Multi-Agent Deep Reinforcement Learning in Vehicular OCC

TL;DR

This paper presents a multi-agent deep reinforcement learning approach to optimize spectral efficiency in vehicular optical camera communications, effectively balancing modulation, speed, and quality constraints for autonomous vehicles.

Contribution

It introduces a novel DRL-based method for spectral efficiency optimization in vehicular OCC, addressing the NP-hard problem with online adaptive solutions.

Findings

Achieves higher spectral efficiency than existing schemes

Demonstrates effectiveness through extensive simulations

Balances modulation, speed, and latency constraints successfully

Abstract

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.