Reinforcement learning adaptive vertical handover scheme for hybrid VLC-IR networks in ship cabins

Jingtao Wu, Dahai Han, Min Zhang, Zabih Ghassemlooy

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

    2 Citations (Scopus)

    Abstract

    We propose an adaptive seamless vertical handover scheme for hybrid visible light communication-infrared (VLC-IR) networks in ship cabins based on Sarsa-lambda reinforcement learning algorithm. Compared with the Q-learning and Sarsa, the proposed algorithm surpasses the average downlink data rate by 13% and 14%, respectively. Moreover, the proposed algorithm outperforms the average downlink data rate of immediate handover and dwell handover schemes by 57% and 22%, respectively when the user device movement speed is 1 m/s.

    Original languageEnglish
    Title of host publication2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021
    PublisherVDE Verlag GmbH
    ISBN (Electronic)9781728174327
    DOIs
    Publication statusPublished - 6 Sept 2021
    Event17th International Symposium on Wireless Communication Systems, ISWCS 2021 - Berlin, Germany
    Duration: 6 Sept 20219 Sept 2021

    Publication series

    NameProceedings of the International Symposium on Wireless Communication Systems
    Volume2021-September
    ISSN (Print)2154-0217
    ISSN (Electronic)2154-0225

    Conference

    Conference17th International Symposium on Wireless Communication Systems, ISWCS 2021
    Country/TerritoryGermany
    CityBerlin
    Period6/09/219/09/21

    Keywords

    • Average downlink data rate
    • Sarsa-lambda algorithm
    • Ship cabins
    • Vertical handover
    • VLC-IR

    Fingerprint

    Dive into the research topics of 'Reinforcement learning adaptive vertical handover scheme for hybrid VLC-IR networks in ship cabins'. Together they form a unique fingerprint.

    Cite this