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

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

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.

Original languageEnglish
Title of host publication2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197944
DOIs
Publication statusPublished - 9 Jan 2021
Event18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
Duration: 9 Jan 202113 Jan 2021

Publication series

Name2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

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

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