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Review Article
Online Published: 06 Aug 2025
 


Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review

Opeyemi A. Ajibola, Samuel B. Ajibowu, Adewole A. Osobukola, Mubaraq O. Asafa, Joseph B. Samson, Surajudeen A. Babatunde.


Abstract
In wireless communication networks (WCNs), resource allocation has become an important task due to the varying nature of wireless networks. Conventional approaches for resource allocation always find it difficult to adapt to the complex nature and real-time variations of network conditions. Machine Learning (ML) has emerged as a promising technology to improve resource allocation leveraging data-driven insights and intelligent decision-making. This paper reviews various ML algorithms including Graph Neural Network (GNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), Federated Reinforcement Learning (FRL), and GNN-based DRL for allocating resources in wireless communication networks. The paper further discusses the applications of ML for WCNs resource allocation, the challenges in ML based resource allocation, and recommends prospective future directions.

Key words: Machine Learning, Resource Allocation, Wireless Communication, Federated Learning, Deep Reinforcement Learning


 
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How to Cite this Article
Pubmed Style

Ajibola OA, Ajibowu SB, Osobukola AA, Asafa MO, Samson JB, Babatunde SA. Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. Eu J Comp Sci Informatics. 2025; 2(4): 229-238. doi:10.5455/EJCSI.20250530124623

Web Style

Ajibola OA, Ajibowu SB, Osobukola AA, Asafa MO, Samson JB, Babatunde SA. Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. https://www.wisdomgale.com/ejcsi/?mno=261683 [Access: August 09, 2025]. doi:10.5455/EJCSI.20250530124623

AMA (American Medical Association) Style

Ajibola OA, Ajibowu SB, Osobukola AA, Asafa MO, Samson JB, Babatunde SA. Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. Eu J Comp Sci Informatics. 2025; 2(4): 229-238. doi:10.5455/EJCSI.20250530124623


Vancouver/ICMJE Style

Ajibola OA, Ajibowu SB, Osobukola AA, Asafa MO, Samson JB, Babatunde SA. Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. Eu J Comp Sci Informatics. (2025), [cited August 09, 2025]; 2(4): 229-238. doi:10.5455/EJCSI.20250530124623


Harvard Style

Ajibola, O. A., Ajibowu, . S. B., Osobukola, . A. A., Asafa, . M. O., Samson, . J. B. & Babatunde, . S. A. (2025) Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. Eu J Comp Sci Informatics, 2 (4), 229-238. doi:10.5455/EJCSI.20250530124623


Turabian Style

Ajibola, Opeyemi A., Samuel B. Ajibowu, Adewole A. Osobukola, Mubaraq O. Asafa, Joseph B. Samson, and Surajudeen A. Babatunde. 2025. Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. European Journal of Computer Sciences and Informatics, 2 (4), 229-238. doi:10.5455/EJCSI.20250530124623


Chicago Style

Ajibola, Opeyemi A., Samuel B. Ajibowu, Adewole A. Osobukola, Mubaraq O. Asafa, Joseph B. Samson, and Surajudeen A. Babatunde. "Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review." European Journal of Computer Sciences and Informatics 2 (2025), 229-238. doi:10.5455/EJCSI.20250530124623


MLA (The Modern Language Association) Style

Ajibola, Opeyemi A., Samuel B. Ajibowu, Adewole A. Osobukola, Mubaraq O. Asafa, Joseph B. Samson, and Surajudeen A. Babatunde. "Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review." European Journal of Computer Sciences and Informatics 2.4 (2025), 229-238. Print. doi:10.5455/EJCSI.20250530124623


APA (American Psychological Association) Style

Ajibola, O. A., Ajibowu, . S. B., Osobukola, . A. A., Asafa, . M. O., Samson, . J. B. & Babatunde, . S. A. (2025) Machine Learning Techniques for Resource Allocation In Wireless Communication Networks: a Review. European Journal of Computer Sciences and Informatics, 2 (4), 229-238. doi:10.5455/EJCSI.20250530124623


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