Slicing Resource Scheduling for 6G Green Cellular-Vehicular Networks: A Secrecy Oriented Energy-Efficient Approach

The 6G empowered Internet of connected vehicles (IoCVs) have paved the way to the autonomous driving era, where the ultra-low latency communication and ultra-reliable connections promote the quality of experience (QoE) for vehicle users. However, the high data traffic load and communication resource constraint pose a heavy burden to autonomous driving. Moreover, the energy consumption and secrecy data transmission are critical issues that influence the service efficiency of 6G green vehicular networks. To address the above challenges, in this paper, we present a secrecy oriented energy-efficient slicing resource scheduling framework in 6G green cellular-vehicular networks, with the objective of promoting the service efficiency for vehicle users. In specific, the IoCVs can connect to roadside units (RSUs) through vehicle-to-infrastructure links. We consider that non-internet of connected vehicles (N-IoCVs) and eavesdroppers coexist in the networks, where N-IoCVs can reuse the resource block non-orthogonally with IoCVs for information exchange, and the eavesdroppers may overhear the data transmission of IoCVs and N-IoCVs. To guarantee the requirement of quality of service and meet the green communication in autonomous driving, we design a joint energy efficiency and slicing resource scheduling problem subject to the data rate and secrecy capacity requirements. To address the formulated non-convex optimization problem, we transform the resource scheduling problem into an equivalent form, and exploit a vertical decomposition approach to solve it. Regarding each decomposed subproblem, we propose the corresponding algorithms to yield the optimal strategy for slicing resource scheduling. Finally, numerical results show the advantages of our proposed slicing resource scheduling scheme for vehicular networks compared to several benchmark algorithms.