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Cooperation for Spreading Factor Assignment in a Multioperator LoRaWAN Deployment
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DOI：10.1109/JIOT.2020.3031681.png)
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摘要
En 中文
Faced with the limitations of the Aloha random access scheme and spread spectrum techniques, LoRaWAN is yet to realize its potential as the flagship technology for large-scale Internet-of-Things applications. LoRaWAN allows for low power and long range communications. Nonetheless, concurrent transmissions on the same spreading factors (SFs), increased with the inevitable densification of device deployment, will lead to collisions and degradation in performance. The problem is further amplified due to the shortage in radio resources, with multiple operators utilizing the same unlicensed frequency bands. In this article, we investigate different interoperator cooperation schemes and devise multiple algorithms for SF assignment in a multioperator LoRaWAN deployment scenario. We start by proposing a proportional fair optimal formulation for the assignment with the objective of maximizing the logarithmic sum of the normalized throughput per SF. Under the assumption of partial operator cooperation, we propose a gradient ascent-based iterative algorithm for solving the SF assignment problem, and a game theory-based approach, wherein each network operator seeks to maximize its own normalized throughput. Finally, and with cooperation between different operators bound to be limited, we use recurrent neural networks to enable the prediction of the success rate per SF. This prediction allows the different operators to assign SFs with minimum cooperation. We simulate our proposals and compare them to the legacy LoRaWAN approach as well as others in the state of the art, highlighting the gains they produce in terms of total normalized throughput and packet delivery ratios.
Keyword:
Proposals
Logic gates
Internet of Things
Scalability
Recurrent neural networks
Throughput
Performance evaluation
Long range (LoRa)
LoRaWAN
long short-term memory (LSTM)
optimization
recurrent neural networks~(RNNs)
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期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
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