Channel state information (CSI) feedback is critical for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. Most conventional algorithms are based on compressive sensing (CS) and are highly dependent on the level of channel sparsity. To address the issue, a recent approach adopts deep learning (DL) to compress CSI into a codeword with low dimensionality, which has shown much better performance than the CS algorithms when feedback link is perfect. In practical scenario, however, there exists various interference and non-linear effect. In this article, we design a DL-based denoise network, called DNNet, to improve the performance of channel feedback. Numerical results show that the DL-based feedback algorithm with the proposed DNNet has superior performance over the existing algorithms, especially at low signal-to-noise ratio (SNR).
@article{arxiv.2004.07576,
title = {Deep Learning based Denoise Network for CSI Feedback in FDD Massive MIMO Systems},
author = {Hongyuan Ye and Feifei Gao and Jing Qian and Hao Wang and Geoffrey Ye Li},
journal= {arXiv preprint arXiv:2004.07576},
year = {2020}
}