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