AptBLE: Adaptive PHY Mode based on K-means Algorithm in Bluetooth5

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    Abstract

    Since Bluetooth5 standard released in 2016, its usage in commercial electronic products has been increased rapidly and substantially. Comparing to BLE 4.2, Bluetooth5 supports three PHY modes, respectively 2M, 1M and Coded PHY mode, providing a higher throughput and a wider range. Whereas there is a trade-off between its throughput and coverage. When the connection is established, the PHY mode is commonly pre-configured and fixed. This rigid design limits the flexibility in offering dynamic throughput and coverage. Therefore, we propose a method termed AptBLE, that switches the PHY mode in Bluetooth5 adaptively by considering the Received Signal Strength Indicator (RSSI) level. Specifically, we optimise the RSSI threshold for different PHY modes using the K-means clustering algorithm. Moreover, based on AptBLE, we further enable the Data Length Extension (DLE) feature and term the improved method as AptBLEM. We implement AptBLE (M) on the boards and test in indoor environment. The experimental results show that, AptBLE is more flexible, robust and outperforms the original fixed PHY mode in terms of throughput and transmission range. Furthermore, AptBLEM can triple the throughput than AptBLE, with a maximum throughput value in 1035Kbps and 42m range in indoor environment.
    Original languageEnglish
    Title of host publicationProceedings of 2021 Wireless Telecommunications Symposium (WTS)
    Number of pages6
    PublisherIEEE
    Publication date2021
    ISBN (Print)978-1-7281-8481-4
    DOIs
    Publication statusPublished - 2021
    Event2021 Wireless Telecommunications Symposium (WTS) - Virtuel event
    Duration: 22 Apr 202024 Apr 2020

    Conference

    Conference2021 Wireless Telecommunications Symposium (WTS)
    LocationVirtuel event
    Period22/04/202024/04/2020

    Keywords

    • Bluetooth5
    • Internet of Things
    • Machine learning algorithm
    • RSSI
    • Indoor measurement
    • Adaptive algorithm

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