An Energy-Efficient Multi-Objective Optimization Framework for QoS-Aware Resource Allocation in 5G Slicing Networks

Li Ding; Minjia Yu; Zeyuan Du

doi:10.54097/04k0b578

Authors

Li Ding
Minjia Yu
Zeyuan Du

DOI:

https://doi.org/10.54097/04k0b578

Keywords:

Resource Allocation, Genetic Algorithm, QoS Optimization

Abstract

This paper investigates decision optimization for wireless resource allocation and energy-efficient scheduling in multi-user, multi-task 5G network slicing scenarios. We propose a hybrid computational framework combining particle swarm optimization, temporal scheduling, and genetic algorithms to address interference management, power control, and differentiated QoS guarantees across heterogeneous base station deployments. The proposed models incorporate Tikhonov regularization and service-type-specific QoS scoring to improve robustness and solution quality. Simulation-based experiments in MATLAB demonstrate high resource utilization (75.8%), system utility (1866.0), and significant energy efficiency gains (2.3179 ratio) under dynamic and interference-prone environments. Our results validate the framework’s capability to balance spectrum utilization, QoS maximization, and energy consumption, offering a scalable solution for intelligent network resource management.

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References

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