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

IEEE CCNC 2021 | Furukawa H, Li A, Shoji Y, Watanabe Y, Kim SJ, et al. | It is necessary to develop an efficient channel selection method with low power consumption to achieve high communication q...

9 January 2021

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

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.

Publication Type:Conference
Authors:Furukawa H, Li A, Shoji Y, Watanabe Y, Kim SJ, Sato K, Andreopoulos Y, Hasegawa M
Publication date:09/01/2021
Published  proceedings:2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
ISBN-13:9781728197944
Name of Conference:IEEE Consumer Communications & Networking Conference
Conference start date:09/01/2021
Conference end date:12/01/2021
Conference location:Las Vegas, Nevada,  USA (Virtual)
Status:Published
DOI:
http://dx.doi.org/10.1109/CCNC49032.2021.9369474
Full Text URL: 

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