ML-Optimized QKD Frequency Assignment for Efficient Quantum-Classical Coexistence in Multi-Band EONs

Abstract: Quantum key distribution (QKD) represents a cutting-edge technology that ensures unbreakable security. Coexisting quantum and classical signals on a multi-band (O+E+S+C+L-band) system offer a viable solution for secure, high-rate networks amidst growing classical traffic and address quantum signal sensitivity. In this study, we assume a dynamic classical traffic load and varying configurations of classical channels (CChs). Considering the varying behavior of Secure Key Rate (SKR) under different classical conditions, solving the integral noise equations are crucial for optimizing QKD implementation and enhancing resource efficiency. The complexity and time-consuming nature of this process challenge infrastructure providers in determining the optimal quantum channel (QCh) frequency in real time. To tackle these challenges, we propose a machine learning (ML) algorithm. By leveraging ML, QKD can be implemented efficiently, optimizing resource utilization while significantly reducing computation and processing time in dynamic classical traffic. We implement three ML algorithms at various fiber intervals, all of which estimate the optimal frequency for QCh with 99\% accuracy and perform computations on average in 0.09 seconds, which is significantly faster compared to integral computational methods that have a mean time of 637 seconds.Information: In this file, the Excel sheet contains data for each fiber interval, including inputs such as fiber length in each interval, the overall classical loading factor percentage, the C-band loading factor percentage, the L-band loading factor percentage, the highest active classical frequency (which serves as input to the machine learning model), and the QCh frequency that resulted in the highest SKR.