Priority-Aware Joint Computational Offloading and Resource Allocation in NOMA-Assisted Vehicular Edge Computing
Pradeep Chennakesavula, Jen‐Ming Wu · 2024
Vehicular edge computing (VEC) is a crucial paradigm in B5G networks, aiming to offload computation-intensive and latency-sensitive tasks for vehicular users (VUs). However, not all tasks are time-critical; some are soft real-time (SRT), i.e., tasks with soft deadlines, and some are hard real-time (HRT), i.e., tasks with hard deadlines. Most of the prior studies primarily focus on either SRTs or HRTs and the realistic network with the culmination of both SRTs and HRTs with more priority to HRTs is needed to be explored more. In this paper, we investigate a non-orthogonal multiple access (NOMA) assisted VEC network and propose an offloading and resource allocation approach that considers the criticalness of task deadlines. To improve the prioritized service miss function, we propose joint computational offloading and resource allocation schemes under allowable power and computational resource constraints. This results in a mixed integer non-linear programming problem that is solved using continuous relaxation, successive convex approximation, and alternating optimization. Simulations are conducted to illustrate the efficacy of the proposed scheme.