5G Network Optimization Using Artificial Intelligence Algorithms to Improve Quality of Service

Abstract

The rapid deployment of 5G networks presents unique challenges in meeting the growing demand for high-speed, reliable, and low-latency communication services. Traditional network management methods, which rely on static resource allocation and manual configurations, are insufficient to handle the dynamic nature of 5G networks. This paper explores the application of Artificial Intelligence (AI) algorithms, specifically Reinforcement Learning (RL), for optimizing 5G resource allocation and improving Quality of Service (QoS). The study reviews existing research on 5G network optimization, highlighting the limitations of traditional network management techniques and the need for intelligent, adaptive solutions. RL, with its ability to dynamically adjust to changing network conditions, offers a promising approach to address these challenges by optimizing traffic scheduling and resource utilization. The proposed method leverages RL to autonomously allocate resources in real-time based on network conditions, ensuring optimal network performance and enhanced QoS. Simulation results demonstrate significant improvements in latency, throughput, and packet loss when using RL-based scheduling compared to traditional static methods. RL-based optimization not only enhances the adaptability and stability of network performance but also improves resource efficiency by reducing congestion and minimizing packet loss during peak traffic periods. This paper also discusses the advantages of RL in terms of stability and adaptability, emphasizing its potential to outperform static methods in complex, high-demand 5G environments. In conclusion, the integration of AI-driven optimization, particularly RL, in 5G networks offers substantial benefits in terms of resource management and QoS improvement.