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

研究成果: Article査読

抄録

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).

本文言語English
ジャーナルIEEE Transactions on Cognitive Communications and Networking
DOI
出版ステータスAccepted/In press - 2021

ASJC Scopus subject areas

  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信
  • 人工知能

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