Machine learning-enhanced dual-task processor for next generation access networks

Abstract

We propose a dual-task, all-optical processor controlled by a Machine Learning-based digital optimizer. This innovative system compensates for signal impairments in Intensity Modulation/Direct Detection (IM/DD) and mitigates bandwidth limitations, reducing the need for power-hungry Digital Signal Processing (DSP). The synergy between the optical processor and digital optimizer, operating with the Tree-structured Parzen Estimator algorithm, forms a photonic Reservoir Computing approach that enhances signal performance and reach. This versatile system can function as an all-optical equalizer and chromatic dispersion compensation filter, outperforming traditional electrical counterparts. The proposed scheme, offers a cost-effective and efficient solution for next-generation access networks.