Learning the CSI Denoising and Feedback Without Supervision

TL;DR

This paper introduces a novel learning-based method for denoising and compressing channel state information in frequency division duplex systems, enabling efficient feedback without supervision or training at mobile terminals.

Contribution

It presents a joint denoising and feedback framework that learns from noisy uplink data to improve downlink channel estimation and feedback without requiring terminal training.

Findings

High denoising and compression performance demonstrated in simulations

Method generalizes from uplink to downlink data effectively

No training needed at mobile terminals

Abstract

In this work, we develop a joint denoising and feedback strategy for channel state information in frequency division duplex systems. In such systems, the biggest challenge is the overhead incurred when the mobile terminal has to send the downlink channel state information or corresponding partial information to the base station, where the complete estimates can subsequently be restored. To this end, we propose a novel learning-based framework for denoising and compression of channel estimates. Unlike existing studies, we extend a recently proposed approach and show that based solely on noisy uplink data available at the base station, it is possible to learn an autoencoder neural network that generalizes to downlink data. Subsequently, half of the autoencoder can be offloaded to the mobile terminals to generate channel feedback there as efficiently as possible, without any training effort at the terminals or corresponding transfer of training data. Numerical simulations demonstrate the excellent performance of the proposed method.