Deep Reinforcement Learning-Based Beam Training for Spatially Consistent Millimeter Wave Channels

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The fifth generation wireless systems are starting to exploit the large bandwidths available in the millimeter-wave (mmWave) spectrum to provide high data rates. The exploitation of mmWave requires the use of compact antenna arrays with hundreds of antenna elements, which leads to very directional beam patterns. The beams at both the transmitter and the receiver are trained periodically to maintain accurate beam alignments. The trade-off between the training overhead and the achievable data rate must be considered. In this paper, we propose an adaptive beam training algorithm using deep reinforcement learning for tracking dynamic mmWave channels. Based on the patterns learnt from historical data, the proposed algorithm can sense the changes in the environment and switch between different beam training methods so that a high data rate can be achieved with a minimum amount of beam training.
Original languageEnglish
Title of host publicationIEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages579-584
ISBN (Electronic)978-1-7281-7586-7
ISBN (Print)978-1-7281-7587-4
DOIs
Publication statusPublished - 13 Sept 2021
Event2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications - Virtual Conference
Duration: 13 Sept 202116 Sept 2021
https://pimrc2021.ieee-pimrc.org/

Publication series

Name
ISSN (Print)2166-9570
ISSN (Electronic)2166-9589

Symposium

Symposium2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
Abbreviated titlePIMRC 2021
Period13/09/2116/09/21
Internet address

Keywords / Materials (for Non-textual outputs)

  • Beam training
  • millimeter wave
  • deep reinforcement learning

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