Open Access

REVIEW

Survey on AI-Enabled Resource Management for 6G Heterogeneous Networks: Recent Research, Challenges, and Future Trends

Hayder Faeq Alhashimi¹, Mhd Nour Hindia¹, Kaharudin Dimyati^1,*, Effariza Binti Hanafi¹, Feras Zen Alden², Faizan Qamar³, Quang Ngoc Nguyen^4,5,*

1 Centre of Advanced Communication, Research and Innovation (ACRI), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
2 School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi Mara, Shah Alam, 40450, Malaysia
3 Centre for Cyber Security, Faculty of Information Science and Technology (FTSM), Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Malaysia
4 Faculty of Science and Engineering, Waseda University, Tokyo, 169-8555, Japan
5 Faculty of Telecommunications, Posts and Telecommunications Institute of Technology, Hanoi, 11518, Vietnam

* Corresponding Authors: Kaharudin Dimyati. Email: ; Quang Ngoc Nguyen. Email:

(This article belongs to the Special Issue: AI and Advanced High-Tech Research and Development)

Computers, Materials & Continua 2025, 83(3), 3585-3622. https://doi.org/10.32604/cmc.2025.062867

Received 30 December 2024; Accepted 02 April 2025; Issue published 19 May 2025

Abstract

The forthcoming 6G wireless networks have great potential for establishing AI-based networks that can enhance end-to-end connection and manage massive data of real-time networks. Artificial Intelligence (AI) advancements have contributed to the development of several innovative technologies by providing sophisticated specific AI mathematical models such as machine learning models, deep learning models, and hybrid models. Furthermore, intelligent resource management allows for self-configuration and autonomous decision-making capabilities of AI methods, which in turn improves the performance of 6G networks. Hence, 6G networks rely substantially on AI methods to manage resources. This paper comprehensively surveys the recent work of AI methods-based resource management for 6G networks. Firstly, the AI methods are categorized into Deep Learning (DL), Federated Learning (FL), Reinforcement Learning (RL), and Evolutionary Learning (EL). Then, we analyze the AI approaches according to optimization issues such as user association, channel allocation, power allocation, and mode selection. Thereafter, we provide appropriate solutions to the most significant problems with the existing approaches of AI-based resource management. Finally, various open issues and potential trends related to AI-based resource management applications are presented. In summary, this survey enables researchers to understand these advancements thoroughly and quickly identify remaining challenges that need further investigation.

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Keywords

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