Awais, Muhammad and Pervaiz, Haris and Ni, Qiang and Yu, Wenjuan (2024) Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength. IEEE Transactions on Green Communications and Networking. p. 1. DOI https://doi.org/10.1109/tgcn.2024.3425442
Awais, Muhammad and Pervaiz, Haris and Ni, Qiang and Yu, Wenjuan (2024) Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength. IEEE Transactions on Green Communications and Networking. p. 1. DOI https://doi.org/10.1109/tgcn.2024.3425442
Awais, Muhammad and Pervaiz, Haris and Ni, Qiang and Yu, Wenjuan (2024) Task Dependency Aware Optimal Resource Allocation for URLLC Edge Network: A Digital Twin Approach Using Finite Blocklength. IEEE Transactions on Green Communications and Networking. p. 1. DOI https://doi.org/10.1109/tgcn.2024.3425442
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 resourcelimited 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 samplingbased stochastic solution enhancement (BSSE) algorithm to minimize the systemfs 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 ∂ λ k2 k2 is increased from 0.1 to 0.3, respectively.
Item Type: | Article |
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Uncontrolled Keywords: | Digital Twins; Edge Network; Resource Allocation; Task Dependency Aware; URLLC |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 16 Sep 2024 17:27 |
Last Modified: | 01 Nov 2024 11:23 |
URI: | http://repository.essex.ac.uk/id/eprint/39197 |
Available files
Filename: Accepted_Manuscript.pdf