Title: Age-of-computing constrained fairness-aware energy efficient resource allocation in MEC-assisted HSRNs

Authors: Yeshen Li; Ke Xiong; Zhifei Zhang; Yingying Wu; Pingyi Fan

Addresses: Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China ' Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China ' Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China ' Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education, School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China ' Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

Abstract: This paper investigates the mobile edge computing (MEC)-assisted high-speed railway network (HSRN), where train users' tasks can be offloaded to the MEC server deployed at the ground base station (GBS). The age of computing (AoC) is used to measure the timeliness of users' task computation and the fairness-aware energy efficiency (FEE) is maximised by jointly optimising the task offloading ratio, channel selection factor, power allocation vector and the computing resource assignment vector at the MEC server subject to the constraints of AoC of the users' tasks. To tackle such a non-convex mixed integer nonlinear programming (MINLP) problem, we propose a heterogeneous multi-agent twin delayed deep deterministic policy gradient-based resource allocation (HMATD3-RA) algorithm with a designed FEE-AoC reward function that linearly combines the violation of AoC and the FEE. Simulation results show that HMATD3-RA achieves the highest FEE-AoC reward compared with baselines while revealing the trade-off between FEE and AoC.

Keywords: mobile edge computing; energy efficiency; age of computing; AoC; high-speed railway networks; HSRNs; multi-agent reinforcement learning.

DOI: 10.1504/IJAHUC.2026.154343

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.2, pp.79 - 90

Received: 30 Dec 2024
Accepted: 13 Oct 2025
Published online: 23 Jun 2026 *

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