DNN-based QoT Estimation Using Topological Inputs and Training with Synthetic-Physical Data

We propose a DNN-based QoT estimation technique that operates on a network-wide scale. The DNN training data is composed of connection paths, frequency slots, and OSNR transponder telemetry, collected from both synthetic and physical connections. Simulation results indicate effective and low-complexity QoT estimation.