Multi-Agent Deep Reinforcement Learning in Vehicular OCC
This work addresses spectral efficiency optimization for autonomous vehicles using optical camera communications, representing an incremental improvement with a domain-specific focus.
The paper tackles the problem of optimizing spectral efficiency in vehicular optical camera communications by adapting modulation order and relative speed under bit error rate and latency constraints, achieving significantly higher sum spectral efficiency compared to baseline methods in simulations.
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.