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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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  • 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