On Performance of Deep Reinforcement Learning-based Listen-Before-Talk (LBT) Scheme

Minjeong Lee, Jaewook Lee, Hochan Lee, Taeyun Kim, Sangheon Pack

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

Abstract

Licensed assisted access (LAA) is a promising system to overcome the limited radio resource by sharing the unlicensed band with wireless local area networks (WLANs), and the listen-before-talk (LBT) scheme is a key technology for providing fairness between LAA and WLAN. Recently, deep reinforcement learning (DRL) has been investigated to improve the performance of LBT; however, such approaches assume that there is no processing delay and thus the optimal decision can be immediately done. In this paper, we evaluate the performance of the DRL-based LBT (DRL-LBT) scheme when different processing delays are considered for DRL. Evaluation results demonstrate that the throughput fairness index and the total throughput of DRL-LBT with the processing delay can be degraded up to by 9.4% and 10.0%, respectively, compared with an ideal case without any processing delay.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages72-75
Number of pages4
ISBN (Electronic)9781728191003
DOIs
Publication statusPublished - 2021 Jan 13
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 2021 Jan 132021 Jan 16

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
CountryKorea, Republic of
CityJeju Island
Period21/1/1321/1/16

Keywords

  • Deep Reinforcement Learning (DRL)
  • Licensed assisted access (LAA)
  • Listen-before-talk (LBT)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems

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