Deep Transfer Learning-Based Downlink Channel Prediction for FDD Massive MIMO Systems

Yuwen Yang, Feifei Gao, Zhimeng Zhong, Bo Ai, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review

100 Scopus citations


Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.

Original languageEnglish (US)
Article number9175003
Pages (from-to)7485-7497
Number of pages13
JournalIEEE Transactions on Communications
Issue number12
StatePublished - Dec 2020


  • Deep transfer learning (DTL)
  • FDD
  • downlink CSI prediction
  • few-shot learning
  • massive MIMO
  • meta-learning

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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