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Dynamic Bandwidth Allocation Scheme for Enhanced Performance in 5G Point-To-Point Networks
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This paper proposes a novel dynamic bandwidth allocation scheme for enhancing performance in 5G point-to-point networks. The scheme aims to optimize bandwidth utilization by dynamically allocating resources based on traffic demands and quality of service (QoS) requirements. Through continuous traffic monitoring, QoS analysis, and adaptive allocation algorithms, the scheme ensures optimal resource allocation in real-time. Additionally, load balancing techniques and a feedback mechanism further improve performance by distributing traffic evenly and incorporating user feedback. The proposed scheme contributes to the efficient utilization of available bandwidth resources, optimized QoS provisioning, and adaptation to changing network conditions, thereby enhancing the overall performance of 5G point-to-point networks.
Keywords
5G, Point-To-Point Networks, Dynamic Bandwidth Allocation, Performance Enhancement, Traffic Monitoring, Quality of Service, Resource Allocation Algorithm, Adaptive Allocation, Load Balancing, Feedback Mechanism.
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- I. Ahmad, W. Tan and H. Sun, “Latest Performance Improvement Strategies and Techniques used in 5G Antenna Designing Technology, a Comprehensive Study”, Micromachines, Vol. 13, pp. 717-736, 2022.
- H. Chen, Y. Tsai, C. Sim and C. Kuo, “Broadband 8-Antenna Array Design for Sub-6GHz 5G NR Bands Metal Frame Smartphone Applications”, IEEE Antennas and Wireless Propagation Letters, Vol. 19, No. 7, pp. 1078-1082, 2020.
- A. Thantharate and C. Beard, “ADAPTIVE6G: Adaptive Resource Management for Network Slicing Architectures in Current 5G and Future 6G Systems”, Journal of Network and Systems Management, Vol. 31, No. 1, pp. 1-9, 2023.
- I. Khan, M.H. Zafar, M.T. Jan, J Lloret, M Basheri and D Singh, “Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems”, Entropy, Vol. 20, No. 2, pp. 92-108, 2018.
- H. Yang and X. Xie, “Deep-Reinforcement-Learning-based Energy-Efficient Resource Management for Social and Cognitive Internet of Things”, IEEE Internet of Things Journal, Vol. 7, No. 6, pp. 5677-5689, 2020.
- D. Wang and X. Du, “Intelligent Cognitive Radio in 5G: AIBased Hierarchical Cognitive Cellular Networks”, IEEE Wireless Communications, Vol. 26, No. 3, pp. 54-61, 2019.
- I.A. Najm, A.K. Hamoud, J Lloret and I Bosch, “Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment”, Electronics, Vol. 8, No. 6, pp. 607-615, 2019.
- C.D. Paola, K. Zhao, S. Zhang and G. F. Pedersen, “SIW Multibeam Antenna Array at 30 GHz for 5G Mobile Devices”, IEEE Access, Vol. 7, pp. 73157-73164, 2019.
- A.D. Boursianis, S.K. Goudos, T.V. Yioultsis, K. Siakavara and P. Rocca, “MIMO Antenna Design for 5G Communication Systems using Salp Swarm Algorithm”, Proceedings of International Workshop on Antenna Technology, pp. 1-3, 2020.
- E. Hossain, D. Niyato, and Z. Han, “Dynamic Bandwidth Access in Cognitive Radio Networks”, Cambridge University Press, 2009.
- C. Yang, J. Li, M. Guizani and M. Elkashlan “Advanced Bandwidth Sharing in 5G Cognitive Heterogeneous Networks”, IEEE Wireless Communications, Vol. 15, No. 2, pp. 94-101, 2016.
- M. Rajalakshmi, V. Saravanan and C. Karthik, “Machine Learning for Modeling and Control of Industrial Clarifier Process”, Intelligent Automation and Soft Computing, Vol. 32, No. 1, pp. 339-359, 2022.
- B. Vijayalakshmi “Improved Spectral Efficiency in Massive MIMO Ultra-Dense Networks through Optimal Pilot-Based Vector Perturbation Precoding. Optik, Vol. 273, pp. 1-8, 2023.
- J. Gowrishankar, P.S. Kumar and T. Narmadha, “A Trust Based Protocol for Manets in IoT Environment”, International Journal of Advanced Science and Technology, Vol. 29, No. 7, pp. 2770-2775, 2020.
- Z. Ai and H. Zhang, “A Smart Collaborative Charging Algorithm for Mobile Power Distribution in 5G Networks”, IEEE Access, Vol. 6, pp. 28668-28679, 2018.
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