AI-Enhanced RAN Performance Optimization in Open RAN Ecosystems

Venkata Ramanaiah Chintha,

Wright State University, Dayton, OH, United States, venkatch1104@gmail.com

 Kratika Jain,

Teerthanker Mahaveer University, Delhi Road, NH9, Moradabad, Uttar Pradesh 244001 India, jainkratika.567@gmail.com

Abstract

The advent of Open Radio Access Networks (Open RAN) has revolutionized the telecommunications industry by offering a flexible, interoperable, and cost-effective alternative to traditional RAN architectures. However, the complexity of managing Open RAN ecosystems, with diverse vendors and technologies, poses challenges in ensuring optimal network performance. This paper explores the application of Artificial Intelligence (AI) to enhance the performance optimization of Open RAN environments. AI techniques, including machine learning (ML), deep learning (DL), and reinforcement learning (RL), can significantly improve various aspects of RAN performance such as spectrum management, interference mitigation, load balancing, and network automation.

By leveraging AI, Open RAN systems can dynamically adapt to network conditions, predict traffic patterns, optimize resource allocation, and enhance Quality of Service (QoS). The use of AI-driven predictive models also enables proactive fault detection, reducing downtime and operational costs. Moreover, AI enhances network self-configuration and self-optimization capabilities, which are crucial for achieving the scalability and flexibility promised by Open RAN.

This paper investigates key AI methodologies that can be integrated with Open RAN, examines their impact on network performance metrics, and discusses the challenges and opportunities associated with AI-driven optimization in a multi-vendor ecosystem. Ultimately, AI-enhanced RAN performance optimization is crucial for realizing the full potential of Open RAN, offering improved network efficiency, reduced operational costs, and better end-user experiences in next-generation mobile networks.

Keywords

AI, Open RAN, performance optimization, machine learning, deep learning, reinforcement learning, network automation, spectrum management, interference mitigation, load balancing, predictive models, network scalability, self-configuration, self-optimization, multi-vendor ecosystem.

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