Queue-Informed Neural Network Model for Estimating Queuing Delay in PON-Based Aggregation Networks

Future carrier networks will need to aggregate traffic from widely distributed sensor nodes while meeting stringent service requirements in industrial and logistics applications. As these networks scale, ensuring deterministic latency and cost efficiency becomes increasingly critical. Technologies with optical transmission and aggregation capabilities, such as Passive Optical Networks (PONs) with switching functions, offer cost-effective solutions. However, the queuing delays caused by multi-stage traffic aggregation for large-scale data collection must be accurately estimated to meet performance requirements. Traditional machine learning approaches for the queuing delay estimation often rely heavily on large labeled datasets, which are expensive and impractical to collect in large-scale environments. To address these limitations, this study proposes a Queue-Informed Neural Network (QINN) that incorporates governing equations derived from queuing theory into the neural network training process. By embedding queuing principles into the learning model, the proposed QINN improves the accuracy of queuing delay estimations without requiring extensive datasets. Application of the proposed QINN model to simulation data under different aggregation scenarios, such as PONs combined with an Ethernet switch, demonstrates improved the accuracy of queue delay estimation. These results highlight the potential of integrating governing equations based on queuing theory to improve queuing delay estimation in aggregation networks.