Empowering Next-Gen Networks: AI-Driven Autonomy in O-RAN and SON Architectures

Abstract

Wireless communication plays a crucial role in evolving next-generation networks. Open Radio Access Networks (O-RAN), Self-Organizing Networks (SON), and Artificial Intelligence (AI)-driven automation are set to revolutionize network operations by enhancing flexibility, scalability, intelligence, and efficiency. Integrating AI and Machine Learning (ML) into O-RAN and SON enables autonomous network management. These technologies dynamically optimize performance, resource allocation, and fault recovery, allowing the network to adapt in real time. SON introduces self-configuration, self-optimization, and self-healing capabilities. These features help reduce operational costs and improve service reliability across different network layers. O-RAN, on the other hand, provides an open and intelligent architecture that supports the deployment of SON functionalities. Its modular design and open interfaces promote flexibility, interoperability, and vendor-neutral customization of network resources. Together, O-RAN and SON enhance network efficiency, scalability, and reliability. Dynamic resource allocation increases efficiency, while scalable architectures accommodate growing device densities. Automated fault detection and recovery further strengthen network resilience. This paper presents a comprehensive analysis of the synergies between O-RAN and SON in achieving AI-driven autonomy. It highlights their technical and operational benefits, while also addressing key challenges such as interoperability, security, and standardization. Finally, the study discusses future research opportunities and emphasizes the critical role of O-RAN and SON in shaping autonomous next-generation wireless networks.