A Reinforcement Learning Based Collision Avoidance Mechanism to Superposed LoRa Signals in Distributed Massive IoT Systems

Takuma Onishi; Aohan Li; Song-Ju Kim; Mikio Hasegawa

doi:10.1587/comex.2021XBL0033

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A Reinforcement Learning Based Collision Avoidance Mechanism to Superposed LoRa Signals in Distributed Massive IoT Systems

Takuma Onishi, Aohan Li, Song-Ju Kim, Mikio Hasegawa

著者情報

キーワード: Massive IoT, Collision Avoidance, Reinforcement Learning, Multi-armed Bandit Algorithm, Successive Interference Cancellation

ジャーナルフリー早期公開

論文ID: 2021XBL0033

DOI https://doi.org/10.1587/comex.2021XBL0033

この記事には本公開記事があります。

The final version of this article is now available: Vol. 10 (2021), No. 5 pp. 289-294

詳細

抄録

For Massive IoT systems, various Low Power Wide Area (LPWA) systems have been developed and deployed, i.e., LoRa, SigFox, etc. In this paper, to avoid destructive collisions when multiple IoT LoRa signals simultaneously received in the same channel, we propose a Successive Interference Cancellation (SIC) based collision avoidance mechanism by accessing channel using reinforcement learning for distributed massive IoT systems. Simulation results show the effectiveness of our proposed mechanism in terms of Frame Success Rate (FSR). （85words）

責任著者(Corresponding author)

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