Deep Reinforcement Learning for Secure MEC Service in Vehicular Networks with Reconfigurable Intelligent Surfaces.

The broadcasting nature of wireless signals may result in the task offloading process of mobile edge computing (MEC) suffering serious information leakage. As a novel technology, physical layer security (PLS) combined with reconfigurable intelligent surfaces (RIS) can enhance transmission quality and security. This paper investigates the MEC service delay problem in RIS-aided vehicular networks under malicious eavesdropping. Due to the lack of an explicit formulation for the optimization problem, we propose a deep deterministic policy gradient (DDPG)-based communication scheme to optimize the secure MEC service. It aims to minimize the maximum MEC service time while reducing eavesdropping threats by jointly designing the RIS phase shift matrix and computing resource allocation in real-time. Simulation results demonstrate that 1) the DDPG-based scheme can help the base station make reasonable actions to realize secure MEC service in dynamic MEC vehicular networks; 2) deploying RIS can dramatically reduce eavesdropping threats and improve the overall MEC service quality.