A SYSTEMATIC REVIEW OF RESOURCE ALLOCATION FOR 5G NETWORKS: CHALLENGES, METHODS AND FUTURE RESEARCH DIRECTIONS

Maniru Malami Umar; Ahmad Baita Garko; Hassan Umar Suru; Muhammad Nasir Sarki

doi:10.4314/njt.2026.5706

Authors

Maniru Malami Umar Abdullahi Fodiyo University of Science and Technology, Aliero, Kebbi State
Ahmad Baita Garko Department of Computer Science, Abdullahi Fodio University of Science and Technology, Aliero, Kebbi State. NIGERIA
Hassan Umar Suru Department of Computer Science, Abdullahi Fodio University of Science and Technology, Aliero, Kebbi State. NIGERIA
Muhammad Nasir Sarki Department of Mathematics, Abdullahi Fodio University of Science and Technology, Aliero, Kebbi State. NIGERIA

DOI:

https://doi.org/10.4314/njt.2026.5706

Keywords:

5G, Resource allocation, eMBB, mMTC, URLLC

Abstract

Fifth-generation (5G) networks are designed to support high data rates, low latency, and reliable communication for different service use cases, including enhanced Mobile Broadband (eMBB), massive Machine-Type Communication (mMTC), and ultra-Reliable Low-Latency Communication (URLLC). Each of these use cases imposes different Quality of Service (QoS) requirements, making efficient resource allocation (RA) a critical challenge in the dynamic and heterogeneous 5G environment. This paper presents a systematic review of existing RA schemes, grouping them into eMBB-based, mMTC-based, URLLC-based, and multi-use-case approaches. The review evaluates the techniques used in each category, their performance, and their limitations in meeting specific service demands. Some of the challenges identified after reviewing these existing RA schemes include unfair resource distribution, inflexible allocation strategies, energy inefficiency, and high computational complexity, particularly in learning-based models. The coexistence of multiple 5G use cases remains a significant unresolved issue. To address these gaps, the paper highlights future research directions that include adaptive and hybrid RA frameworks, finer intra-use-case differentiation, intelligent resource reclamation, and lightweight learning-based solutions suitable for real-time operation. This review also provides a useful reference for improving scalable, efficient, and fair resource allocation in 5G and beyond networks.

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