NOMA and Hybrid Beamforming Aided Secure Computation Offloading for mmWave VEC Networks With Multi-Agent DRL

Mobile edge computing (MEC) meets the requirements of various delay-sensitive applications by providing high-speed computing services to a large number of user vehicles simultaneously. Nevertheless, the inherent open feature of wireless channels and the constraints of limited spectrum resources present significant challenges to achieving both secure offloading and high offloading rate simultaneously. Millimeter wave (mmWave) can provide user vehicles with abundant spectrum resources, but its short wavelength causes high path loss. In this paper, we utilize hybrid beamforming and non-orthogonal multiple access (NOMA) technologies to improve the offloading rate of user vehicles and to interfere with eavesdroppers, thus improving the security of the offloading process in mmWave vehicular edge computing (VEC) networks. We first use the K-means algorithm to cluster user vehicles. Then, we minimize the system delay by jointly optimizing the analog beamforming matrix, the user vehicle transmit power and the allocation ratio of the MEC server computation resource while ensuring the security of the offloading process. The above optimization problem is formulated as a Markov decision process (MDP) and a twin Delayed Deep Deterministic Policy Gradient (TD3)-Dueling Double Deep Q Network (D3QN) based multi-agent secure computation offloading scheme is proposed to solve the MDP problem. Simulation results demonstrate that the TD3-D3QN based multi-agent scheme is able to adapt to highly dynamic VEC networks when guaranteeing the security of the offloading process and low system delay.