2 citations · 3 across the 6 of their papers we have counts for
7 papers
Reinforcement Learning for Physical Layer Communications
Philippe Mary, Visa Koivunen, Christophe Moy
In this chapter, we will give comprehensive examples of applying RL in optimizing the physical layer of wireless communications by defining different class of problems and the poss…
Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation
Christophe Moy, Lilian Besson
This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning…
Upper-Confidence Bound for Channel Selection in LPWA Networks with Retransmissions
Remi Bonnefoi, Lilian Besson, Julio Manco-Vasquez +1
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…
GNU Radio Implementation of MALIN: "Multi-Armed bandits Learning for Internet-of-things Networks"
Lilian Besson, Remi Bonnefoi, Christophe Moy
We implement an IoT network the following way: one gateway, one or several intelligent (i.e., learning) objects, embedding the proposed solution, and a traffic generator that emula…
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings
Rémi Bonnefoi, Lilian Besson, Christophe Moy +2
Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use M…
Collaboration and Coordination in Secondary Networks for Opportunistic Spectrum Access
Wassim Jouini, Marco Di Felice, Luciano Bononi +1
In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since second…