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Optimizing Efficiency and Performance in 5G Networks through a Dynamic Resource Allocation Algorithmic Framework


Affiliations
1 Department of Computer Science, Government First Grade College for Women, Hassan, Karnataka, India
2 Department of Information Science and Engineering, Cambridge Institute of Technology, India
3 Department of Master of Business Administration, Kalasalingam Academy of Research and Education, India
4 Department of Computer Science, Oryx Universal College, Qatar
     

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With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency.

Keywords

5G Networks, Dynamic Resource Allocation, Fractional Pilots, Interference Management, Spectral Efficiency.
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  • S. Doucha and M. Abri, “New Design of Leaky-Wave Antenna based on SIW Technology for Beam Steering”, International Journal of Computer Networks and Communications, Vol. 5, No. 5, pp. 73-82, 2013.

  • A. Vahidi and E. Saberinia, "OFDM High Speed Train Communication Systems in 5G Cellular Networks”, Proceedings of International Conference on Communications and Networking, pp. 1-6, 2018.

  • K. Yan, P. Yang, F. Yang, L.Y. Zeng, and S. Huang, “EightAntenna Array in the 5G Smartphone for the Dual-Band MIMO System”, Proceedings of IEEE International Symposium on Antennas and Propagation and USNC/URSI National Radio Science Meeting, pp. 1431-1435, 2018.

  • A. Gonzalez-Plaza, “5G Communications in High Speed and Metropolitan Railways”, Proceedings of European Conference on Antennas and Propagation, pp. 658-660, 2017.

  • 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.

  • D. Zhang, D. Zhe, M. Jiang and J. Zhang, “High Speed WDM-PON Technology for 5G Fronthaul Network”, Proceedings of International Conference on Asia Communications and Photonics, pp. 1-3, 2018.

  • G.O. Perez, J.A. Hernandez and D. Larrabeiti, “Fronthaul Network Modeling and Dimensioning Meeting Ultra-Low Latency Requirements for 5G”, Journal of Optical Communications and Networking, Vol. 10, No. 6, pp. 573-581, 2018.

  • N. Shanmugasundaram and J. Lloret, “Energy‐Efficient Resource Allocation Model for Device‐to‐Device Communication in 5G Wireless Personal Area Networks”, International Journal of Communication Systems, Vol. 21, pp. 5524-5534, 2023.

  • V. Saravanan, D. Saravanan and H.P. Sultana, “Design of Deep Learning Model for Radio Resource Allocation in 5G for Massive IoT Device”, Sustainable Energy Technologies and Assessments, Vol. 56, pp. 103054-103065, 2023.

  • F.C. Jiang, D.C. Huang, C.T. Yang and F.Y. Leu, “Lifetime Elongation for Wireless Sensor Network using Queue-Based Approaches”, Supercomputing, Vol. 59, pp. 1312-1335, 2012.

  • Y. Ji, J. Zhang, Y. Xiao and Z. Liu, “5G Flexible Optical Transport Networks with Large-Capacity, Low-Latency and High-Efficiency”, China Communications, Vol. 16, No. 5, pp. 19-32, 2019.

  • 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.

  • E. Hossain, D. Niyato, and Z. Han, “Dynamic Bandwidth Access in Cognitive Radio Networks”, Cambridge University Press, 2009.


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  • Optimizing Efficiency and Performance in 5G Networks through a Dynamic Resource Allocation Algorithmic Framework

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Authors

M.J. Sridevi
Department of Computer Science, Government First Grade College for Women, Hassan, Karnataka, India
Pradosh Kumar Sharma
Department of Information Science and Engineering, Cambridge Institute of Technology, India
M. Dhiliphan Kumar
Department of Master of Business Administration, Kalasalingam Academy of Research and Education, India
D. Loganathan
Department of Information Science and Engineering, Cambridge Institute of Technology, India
Saleem Ahmed
Department of Computer Science, Oryx Universal College, Qatar

Abstract


With the exponential growth of data demand and the advent of 5G networks, the need for efficient resource allocation algorithms has become paramount. This study presents a dynamic resource allocation algorithmic framework aimed at optimizing efficiency and performance in 5G networks. The framework focuses on frequency reuse at the edges while employing fractional pilots for enhanced spectrum utilization. 5G networks promise unprecedented speeds and low latency, enabling a wide array of applications from IoT to augmented reality. However, the efficient allocation of resources remains a challenge, especially at the network edges where interference is high. Traditional static resource allocation schemes fail to adapt to dynamic network conditions, leading to suboptimal performance. The main challenge lies in effectively managing resources to meet the diverse demands of various applications while mitigating interference and maximizing spectral efficiency. The proposed framework employs a dynamic resource allocation algorithm that adapts to changing network conditions in real-time. Leveraging fractional pilots, the algorithm optimizes frequency reuse at the network edges, thereby enhancing spectral efficiency. The framework integrates stochastic learning for predictive analytics to anticipate resource demands and interference patterns. Simulation results demonstrate significant improvements in spectral efficiency and network performance compared to traditional static allocation methods. The utilization of fractional pilots effectively reduces interference, enabling higher throughput and lower latency, especially at the network edges. The dynamic nature of the algorithm ensures adaptability to varying traffic loads, leading to enhanced overall network efficiency.

Keywords


5G Networks, Dynamic Resource Allocation, Fractional Pilots, Interference Management, Spectral Efficiency.

References