Federated learning for DL-CSI prediction in FDD massive MIMO systems

Weihao Hou, Jinlong Sun, Guan Gui, Tomoaki Ohtsuki, Ahmet M. Elbir, Haris Gacanin, Hikmet Sari

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, deep learning for predicting the downlink channel state information (DL-CSI) has been extensively studied. However, in some small cellular base stations (SBSs), a small amount of training data is insufficient to produce an excellent model for CSI prediction. Traditional centralized learning (CL) based method brings all the data together for training, which can lead to overwhelming communication overheads. In this work, we introduce a federated learning (FL) based framework for DL-CSI prediction, where the global model is trained at the macro base station (MBS) by collecting the local models from the edge SBSs. We propose a novel model aggregation algorithm, which updates the global model twice by considering the local model weights and the local gradients, respectively. Numerical simulations show that the proposed aggregation algorithm can make the global model converge faster and perform better than the compared schemes. The performance of the FL architecture is close to that of the CL-based method, and the transmission overheads are much fewer.

Original languageEnglish
Article number9435623
Pages (from-to)1810-1814
Number of pages5
JournalIEEE Wireless Communications Letters
Issue number8
Publication statusPublished - 2021 Aug


  • Centralized learning
  • Channel state information
  • Federated learning
  • Macro base station
  • Small cellular base station

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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