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

• Published in: IEEE transactions on communications Vol. 70; no. 11; p. 1
• Main Authors: Van Huynh, Dang, Nguyen, Van-Dinh, Khosravirad, Saeed R., Sharma, Vishal, Dobre, Octavia A., Shin, Hyundong, Duong, Trung Q.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alternating optimisation; Computation offloading; Computational modeling; digital twin; Digital twins; Edge computing; industrial Internet-of-things; Internet of Things; Iterative algorithms; Iterative methods; mobile edge computing (MEC); Network latency; Optimization; Resource allocation; Resource management; Servers; Task analysis; Ultra reliable low latency communication; ultra-reliable and low latency communications (URLLC); Wireless networks
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2022.3205692

This paper addresses the problem of minimising latency in computation offloading with digital twin (DT) wire-less edge networks for industrial Internet-of-Things (IoT) environment via ultra-reliable and low latency communications (URLLC) links. The considered DT-aided edge networks provide a powerful computing framework to enable computation-intensive services, where the DT is used to model the computing capacity of edge servers and optimise the resource allocation of the entire system. The objective function is comprised of local processing latency, URLLC-based transmission latency and edge processing latency, subject to both communication and computation resources budgets. In this regard, the minimum latency is obtained by jointly optimising the transmit power, user association, offloading portions, the processing rate of users and edge servers. The formulated problem is highly complicated due to complex non-convex constraints and strong coupling variables. To deal with this computationally intractable problem, we propose an iterative algorithm which decomposes the original problem into three sub-problems and resolve this problem in the fashion of alternating optimisation approach combined with an inner convex approximation framework. Simulation results demonstrate the effectiveness of the proposed method in reducing the latency compared with other benchmark schemes.