arXiv subjects

Jean-Christophe Sibel

Publications and source records attributed to Jean-Christophe Sibel.

4 recordsLinked to original sources

arXiv2024

Optimal Sensing Policy With Interference-Model Uncertainty

This paper considers a half-duplex scenario where an interferer behaves according to a parametric model but the values of the model parameters are unknown. We explore the necessary number of sensing steps to gather sufficient knowledge about the interferer's behavior. With more sensing steps, the reliability of the model-parameter estimates is improved, thereby enabling more effective link adaptation. However, in each time slot, the communication system experiencing interference must choose between sensing and communication. Thus, we propose to investigate the optimal policy for maximizing the expected sum communication data rate over a finite-time communication. This approach contrasts with most studies on interference management in the literature, which assume that the parameters of the interference model are perfectly known. We begin by showing that the problem under consideration can be modeled within the framework of a Markov decision process (MDP). Following this, we demonstrate that both the optimal open-loop and optimal closed-loop policies can be determined with reduced computational complexity compared to the standard backward-induction algorithm.

cs.IT↗

arXiv2023

An MDP approach for radio resource allocation in urban Future Railway Mobile Communication System (FRMCS) scenarios

In the context of railway systems, the application performance can be very critical and the radio conditions not advantageous. Hence, the communication problem parameters include both a survival time stemming from the application layer and a channel error probability stemming from the PHY layer. This paper proposes to consider the framework of Markov Decision Process (MDP) to design a strategy for scheduling radio resources based on both application and PHY layer parameters. The MDP approach enables to obtain the optimal strategy via the value iteration algorithm. The performance of this algorithm can thus serve as a benchmark to assess lower complexity schedulers. We show numerical evaluations where we compare the value iteration algorithm with other schedulers, including one based on deep Q learning.

cs.IT↗

arXiv2023

Minimizing the Outage Probability in a Markov Decision Process

Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain. We propose an algorithm which enables to optimize an alternative objective: the probability that the gain is greater than a given value. The algorithm can be seen as an extension of the value iteration algorithm. We also show how the proposed algorithm could be generalized to use neural networks, similarly to the deep Q learning extension of Q learning.

cs.LG↗

arXiv2023

An application-oriented scheduler

We consider a multi-agent system where agents compete for the access to the radio resource. By combining some application-level parameters, such as the resilience, with a knowledge of the radio environment, we propose a new way of modeling the scheduling problem as an optimization problem. We design accordingly a low-complexity solver. The performance are compared with state-of-the-art schedulers via simulations. The numerical results show that this application-oriented scheduler performs better than standard schedulers. As a result, it offers more space for the selection of the application-level parameters to reach any arbitrary performance.

cs.IT↗