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A Channel Selection Algorithm Using Reinforcement Learning for Mobile Devices in Massive IoT System

Furukawa, H; Li, A; Shoji, Y; Watanabe, Y; Kim, SJ; Sato, K; Andreopoulos, Y; (2021) A Channel Selection Algorithm Using Reinforcement Learning for Mobile Devices in Massive IoT System. In: Proceedings of the 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC). IEEE: Las Vegas, NV, USA. Green open access

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Abstract

It is necessary to develop an efficient channel selection method with low power consumption to achieve high communication quality for distributed massive IoT system. To this end, Ma et al. [1] proposed an autonomous distributed channel selection method based on the Tug-of-War (ToW) dynamics. The ToW-based method can achieve equivalent performance to UCB1-tuned [2], [3] with low computational complexity and power consumption, which is recognized as a best practice technique for solving multi-armed bandit (MAB) problems. However, Ref. [1] only considered fixed IoT devices with simplex communication.

Type: Proceedings paper
Title: A Channel Selection Algorithm Using Reinforcement Learning for Mobile Devices in Massive IoT System
Event: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
ISBN-13: 978-1-7281-9794-4
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CCNC49032.2021.9369474
Publisher version: https://doi.org/10.1109/CCNC49032.2021.9369474
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Performance evaluation, Power demand, Heuristic algorithms, Reinforcement learning, Mobile handsets, Computational complexity, Best practices
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10128612
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