Enhancing 5G Traffic Management with Programmable Intelligence and Open RAN Integration
DOI:
https://doi.org/10.4108/eetiot.9278Keywords:
5G networks, open RAN, deep reinforcement learning, network efficiency, spectral efficiency, artificial intelligenceAbstract
INTRODUCTION: 5G networks are complex. They must handle different types of connections. These networks support industries, cities and mobile users. Managing traffic is difficult. Traditional methods are not efficient.
OBJECTIVES: This paper introduces a software framework called ns-O-RAN. It combines a real-world RAN Intelligent Controller with a network simulator. This allows testing AI solutions without expensive hardware. The study also proposes a smart handover method.
METHODS: The goal is to reduce delays and improve speed. The new method uses deep reinforcement learning (DRL). DRL learns the best way to assign users to base stations. The framework collects a large amount of data. It trains the AI system using this data. The model learns from past network conditions. It then makes better decisions for the future.
RESULTS: The proposed solution increases network efficiency. The researchers tested their model. They compared it with traditional handover methods. This means faster speeds and fewer connection losses. The framework also enables real-time monitoring. It detects network issues quickly and adapts to changing conditions. This ensures stable and high-quality connections for users.
CONCLUSION: This approach supports different types of applications. It works well for video streaming, voice calls and industrial automation. This work has important implications. It helps telecom providers improve service quality. It also reduces operational costs. Researchers and engineers can use this framework for further development.
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Copyright (c) 2025 U.S.B.K. Mahalaxmi, Jeevana Sujitha Mantena, V.V. Jaya Rama Krishnaiah, Vatsala Anand, B. Rajani, Inakoti Ramesh Raja, Pavuluri Girija Krishna Sirisha
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