This paper proposes an AI‐based light fidelity (Li‐Fi) and millimeter‐wave (mmWave) architecture to hastily mitigate most of the challenges in vehicle‐to‐vehicle (V2V) communication systems. Through traffic prediction based on machine learning technologies, the system enables proactive resource allocation, as well as intelligent frequency management in the unpredictable vehicle environment. Traffic forecast is realized by means of a simple linear extrapolation, based on the knowledge of drifting vehicles’ speed and position; optimization techniques based on Q‐learning algorithms allow for adaptive frequency assignment such as to reduce interference and increasing spectral efficiency. Simulation results show the desirable enhancements in communication quality, average SINR values around 25–30 dB, and communication delays less than 3–5 ms at medium traffic scenario. Hybrid Li‐Fi/mmWave infrastructure: up to 10 Gbps (Li‐Fi zones) and reliability reaching up to 100 m (mmWave zones). These observations suggest the promising direction of AI‐powered hybrid communication systems (HCSs) to improve vehicle networks for more intelligent and safe autonomous transportation landscape.
Kadhim et al. (Thu,) studied this question.