URLLC Edge Networks With Joint Optimal User Association, Task Offloading and Resource Allocation: A Digital Twin Approach

Abstract

This paper addresses the problem of minimizing latency in computation offloading within digital twin (DT) wireless edge networks tailored for industrial Internet-of-Things (IoT) environments, utilizing ultra-reliable and low-latency communication (URLLC) links. The proposed DT-aided edge networks establish a robust computing framework that facilitates computation-intensive services, where the digital twin is leveraged to model the computing capacity of edge servers and optimize overall resource allocation.

The objective function encompasses local processing latency, URLLC-based transmission latency, and edge processing latency, while adhering to constraints related to communication and computation resource budgets. To achieve minimum latency, the solution involves jointly optimizing transmit power, user association, offloading portions, and processing rates for both users and edge servers.

Given the complexity of the problem—characterized by non-convex constraints and strongly coupled variables—an iterative algorithm is proposed. This algorithm decomposes the original challenge into three sub-problems and resolves them using an alternating optimization approach, supplemented by an inner convex approximation framework.

Simulation results validate the effectiveness of the proposed method, demonstrating significant latency reductions compared to existing benchmark schemes.