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Network resource optimization with reinforcement learning for low power wide area networks

Authors
  • Park, Gyubong1
  • Lee, Wooyeob1
  • Joe, Inwhee1
  • 1 Department of Computer Science, Hanynag University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea , Seoul (South Korea)
Type
Published Article
Journal
EURASIP Journal on Wireless Communications and Networking
Publisher
Springer International Publishing
Publication Date
Sep 10, 2020
Volume
2020
Issue
1
Identifiers
DOI: 10.1186/s13638-020-01783-5
Source
Springer Nature
Keywords
License
Green

Abstract

As the 4th industrial revolution using information becomes an issue, wireless communication technologies such as the Internet of Things have been spotlighted. Therefore, much research is needed to satisfy the technological demands for the future society. A LPWA (low power wide area) in the wireless communication environment enables low-power, long-distance communication to meet various application requirements that conventional wireless communications have been difficult to meet. We propose a method to consume the minimum transmission power relative to the maximum data rate with the target of LoRaWAN among LPWA networks. Reinforcement learning is adopted to find the appropriate parameter values for the minimum transmission power. With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. The simulation results show that the proposed method is about 15% better than the existing ADR (adaptive data rate) MAX of LoRaWAN in terms of throughput relative to energy transmission.

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