Practical Denoising Autoencoder for CSI Feedback Without Clean Target in Massive MIMO Networks

Abstract

In this letter, we present a novel approach for denoising channel state information (CSI) feedback in massive multiple-input multiple-output (MIMO) cellular networks. Our method utilizes Deep Learning (DL) techniques to compress and remove noise from measured CSI. Traditional DL-based denoising requires pairs of noisy input and corresponding clean targets, which are impractical to obtain in real-world wireless networks. To address this challenge, we propose a training method of denoising autoencoder using pairs of noisy CSIs and practical data acquisition strategies. Extensive evaluations demonstrate the superior reconstruction performance of our method compared to a vanilla autoencoder and legacy codebook-based CSI feedback.