2020/04/16 by Hongyuan Ye, Ye, Hongyuan, Feifei Gao +7 · 3 citations
Engineering · #Advanced MIMO Systems Optimization #Advanced Wireless Communication Techniques #FOS: Electrical engineering #Full-Duplex Wireless Communications #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.07576
openalex publication_date 2020/04/16 · openalex created_date 2020/04/24 · openalex updated_date 2026/07/28
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).