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Improving the Requirements Based Bandwidth Allocation In 5G Point To Point Networks
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In General, the 5G is a fifth-generation technology that works at speeds of 4G to 100 times the network speed. The main objective of introducing modern information technologies is to facilitate and facilitate access to public services. The use of new technologies is a key factor in improving the overall structure of public administration and increasing its efficiency. In addition, it is important to improve the infrastructure of all types of communications. The telecom operators are often willing to invest more in infrastructure development. In this paper, a new model was proposed to enhance the bandwidth allocation and utilization. It is a requirements-based network that allows the users to increase the speed of wireless networks. This proposed method also increases the amount of data that can be transmitted over wireless networks.
Keywords
5G, Telecom Operators, Bandwidth, Wireless Networks
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- E. Hossain, D. Niyato, and Z. Han, “Dynamic Bandwidth Access in Cognitive Radio Networks”, Cambridge University Press, 2009.
- T.D. Lagkas and I. Tomkos, “Joint Spatial and Spectral Resource Optimization over Both Wireless and Optical Fronthaul Domains of 5G Architectures”, Proceedings of International Conference on Transparent Optical Networks, pp. 1-7, 2020.
- Yuan Ai and Yaohua Sun, “Joint Resource Allocation and Admission Control in Sliced Fog Radio Access Networks”, China Communications, Vol. 17, No. 8, pp. 14-30, 2020.
- N. Khumalo and L. Mfupe, “Reinforcement Learning-based Computation Resource Allocation Scheme for 5G Fog-Radio Access Network”, Proceedings of International Conference on Fog and Mobile Edge Computing, pp. 353-355. 2020.
- Y.H. Robinson, V. Saravanan and P.E. Darney, “Enhanced Energy Proficient Encoding Algorithm for Reducing Medium Time in Wireless Networks”, Wireless Personal Communications, Vol. 119, No. 4, pp. 3569-3588, 2021.
- V. Hanumante and S. Roy, “Comparative Study of Microstrip Patch Antenna Using Different Dielectric Materials”, Proceedings of International Conference on Microwave, Antennas and Propagation, Remote Sensing, pp. 56-60, 2013.
- Y. Wang, K. Wang, H. Huang, T. Miyazaki and S. Guo, “Traffic and Computation Co-Offloading With Reinforcement Learning in Fog Computing for Industrial Applications”, IEEE Transactions on Industrial Informatics, Vol. 15, No. 2, pp. 976-986, 2019.
- 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.
- L. Huang, X. Feng, C. Zhang, L. Qian and Y. Wu, “Deep Reinforcement Learning-based Joint Task Offloading and Bandwidth Allocation for Multiuser Mobile Edge Computing”, Digital Communications and Networks, Vol. 5, No. 1, pp. 10-17, 2019.
- 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.
- M. Matinmikko, P. Ahokangas and M. Mustonen, “Bandwidth Sharing using Licensed Shared Access: The Concept and its Workflow for LTE-Advanced Networks”, IEEE Wireless Communications, Vol. 21, No. 2, pp. 72-79, 2014.
- E. Jorswieck, E. Karipidis and J. Luo, “Bandwidth Sharing Improves the Network Efficiency for Cellular Operators”, IEEE Communications Magazine, Vol. 52, No. 3, pp. 129-136. 2014.
- N. Michelusi, M. Nokleby, U.i Mitra, and R. Calderbank “Multi-Scale Bandwidth Sensing in Dense Multi-Cell Cognitive Networks”, IEEE Transactions on Wireless Communications, vol. 67, no. 4, pp. 2673–2688, 2019
- 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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