Multi-Agent Deep Reinforcement Learning in Vehicular OCC

Autor:	Islam, Amirul, Musavian, Leila, Thomos, Nikolaos
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Information Theory Computer Science - Multiagent Systems Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison. Comment: Accepted in VTC2022-Spring
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.02672 Zobrazit plný text záznamu View this record from Arxiv