Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions

Remi Bonnefoi 1, 2, 3, 4 Lilian Besson 1, 4, 5, 2, 3 Julio Manco-Vasquez 1, 2, 3, 4 Christophe Moy 1, 2
5 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : In this paper, we propose and evaluate different learning strategies based on Multi-Arm Bandit (MAB) algorithms. They allow Internet of Things (IoT) devices to improve their access to the network and their autonomy, while taking into account the impact of encountered radio collisions. For that end, several heuristics employing Upper-Confident Bound (UCB) algorithms are examined, to explore the contextual information provided by the number of retransmissions. Our results show that approaches based on UCB obtain a significant improvement in terms of successful transmission probabilities. Furthermore, it also reveals that a pure UCB channel access is as efficient as more sophisticated learning strategies.
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Submitted on : Tuesday, February 26, 2019 - 4:29:14 PM
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  • HAL Id : hal-02049824, version 1
  • ARXIV : 1902.10615



Remi Bonnefoi, Lilian Besson, Julio Manco-Vasquez, Christophe Moy. Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions. The 1st International Workshop on Mathematical Tools and technologies for IoT and mMTC Networks Modeling, Philippe Mary; Samir Perlaza; Petar Popovski, Apr 2019, Marrakech, Morocco. ⟨hal-02049824v1⟩



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