Scalable and resilient optical network infrastructure with MAB-driven bandwidth management and DQAE-based anomaly detection

Abstract

Abstract Recent studies show that global Passive Optical Network (PON) deployments will reach over 1.3 billion subscribers by 2030in Optical Network Unit (ONU) device integration. Despite this, legacy systems suffer from scalability bottlenecks and average latency increments of 15–30 % during user density surges. Existing networks face significant challenges, such as latency spikes at the Optical Line Terminal (OLT) during ONU scalability and insufficient reliability in handling anomalies within converged infrastructures. To address these issues, a novel Multi-Armed Bandit (MAB) approach is applied to optimize dynamic bandwidth allocation (DBA) at the ONU layer, enabling increased user density without inducing latency burdens at the OLT. The MAB-based selection strategy efficiently adapts to varying traffic patterns by learning optimal resource assignment policies in real time, ensuring minimal contention delays and better quality of service (QoS). Network fault tolerance and reliability are enhanced through a Deep Q-Auto Encoder (DQAE)-based anomaly detection model trained to recognize and classify failure signatures across optical and packet layers. This unsupervised deep reinforcement learning model integrates reconstruction loss with Q-learning to identify unknown failure states simultaneously and recommend proactive recovery actions. The combined strategy improves user scalability and service stability and establishes a fault-resilient infrastructure suitable for next-generation converged optical networks.