Integration of AI in passive optical networks (PON): enhancing performance and efficiency

Abstract Passive Optical Networks (PON) have emerged as a foundational technology in modern broadband infrastructure, renowned for their ability to deliver high-speed, high-bandwidth communication with low energy consumption and long-term scalability. However, as digital services expand and network traffic grows exponentially – driven by the proliferation of cloud computing, real-time applications, IoT devices, and video streaming – the limitations of traditional PON architectures are becoming increasingly evident. Static network configurations and conventional management strategies struggle to adapt to fluctuating demands, ensure consistent Quality of Service (QoS), and respond swiftly to faults or security threats. In response to these challenges, Artificial Intelligence (AI) offers a promising solution by introducing automation, adaptability, and data-driven intelligence into optical networks. This paper explores the integration of AI techniques into PON systems across key application domains, including fault detection, traffic forecasting, dynamic resource allocation, QoS optimization, and cyber security enhancement. By leveraging AI, PONs can achieve improved operational efficiency, predictive maintenance, enhanced user experience, and robust security, setting the foundation for intelligent and self-optimizing next-generation access networks. The study also discusses performance metrics, visual representations, challenges, and future directions to guide ongoing research and practical implementations.