Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

LoRa is an IoT communication technology that realizes ultra-long-distance transmission through spread spectrum modulation. However, its ultra-long-distance transmission also sacrifices the corresponding rate, and data conflicts are prone to occur when the number of nodes is large. In this article, w...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-10, Vol.2022, p.1-14
Main Authors: Zhong, Han, Ning, Lei, Wang, Junsong, Suo, Siliang, Chen, Liming
Format: Article
Language:English
Subjects:
Collision rates
Communication
Computer networks
Control algorithms
Deep learning
Energy consumption
Internet of Things
Nodes
Optimization
Optimization algorithms
Spread spectrum
Spread spectrum transmission
Wide area networks
Wireless networks
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Summary:LoRa is an IoT communication technology that realizes ultra-long-distance transmission through spread spectrum modulation. However, its ultra-long-distance transmission also sacrifices the corresponding rate, and data conflicts are prone to occur when the number of nodes is large. In this article, we investigate various types of data collisions in LoRa wireless work, most of which are affected by Spreading Factor (SF) assignment. At present, the distribution of the SF for LoRa in the industry is mostly based on Min-airtime and Min-distance. In the case of a large number of nodes, the data collision between nodes will increase sharply. This paper proposes a SF redistribution scheme under limited network resources, in order to improve the terminal capacity of the LoRa gateway. First, the problem of minimizing the data collision rate without expanding gateway or network resources is presented. Specifically, the reallocation of SF with increasing number of terminals is studied. Finally, considering the randomness of the data sent by the terminal, SF redistribution schemes based on deep reinforcement learning (DRL) are developed. The simulation results show that the collision rate of the proposed SF redistribution scheme is nearly 30% lower than Min-airtime and Min-distance, and its total energy consumption is close to Min-distance. Therefore, the proposed SF redistribution scheme can effectively improve the gateway capacity of LoRa wireless network.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/1690667