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

研究成果: Article査読

10 被引用数 (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.

ジャーナルIEEE Wireless Communications Letters
出版ステータスPublished - 2021 8月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 電子工学および電気工学


「Federated learning for DL-CSI prediction in FDD massive MIMO systems」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。