Enhancing Spectral Efficiency in Vehicular Optical Camera Communications Using Multi-Agent Deep Reinforcement Learning

Vehicular communication systems are very important for modern transportation. These systems allow vehicles to share data and improve road safety. Optical Camera Communication (OCC) is a new method that helps vehicles communicate using visible light. This method has many advantages over traditional Radio Frequency (RF) communication. It offers a larger spectrum, lower cost and better security. This paper focuses on optimizing spectral efficiency in vehicular OCC. It proposes a new approach using multi-agent Deep Reinforcement Learning (DRL). This method helps vehicles decide their speed and modulation to maximize spectral efficiency. The system also ensures a low Bit Error Rate (BER) and ultra-low latency. The main goal is to find the best modulation order and vehicle speed to increase spectral efficiency. This needs to be done while maintaining reliability and low latency. The problem is difficult to solve with traditional methods. The reason is that it is a mixed-integer programming problem with nonlinear constraints. A solution is proposed using Reinforcement Learning (RL). In this case, each vehicle acts as an autonomous agent. The vehicles learn the best way to adjust their speed and modulation order. This is done using a technique called Q-learning. However, since the problem is large and complex, DRL is used to improve learning efficiency. This paper presents a new way to improve spectral efficiency in vehicular OCC. It uses DRL to optimize speed and modulation order. The system meets reliability and latency constraints. The results show that this method is more effective than existing approaches.