Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems✩

Dongjun Jung; Jong-Moon Chung; Hea-Sook Park

doi:10.1016/j.dcan.2025.07.002

›› 2026, Vol. 12 ›› Issue (4) :662 -676. DOI: 10.1016/j.dcan.2025.07.002

Research article

research-article

Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems^✩

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^a School of Electrical and Electronic Engineering, Yonsei University , Seoul 03722, South Korea

^b Electronics and Telecommunications Research Institute , Daejeon 34129, South Korea

^* E-mail addresses: dongjunjun@yonsei.ac.kr (D. Jung),

jmc@yonsei.ac.kr (J.-M. Chung),

parkhs@etri.re.kr (H.-S. Park).

Dongjun Jung received the B.S. degree in electrical and electronic engineering from Yonsei University, Seoul, Republic of Korea, in 2019, and the Ph.D degree in electrical and electronic engineering from Yonsei University in 2025. From 2019 to 2025, he was a research member with the Communications and Networking Laboratory (CNL), Yonsei University. He is currently a staff engineer with Samsung Electronics. His research interests include MECs, IoT, Wi-Fi, 5G & 6G mobile systems, augmented reality (AR) systems, military networks, reinforcement learning, game theory, large language model (LLM), and deep learning based optimization.

Jong-Moon Chung received B.S. and M.S. degrees in electronic engineering from Yonsei University and Ph.D. in electrical engineering from the Pennsylvania State University. Since 2005, he has been a professor in the School of Electrical and Electronic Engineering at Yonsei University, where he is also Associate Dean of the College of Engineering and professor of the Department of Emergency Medicine in the College of Medicine and Director of CNL. From 1997 to 1999, he was an assistant professor and instructor at the Pennsylvania State University. From 2000 to 2005, he was with the Oklahoma State University as a tenured associate professor. He is an IEEE Fellow, Vice President of the IEEE Consumer Technology Society (CTSoc) and Product Safety Engineering Society (PSES), Senior Editor of the IEEE Transactions on Consumer Electronics, Section Editor of the Wiley ETRI Journal, Chair Editor-in-Chief of the KSII Transactions on Internet and Information Systems, and served as an Editor of the IEEE Transactions on Vehicular Technology from 2011 to 2021.

Hea-Sook Park received the Ph.D. degree in computer science from the Department of Computer Engineering, Chungnam National University, Daejeon, Republic of Korea, in 2005. Since 1994, she has been a Principal Member of the Research Engineering Staff with the Electronics and Telecommunications Research Institute, Daejeon, where she is currently working on intelligent stealth networks in the Defense ICT Convergence Research Section. Her significant areas of research interests include network QoS and trustworthy IP networks.

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Abstract

In this paper, a multi-agent deep reinforcement learning-based joint resource allocation and task offloading control technique is proposed to minimize the energy consumption of user equipment (UE) supporting 5G ultra reliability low latency communications (URLLC) and enhanced mobile broadband (eMBB) services while meeting strict quality of service (QoS) requirements in 5G multi-radio access technology (RAT) networks. An optimization problem involving transmission power, channel resource, user association, offloading rate, and central processing unit (CPU) frequency is formulated using a queueing system-based mathematical design to support services with different characteristics while minimizing the energy consumption. It is proven in this paper that this problem is nondeterministic polynomial (NP) hard, in which multi-agent deep reinforcement learning (DRL) is used to solve the problem. To increase the learning efficiency and stability of deep reinforcement learning, prioritized experience replay (PER) and delayed target network and policy updates are applied. Simulation results show that the proposed scheme provides an improved energy consumption performance compared to the benchmarked schemes.

Keywords

Enhanced mobile broadband / Multi-agent deep reinforcement learning / Multi-access edge computing / Resource allocation / Ultra reliability low-latency communication

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Dongjun Jung, Jong-Moon Chung, Hea-Sook Park. Energy minimized joint resource allocation and task offloading with user association for URLLC and eMBB 5G multi-RAT systems^✩. , 2026, 12 (4) : 662-676 DOI:10.1016/j.dcan.2025.07.002

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CRediT authorship contribution statement

Dongjun Jung: Writing -- review & editing, Writing -- original draft, Visualization, Validation, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Jong-Moon Chung: Supervision, Project administration. Hea-Sook Park: Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Republic of Korea Government (MSIT, Development of Trust Inter-Networking Technology of Defense Mobile Environment for Real-Time Information Sharing) under Grant RS-2022-II220030.

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