Noise Prediction Based on Machine Learning in Quantum Secured SWDM B5G Fronthaul Networks

The fronthaul network is an essential way to improve the comprehensive performance of beyond fifth generation (B5G) communication network. Space-wavelength division multiplexing (SWDM) can effectively improve its capacity, and quantum key distribution technique can provide unconditional information security for it. In quantum secured SWDM B5G fronthaul network, quantum signals are affected by noises generated by classical signals, such as spontaneous Raman scattering, four-wave mixing and inter-core crosstalk. Evaluating these noises in real-time will increase the time delay of the whole network. In this paper, we propose two machine learning (ML) models (XGBoost and LightGBM) to predict these noises. Simulation results show that the ML models can reduce the noise evaluation time by up to 98.8%. Besides, the available channel predicting accuracy rate is close to 100%. The minimum noise predicting accuracy rate increases with the increasement of the channel occupancy rate and can reach 100% when channel occupancy is higher than 80%.