Deep Multimodal Learning: Merging Sensory Data for Massive MIMO Channel Prediction

Yuwen Yang, Feifei Gao, Chengwen Xing, Jianping An, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications.

Original languageEnglish (US)
Article number9277535
Pages (from-to)1885-1898
Number of pages14
JournalIEEE Journal on Selected Areas in Communications
Issue number7
StatePublished - Jul 2021


  • Deep multimodal learning (DML)
  • channel prediction
  • deep learning
  • massive MIMO
  • wireless communications

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications


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