Against Eavesdropping in Vehicular Networks: Joint Optimization of Power and Spectrum Based on Multi-Agent Reinforcement Learning

With the continuous development of vehicular networks, the security of in-vehicle data is receiving more and more attention. Different from traditional cryptographic techniques, the solution of physical layer security offers the advantages of lower algorithmic complexity and reduced computational demands on devices. This paper addresses the issue of resource allocation for vehicular networks in the presence of eavesdroppers. We formulate the joint optimization problem of power control and spectrum allocation under the quality of service requirements for vehicle-to-infrastructure (V2I) links with high capacity and vehicle-to-vehicle (V2V) links with low latency while ensuring the security of V2V transmission. To cope with the fast-changing channel state information in the high-mobility vehicular scenario, the optimization problem is transformed into a Markov decision process and solved by a multi-agent deep deterministic policy gradients (MADDPG) based deep reinforcement learning (DRL) algorithm. Simulation results show that the proposed MADDPG-based algorithm can effectively ensure high capacity of V2I links while ensuring the low latency of V2V secure transmission.