Learning-assisted beam search for indoor mmWave networks

Yu Jia Chen, Wei Yuan Cheng, Li-Chun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

This paper proposes a learning-assisted beam search scheme for indoor millimeter wave (mmWave) networks with multi-base stations. Recently, directional antennas are often used to achieve the high data rates and compensate the high freespace loss in the mmWave frequency range. However, establishing reliable communication links with narrow beamwidth is a challenging task in indoor moving environments since the sector search space scales with device mobility and base station density. To tackle such an issue, we develop a multi-state Q-learning approach that incorporates the base station selection into the beam selection process. By exploiting the radio environment data from ray tracing simulation, the proposed learning approach can enable fast and reliable beam selection for different indoor environments and mobility patterns. Simulation results show that the proposed scheme outperforms the beam search schemes based on the existing exhaustive search approach and the original Q-learning approach in terms of beam search latency, link outage times, and aggregated throughput.

Original languageEnglish
Title of host publication2018 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages320-325
Number of pages6
ISBN (Electronic)9781538611548
DOIs
StatePublished - 29 May 2018
Event2018 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2018 - Barcelona, Spain
Duration: 15 Apr 201818 Apr 2018

Publication series

Name2018 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2018

Conference

Conference2018 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2018
CountrySpain
CityBarcelona
Period15/04/1818/04/18

Keywords

  • Beam search
  • MmWave networks
  • Q-learning
  • Sector sweep

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