A channel selection algorithm using reinforcement learning for mobile devices in massive IoT system

Honami Furukawa, Aohan Li, Yozo Shoji, Yoshito Watanabe, Song Ju Kim, Koya Sato, Yiannis Andreopoulos, Mikio Hasegawa

研究成果: Conference contribution査読

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728197944
DOI
出版ステータスPublished - 9 1 2021
イベント18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
継続期間: 9 1 202113 1 2021

出版物シリーズ

名前2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

Conference18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
国/地域United States
CityVirtual, Las Vegas
Period9/01/2113/01/21

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