Application of no-light fault prediction of PON based on deep learning method

Abstract

The increased demand for broadband communication services has led to a wider deployment of fiber in the last mile. However, this expansion comes with the challenge of high maintenance costs for optical networks. To address this issue, there is a need for cost-efficient and reliable monitoring solutions that can detect and locate faults in different fiber access architectures. One specific concern in the operation of Passive Optical Networks (PONs) is the occurrence of no-light faults in the PON ports. To tackle this practical problem, we propose an intelligent scheme based on Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Units (GRUs). These deep learning models are trained on historical data of the Optical Line Terminal (OLT) and PON behavior to predict future fault occurrences in real-time. To evaluate the performance of our proposed scheme, we conducted extensive experiments using real-world data. The results demonstrate the superiority of our approach over traditional methods and state-of-the-art models, as evidenced by improved accuracy, precision, recall, and F1 measure.