Secure Transmission in Cellular V2X Communications Using Deep Q-Learning

Cellular vehicle-to-everything (V2X) communication is emerging as a feasible and cost-effective solution to support applications such vehicle platooning, blind spot detection, parking assistance, traffic management. To these features, an increasing number of sensors are being deployed along the road in form roadside objects. However, despite hype surrounding cellular V2X networks, practical realization networks still hampered by under-developed physical security solutions. solve issue wireless link security, we propose deep Q-learning-based strategy secure links. Since one main responsibilities base station (BS) manage interference network, ensured without compromising level network. The formulated problem considers both power constraints while maximizing secrecy rate vehicles. Subsequently, develop reward function Q-learning network for performing efficient allocation. simulation results obtained demonstrate effectiveness our proposed learning approach. provided here will provide strong basis future research efforts domain vehicular communications.