Multi-Rate Compression for Downlink CSI Based on Transfer Learning in FDD Massive MIMO Systems

Yuting Wang, Jinlong Sun, Jie Wang, Jie Yang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin

研究成果: Conference contribution

抄録

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

本文言語English
ホスト出版物のタイトル2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665413688
DOI
出版ステータスPublished - 2021
イベント94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
継続期間: 2021 9月 272021 9月 30

出版物シリーズ

名前IEEE Vehicular Technology Conference
2021-September
ISSN(印刷版)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
国/地域United States
CityVirtual, Online
Period21/9/2721/9/30

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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