A Reinforcement Learning Based Medium Access Control Method for LoRa Networks

Huang，Xu; Jiang，Jie; Yang，Shuang Hua; Ding，Yulong

10.1109/ICNSC48988.2020.9238127

2020-10-30

978-1-7281-6856-2

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

1-6

30 Oct.-2 Nov. 2020

Nanjing, China

LoRa is a low-power long-range network technology, which is used widely in power sensitive and maintenance free Internet of Things applications. LoRa only defines the physical layer protocol, while LoRaWAN is a medium access control (MAC) layer protocol above it. However, simply using ALOHA in LoRaWAN makes a high package collision rate when the number of the end-devices in the network is large, since many end-devices will send the packages to gateway at the same time. To solve this, we present a reinforcement learning (RL) based multi access method for LoRaWAN, which allows end-devices decide when to transmit data based on the environment and reduce the package collision rate. A comparation between the RL method and ALOHA is also included in the paper, which shows that the RL method has a lower package collision rate.

decentralized LoRa multi-agent reinforcement learning

第一 ; 通讯

英语

EI

20204709520716

Multi agent systems ; Network layers ; Gateways (computer networks) ; Medium access control ; Low power electronics ; Learning systems

Data Communication, Equipment and Techniques:722.3 ; Computer Software, Data Handling and Applications:723 ; Artificial Intelligence:723.4

2-s2.0-85096352911

Scopus

Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Huang，Xu,Jiang，Jie,Yang，Shuang Hua,et al. A Reinforcement Learning Based Medium Access Control Method for LoRa Networks[C],2020:1-6.