Joint Task Offloading and Resource Allocation for Integrated VLC and Sensing in Digital Twin-Aided Vehicular Edge Computing Networks

Recently, integrated visible light communication (VLC) and sensing has emerged as a promising technology for vehicular edge computing (VEC). As the explosive increase of latency-sensitive and computation-intensive vehicular applications, limited on-board computing resources are unable to satisfy diversified requirements. This paper proposes a digital twin-aided virtualization VEC network architecture, and studies joint task offloading and resource allocation for integrated VLC and sensing. To determine the associations between channels and subtasks, we first propose a communication-sensing task offloading mechanism, where vehicles send communication and sensing data to edge servers (ESs) via the street lamps, and ESs adaptively assign subtasks for various channels. Then, we formulate a joint task offloading and resource allocation optimization problem by minimizing the overall latency. To tackle the non-convex problem, we decouple it into a communication subproblem and a sensing subproblem. We then propose a successive convex approximation and alternating minimization algorithm and an alternating minimization algorithm to solve the two subproblems, respectively. Finally, an overall algorithm framework is proposed. The convergence and complexity analysis indicates that the proposed algorithms are convergent and time-efficient. Numerical results verify the superiority of the proposed algorithms. Moreover, the effects of key parameters on latency performance are discussed.