As 6G networks develop, the need for fast, low-delay connections grows, making it important to manage the radio spectrum efficiently. Traditional static spectrum allocation fails to adapt to varying network conditions, leading to underutilization and congestion. This research addresses these challenges by proposing a dynamic spectrum management model called ML-DynSpec, which uses machine learning (ML) to adaptively allocate spectrum resources in real-time based on network conditions. The novelty of the proposed model lies in its ability to optimize spectrum utilization while enhancing network performance. Key parameters considered for evaluation include spectrum efficiency, latency, throughput, and network congestion. The simulation is conducted using MATLAB, using its ML toolbox to implement and test the proposed algorithm. Results show that ML-DynSpec improves spectrum utilization by 25%, reduces latency by 15%, and increases throughput by 20%, performing better than the static allocation methods. This work demonstrates that machine learning can optimize spectrum management in 6G networks, offering a scalable and adaptive solution to wireless systems.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: AH, MAS;
Methodology: AH, MAS;
Software: AH;
Data Curation: MAS;
Writing- Original Draft Preparation: AH;
Visualization: AH;
Supervision: AH, MAS;
Validation: AH, MAS;
Writing- Reviewing and Editing: AH, MAS;
Writing- Original Draft: AH, MAS; All authors reviewed the results and approved the final version of the manuscript.
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Authors thank Reviewers for taking the time and effort necessary to review the manuscript.
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This study was funded by the Seed Grant from Sri Eshwar College of Engineering, Coimbatore, India. (Grant Number: SECE/CFRD/2022-2023/SF/006).
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Cite this article
Anandakumar Haldorai and Muhammad Asim Saleem, "Dynamic Spectrum Management for 6G Networks Using Machine Learning based Adaptive Allocation", Journal of Future Networks and Communications, vol.1, no.1, pp. 001-011, January 2025. doi: XXXX/XXXX/JFNC202501001.
