6 citations · 6 across the 4 of their papers we have counts for
6 papers
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…
Upper and Lower Bounds for the Ergodic Capacity of MIMO Jacobi Fading Channels
Amor Nafkha, Remi Bonnefoi
In multi-(core/mode) optical fiber communication, the transmission channel can be modeled as a complex sub-matrix of the Haar-distributed unitary matrix (complex Jacobi unitary ens…
New Lower Bound on the Ergodic Capacity of Optical MIMO Channels
Rémi Bonnefoi, Amor Nafkha
In this paper, we present an analytical lower bound on the ergodic capacity of optical multiple-input multiple-output (MIMO) channels. It turns out that the optical MIMO channel ma…
Closed-Form Expressions of Ergodic Capacity and MMSE Achievable Sum Rate for MIMO Jacobi and Rayleigh Fading Channels
Amor Nafkha, Nizar Demni
Multimode/multicore fibers are expected to provide an attractive solution to overcome the capacity limit of the current optical communication system. In the presence of high cross-…