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Research Article |

A Multi-Agent Deep Reinforcement Learning Framework for MEC Resource Allocation in 5G Networks

Author(s): Meghamala Y1*, Pulipati John Paul2, and M Aravind Kumar3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 4

Publisher : FOREX Publication

Published : 30 December 2025

e-ISSN : 2347-470X

Page(s) : 909-919

DOI: https://doi.org/10.37391/IJEER.130431

Abstract

This paper introduces a multi-agent Deep Reinforcement Learning (DRL)-based model of allocating resources in 5G MEC networks based on the Soft Actor-Critic (SAC) algorithm and the hierarchical MATD3/TD2PG-based actor-critic network. The model distributes sub-channels, power of transmission and MEC computing resources with taking into account user mobility and isolation of the slices. The Python simulation is provided with a Manhattan 5G environment comprising of four interconnected gNodeBs, 5 densities of users (327, 499, 596, 930 and 1088 users), and two MEC classes of service (security and entertainment) with predefined bandwidth, memory, and processing requirements. It is assessed against three baselines: Greedy, Best-fit and Worst-fit allocation strategies in three measures; number of services served, services blocked and services denied. Findings indicate that SAC-based allocation improves the number of services served by 8-14, blocked by 15-22 and denied services by 18-20, respectively, with respect to user density. The advantages of these results support that the suggested multi-agent model, which is SAC-based, offers a measurable performance increase in the given dynamic traffic and heterogeneous service conditions.

Keywords: 5G, MEC, Resource Allocation, DRL, SAC Model.




Meghamala Y*, Dept of Electronics and communication Engineering, Bharatiya Engineering Science & Technology Innovation University (BESTIU), Andhra Pradesh, India; Email: meghamala7@gmail.com

Pulipati John Paul, Ellenki College of Engineering and Technology, Patancheru, Telangana, India; Email: jppulipati@yahoo.com

M Aravind Kumar, West Godavari Institute of Science and Engineering, Prakashraopalem, East Godavari, Andhra Pradesh, India; Email: drmaravindkumar@gmail.com

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Meghamala Y, Pulipati John Paul, M Aravind Kumar (2025), A Multi-Agent Deep Reinforcement Learning Framework for MEC Resource Allocation in 5G Networks. IJEER 13(4), 909-919. DOI: 10.37391/IJEER.130431.