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Optical camera communications (OCC) has emerged as a key enabling technologyfor the seamless operation of future autonomous vehicles. In this paper, weintroduce a spectral efficiency optimization approach in vehicular OCC.Specifically, we aim at optimally adapting the modulation order and therelative speed while respecting bit error rate and latency constraints. As theoptimization problem is NP-hard problem, we model the optimization problem as aMarkov decision process (MDP) to enable the use of solutions that can beapplied online. We then relaxed the constrained problem by employing Lagrangerelaxation approach before solving it by multi-agent deep reinforcementlearning (DRL). We verify the performance of our proposed scheme throughextensive simulations and compare it with various variants of our approach anda random method. The evaluation shows that our system achieves significantlyhigher sum spectral efficiency compared to schemes under comparison.

Multi-Agent Deep Reinforcement Learning in Vehicular OCC