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Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms

Abstract : The use of Low Power Wide Area Networks (LPWANs) is growing due to their advantages in terms of low cost, energy efficiency and range. Although LPWANs attract the interest of industry and network operators, it faces certain constraints related to energy consumption, network coverage and quality of service. In this paper we demonstrate the possibility to optimize the performance of the LoRaWAN (Long Range Wide Area Network) technology, one of the most widely used LPWAN technology. We suggest that nodes use light-weight learning methods, namely, multi-armed bandit algorithms, to select the communication parameters (spreading factor and emission power). Extensive simulations show that such learning methods allow to manage the trade-off between energy consumption and packet loss much better than an Adaptive Data Rate (ADR) algorithm adapting spreading factors and transmission powers on the basis of Signal to Interference and Noise Ratio (SINR) values.
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Contributor : Patrick Maillé Connect in order to contact the contributor
Submitted on : Thursday, December 6, 2018 - 9:50:34 AM
Last modification on : Friday, January 21, 2022 - 3:10:26 AM
Long-term archiving on: : Thursday, March 7, 2019 - 12:59:52 PM


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Raouf Kerkouche, Réda Alami, Raphaël Féraud, Nadège Varsier, Patrick Maillé. Node-based optimization of LoRa transmissions with Multi-Armed Bandit algorithms. ICT 2018 : 25th International Conference on Telecommunications, Jun 2018, Saint Malo, France. pp.1-6, ⟨10.1109/ICT.2018.8464949⟩. ⟨hal-01946456⟩



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