This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Shubham Tathe,
Amit Thombare,
Ganesh Sarak,
Abhishek Savalgi,
Vaibhav Godase,
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- UG Student, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
- Assistant Professor, Department of Electronics and Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India
Abstract
The rapid growth of connected devices, heterogeneous applications, immersive services, and intelligent edge systems has increased the complexity of resource management in fifth generation (5G) and emerging sixth generation (6G) wireless networks. Radio spectrum, transmit power, bandwidth, computing resources, network slices, and user association must be dynamically allocated while satisfying conflicting requirements for throughput, latency, reliability, fairness, and energy efficiency. Conventional optimization techniques can become computationally expensive as the number of users, cells, services, and decision variables increases. Artificial intelligence (AI) and machine learning (ML) provide data-driven mechanisms for predicting network conditions and selecting resource-allocation policies. This paper proposes an AI-based resource-allocation framework integrating network-state monitoring, feature extraction, supervised learning, deep reinforcement learning, and multi-objective optimization. The framework considers bandwidth allocation, power control, user association, spectrum allocation, and network-slice management as coordinated optimization tasks. Random Forest, XGBoost, Artificial Neural Network, and Deep Reinforcement Learning are considered as candidate approaches. Mathematical formulation based on weighted throughput, latency, energy efficiency, and fairness is developed. For methodological demonstration, illustrative simulation values are presented; these values are not claimed as measurements from a deployed 5G/6G network and must be replaced with actual experimental or simulation outputs before publication. The proposed framework provides a pathway toward adaptive and intelligent resource management suitable for heterogeneous 5G and future 6G networks.
Keywords: Artificial Intelligence; Machine Learning; Resource Allocation; Deep Reinforcement Learning; Spectrum Allocation.
References
1. Godase V, Pawar P, Nagane S, Kumbhar S. Automatic railway horn system using node MCU. Journal of Control & Instrumentation. 2024 Jan;15(1).
2. Godase V, Godase J. Diet prediction and feature importance of gut microbiome using machine learning. Evolution in Electrical and Electronic Engineering. 2024 Nov 6;5(2):214-9.
3. Jamadade VK, Ghodke MG, Katakdhond SS, Godase V. A comprehensive review on scalable Arduino radar platform for real-time object detection and mapping. Journal of Microprocessor and Microcontroller Research. 2025 May;2(2):1-2.
4. Salunkhe A, Pawar V, Pise P, Mule S, Survase A, Godase V, Zambre S. A review on real-time RFID-based smart attendance systems for efficient record management. Advance Research in Analog and Digital Communications. 2025 Aug;2(2):32-46.
5. Dhope V, Chavan A, Hadmode N, Godase V. Smart plant monitoring system. International Journal of Creative Research Thoughts (IJCRT). https://www. ijcrt. org. 2024.
6. Lin M, Zhao Y. Artificial intelligence-empowered resource management for future wireless communications: A survey. China Communications. 2020 Apr 7;17(3):58-77. 7. Omheni N, Koubaa H, Zarai F. Artificial intelligence for 5G and 6G networks: A taxonomy-based survey of applications, trends, and challenges. Technologies. 2025 Dec 1;13(12):559.
8. Abba Ari AA, Samafou F, Ndam Njoya A, Djedouboum AC, Aboubakar M, Mohamadou A. IoT‐5G and B5G/6G resource allocation and network slicing orchestration using learning algorithms. IET Networks. 2025 Jan;14(1):e70002.
9. Debbabi F, Rihab JM, Chaari L, Aguiar RL, Gnichi R, Taleb S. Overview of AI-based algorithms for network slicing resource management in B5G and 6G. In2022 International Wireless Communications and Mobile Computing (IWCMC) 2022 May 30 (pp. 330-335). IEEE.
10. Ibrahim SK, Abdulhussien SA, ALkargole HM, Qasim HH. Enhancing bandwidth allocation efficiency in 5G networks with artificial intelligence. Computers, Materials & Continua. 2025 Jul 30;84(3):5223-38.
11. Ioannou I, Christophorou C, Vassiliou V. AI/ML for D2D communication in 5G/6G networks and beyond: Concept, applications, challenges, and future directions. IEEE Open Journal of the Communications Society. 2026 May 18.
Journal of Telecommunication, Switching Systems and Networks
| Volume | 13 | |
| 03 | ||
| Received | 30/09/2026 | |
| Accepted | 05/10/2026 | |
| Published | 31/10/2026 | |
| Publication Time | 31 Days |