Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems

Jun Zeng, Jinlong Sun, Guan Gui, Bamidele Adebisi, Tomoaki Ohtsuki, Haris Gacanin, Hikmet Sari

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

Original languageEnglish
JournalIEEE Transactions on Cognitive Communications and Networking
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Deep transfer learning (DTL)
  • downlink CSI
  • FDD
  • limited feedback
  • massive MIMO
  • model-agnostic meta-learning (MAML).

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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