Deep Autoencoder Based CSI Feedback With Feedback Errors and Feedback Delay in FDD Massive MIMO Systems

被引：60
|
作者：
Jang, Youngrok [1 ]
Kong, Gyuyeol [1 ]
Jung, Minchae [1 ]
Choi, Sooyong [1 ]
Kim, Il-Min [2 ]
机构：
[1] Yonsei Univ, Sch Elect & Elect Engn, Seoul 03722, South Korea
[2] Queens Univ, Dept Elect & Comp Engn, Kingston, ON K7L 3N6, Canada
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2019年 / 8卷 / 03期
基金：
加拿大自然科学与工程研究理事会; 新加坡国家研究基金会;
关键词：
Autoencoder; CSI feedback; FDD massive MIMO; feedback delay; feedback errors; CAPACITY; CHANNELS;
D O I：
10.1109/LWC.2019.2895039
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this letter, we study the channel state information (CSI) feedback based on the deep autoencoder (AE) considering the feedback errors and feedback delay in the frequency division duplex massive multiple-input multiple-output system. We construct the deep AE by modeling the CSI feedback process, which involves feedback transmission errors and delays. The deep AE is trained by setting the delayed version of the downlink channel as the desired output. The proposed scheme reduces the impact of the feedback errors and feedback delay. Simulation results demonstrate that the proposed scheme achieves better performance than other comparable schemes.
引用
收藏
页码：833 / 836
页数：4
相关论文
共 50 条
  • [41] Global-Local Features Reconstruction Network for FDD Massive MIMO CSI Feedback
    Tan, Yuyang
    Tan, Weiqiang
    Guo, Jiajia
    Shi, Zheng
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2024, 13 (08) : 2255 - 2259
  • [42] Antenna Grouping Based Feedback Compression for FDD-Based Massive MIMO Systems
    Lee, Byungju
    Choi, Junil
    Seol, Ji-Yun
    Love, David J.
    Shim, Byonghyo
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2015, 63 (09) : 3261 - 3274
  • [43] A Scalable Framework for CSI Feedback in FDD Massive MIMO via DL Path Aligning
    Luo, Xiliang
    Cai, Penghao
    Zhang, Xiaoyu
    Hu, Die
    Shen, Cong
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (18) : 4702 - 4716
  • [44] Continuous Online Learning-Based CSI Feedback in Massive MIMO Systems
    Zhang, Xudong
    Wang, Jintao
    Lu, Zhilin
    Zhang, Hengyu
    IEEE COMMUNICATIONS LETTERS, 2024, 28 (03) : 557 - 561
  • [45] Adaptive Lightweight CNN-Based CSI Feedback for Massive MIMO Systems
    Jo, Sanguk
    So, Jaewoo
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (12) : 2776 - 2780
  • [46] Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback
    Erak, Omar
    Abou-Zeid, Hatem
    ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, 2023, : 1029 - 1035
  • [47] Massive MIMO CSI Feedback Based on Generative Adversarial Network
    Tolba, Bassant
    Elsabrouty, Maha
    Abdu-Aguye, Mubarak G.
    Gacanin, Haris
    Kasem, Hossam Mohamed
    IEEE COMMUNICATIONS LETTERS, 2020, 24 (12) : 2805 - 2808
  • [48] Efficient Lossless Feedback Compression for FDD Massive MIMO
    Ndiaye, Papis
    Diallo, Moussa
    Mbaye, Moustapha
    Diop, Idy
    Seye, Madoune Robert
    2019 15TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC), 2019, : 579 - 585
  • [49] Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity
    Lin, Yu-Chien
    Liu, Zhenyu
    Lee, Ta-Sung
    Ding, Zhi
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2021, 10 (10) : 2200 - 2204
  • [50] Unsupervised Online Learning in Deep Learning-Based Massive MIMO CSI Feedback
    Cui, Yiming
    Guo, Jiajia
    Wen, Chao-Kai
    Jin, Shi
    Han, Shuangfeng
    IEEE COMMUNICATIONS LETTERS, 2022, 26 (09) : 2086 - 2090
12345