Joint Robust Power Control and Task Scheduling for Vehicular Offloading in Cloud-Assisted MEC Networks

Leveraging the abundance of computational resources, the cloud-edge collaborative architecture provide stronger data processing capabilities for vehicular networks, which not only significantly enhances the timeliness of offloading operations for delay-sensitive tasks but also substantially mitigates resource expenditure associated with non-delay-sensitive tasks. Addressing the communication scenarios characterized by diverse task types, this paper introduces cloud-assisted mobile-edge computing (C-MEC) networks, underscored by a novel optimization scheme. The scheme incorporates a utility function that is correlated with offloading delays during the transmission and computation phases, effectively balancing resource allocations and enhancing the operational efficiency of vehicular networks. However, the mobility of vehicles introduces channel uncertainty, adversely affecting the offloading stability of C-MEC networks. To develop a practical channel model, a first-order Markov process is employed, taking into account vehicular mobility. Additionally, probability constraints regarding co-channel interference are imposed on signal links to ensure the offloading quality. The Bernstein approximation method is utilized to transform the original interference constraints into a tractable form, and the Successive Convex Approximation (SCA) technique is meticulously applied to address the non-convex robust optimization problem. Furthermore, this paper proposes a robust iterative algorithm to ascertain optimal power control and task scheduling strategies. Numerical simulations are conducted to assess the effective of the proposed algorithm against benchmark methods, with a particular focus on robustness in task offloading and utility in resource allocation.