink throughput gains when using the channel information for beamforming. The reported measurement results demonstrate that it is possible to develop an accurate ML-based channel feedback link without having to share ML models between device and network vendors. These results pave the way for a practical implementation of ML-based channel feedback in commercial 6G networks.