Reinforcement learning based resource allocation scheme for vehicular communication in 5G networks for smart cities

With an emphasis on improving energy efficiency (EE) and lowering power consumption of rapidly growing connected vehicles and infrastructures, Vehicle-to-Everything (V2X) communication is emerging as a fundamental element in the development of smart cities. This paper introduces an innovative reinforcement learning (RL)-based method for dynamic resource allocation within 5G-enabled V2X networks, focusing on EE and minimizing power consumption. The suggested framework adeptly modifies transmission power, and spectrum allocation in real-time, responding to fluctuating traffic patterns and network demands. By facilitating ongoing learning and decision-making, the RL system guarantees optimal resource utilization while preserving high-quality service and low-latency communication. Q-learning is employed to dynamically regulate power levels in urban vehicular scenarios, taking Doppler shift, user mobility, and changing traffic conditions into account. Experimental evaluations demonstrate a substantial decrease in power consumption and an improvement in network efficiency providing a sustainable solution for smart mobility initiatives, promoting the advancement of greener, more reliable, and energy-efficient urban transportation systems.