首页|Deep learning for joint channel estimation and feedback in massive MIMO systems

Deep learning for joint channel estimation and feedback in massive MIMO systems

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CSI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to recon-struct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different en-vironments and quantization errors.

Channel estimationCSI feedbackDeep learningMassive MIMOFDD

Jiajia Guo、Tong Chen、Shi Jin、Geoffrey Ye Li、Xin Wang、Xiaolin Hou

展开 >

National Mobile Communications Research Laboratory,Southeast University,Nanjing,210096,China

Department of Electrical and Electronic Engineering,Imperial College London,London,SW7 2AZ,UK

DOCOMO Beijing Communications Laboratories Co.,Ltd,Beijing,China

国家自然科学基金国家自然科学基金山东省重点研发计划Southeast University-China Mobile Research Institute Joint Innovation CenterScientific Research Foundation of Graduate School of Southeast University

61941104619210042020CXGC010108YBPY2118

2024

10.1016/j.dcan.2023.01.011

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(1)
  • 46