A Novel Security Defense and Economic Assessment Algorithm for mmWave‐Vehicular Network Based on Deep Reinforcement Learning

This article proposes a novel algorithm to address the security issues in millimeter‐wave Internet‐of‐vehicles (mmWave‐IoV). The main idea is to provide a new solution to eliminate eavesdropping in dynamic mmWave‐IoV infrastructure. For this purpose, a secure multiagent cooperative communication algorithm based on deep deterministic policy gradient (DDPG) and dueling double deep Q network (D3QN) is proposed. The eavesdropper reception signal quality is reduced by using the cooperative jamming of the road side unit (RSU). The total secrecy rate of all authentic vehicles is used as the optimization problem with the objective to maximize it using the jamming RSUs, joint beam connections of vehicular users and base station, and the transmit power and jamming direction of cooperative RSUs. A real‐time, continuous, and discrete fusion‐based decision‐making strategy is deployed by creating an RSU agent utilizing the capabilities of the DDPG‐D3QN algorithm and a vehicular user agent used in D3QN. Simulation results show that the proposed algorithm has superior performance as compared with existing algorithms.