Network resource optimization with reinforcement learning for low power wide area networks
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RESEARCH
Open Access
Network resource optimization with reinforcement learning for low power wide area networks Gyubong Park† , Wooyeob Lee† and Inwhee Joe* *Correspondence: [email protected] † Gyubong Park and Wooyeob Lee contributed equally to this work. Department of Computer Science, Hanynag University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea
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. Keywords: LPWA, LoRa, Reinforcement learning, Resource optimization, DQN
1 Introduction As the 4th industrial revolution using information becomes an issue, information and communication technologies such as the IoT (Internet of Things), big data, and AI (artificial intelligence) have become popular around the world. In particular, the IoT is a very important part of the technology that generates information that is the basis of the 4th industrial revolution. As a result, the scale of the IoT will increase in the next few years, and the current wireless communication environment will not be able to meet the demands of society. It is necessary to research the Internet of Things to meet the demands of the future society. Even if the number of connected devices is large, there is an IoT environment in which the amount of data generated by each device is relatively small. This environment © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons lic
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