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Publication Detail
A channel selection algorithm using reinforcement learning for mobile devices in massive IoT system
  • Publication Type:
    Conference
  • Authors:
    Furukawa H, Li A, Shoji Y, Watanabe Y, Kim SJ, Sato K, Andreopoulos Y, Hasegawa M
  • Publisher:
    IEEE
  • Publication date:
    11/03/2021
  • Published proceedings:
    2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
  • ISBN-13:
    9781728197944
  • Status:
    Published
  • Name of conference:
    2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC)
  • Print ISSN:
    2331-9852
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.
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