1 research outputs found
Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions
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.Comment: The source code (MATLAB or Octave) used for the simula-tions and the
figures is open-sourced under the MIT License,
atBitbucket.org/scee\_ietr/ucb\_smart\_retran