Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

• Published in: Journal of engineering (Stevenage, England) England), 2024-02, Vol.2024 (2), p.n/a
• Main Authors: Sangeetha, Sivakumar, Logeshwaran, Jaganathan, Faheem, Muhammad, Kannadasan, Raju, Sundararaju, Suganthi, Vijayaraja, Loganathan
• Format: Article
• Language: English
• Subjects: 5G mobile communication; energy efficiency; green communication; optimal scheduling
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/tje2.12358

This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization. The proposed algorithm optimizes the communication resources to minimize the total energy consumption while maximizing the quality of service (QoS) performance. The proposed scheme can be used to dynamically adjust the resource allocation according to the changing traffic conditions in the system.