Learning Based and Physical-layer Assisted Secure Computation Offloading in Vehicular Spectrum Sharing Networks

Massive computing tasks have been generated with the widespread applications of big data analysis in vehicular edge computing (VEC) networks. However, the offloading process of the VEC networks suffers a threat of information leakage. The physical layer security (PLS) technology is an effective security solution to protect confidential information. Furthermore, the contradiction between massive data transmission and limited communication resources promotes an urgent need for a proper scheme to improve resource utilization. In this paper, we design a joint secure offloading and resource allocation (SoRA) scheme based on PLS technology and spectrum sharing architecture. We aim at minimizing the system processing delay of all vehicular users (VUs) while ensuring the security of information, by jointly optimizing the spectrum access, transmit power and computing resource allocation. Then we adopt a multi-agent deep reinforcement learning algorithm to solve the optimization problem. With proper training, we demonstrate that the VU agents can successfully cooperate to improve the system processing delay and ensure the security of the offloading process.