Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength

Abstract

Next-generation wireless networks envision ubiquitous access and computational capabilities by seamlessly integrating aerial and terrestrial networks. Digital twin (DT) technology emerges as a proactive and cost-effective approach for resource-limited networks. Mobile edge computing (MEC) is pivotal in facilitating mobile offloading, particularly under the demanding constraints of ultra-reliable and low-latency communication (URLLC). This study proposes an advanced bisection sampling-based stochastic solution enhancement (BSSE) algorithm to minimize the system’s overall energy-time cost by jointly optimizing task offloading and resource allocation strategies. The formulated problem is a mixed-integer nonlinear programming problem due to its inherently combinatorial linkage with task-offloading decisions and strong correlation with resource allocation. The proposed algorithm operates iteratively through the following steps: 1) narrowing the search space through a one-climb policy, 2) developing a closed-form solution for optimal CPU frequency and transmit power, and 3) implementing randomized task offloading, which updates it in the direction of reducing objective value. The scalability of the proposed algorithm is also analyzed for a two-device model, which is subsequently extended to multiple devices. Comparative analysis against benchmark schemes reveals that our approach reduces total energy-time cost by 15.35% to 33.12% when weighting parameter $\partial ^{\lambda }_{k_{2}}$ is increased from 0.1 to 0.3, respectively.