Experimental Investigation of a Recurrent Optical Spectrum Slicing Receiver for Intensity Modulation/Direct Detection systems using Programmable Photonics
Kostas Sozos, Francesco Da Ros, Metodi P. Yankov, George Sarantoglou,, Stavros Deligiannidis, Charis Mesaritakis, Adonis Bogris

TL;DR
This study experimentally validates a recurrent optical spectrum slicing receiver that uses programmable photonics to improve dispersion compensation in high-speed IM/DD optical links, enabling longer transmission distances.
Contribution
It demonstrates the practical implementation and effectiveness of recurrent optical spectrum slicing accelerators using programmable photonic platforms for dispersion mitigation.
Findings
Successfully equalized 80 km of 64 Gb/s PAM-4 transmission
Validated recurrent filters in programmable photonics for dispersion compensation
Enhanced transmission distance in dispersive channels
Abstract
In this paper, we experimentally validate our previous numerical works in recurrent optical spectrum slicing (ROSS) accelerators for dispersion compensation in high-speed IM/DD links. For this, we utilize recurrent filters implemented both through a waveshaper and by exploiting novel integrated programmable photonic platforms. Different recurrent configurations are tested. The ROSS accelerators exploit frequency processing through recurrent optical filter nodes in order to mitigate the power fading effect, which hinders the transmission distance and baudrate scalability of IM/DD systems. By equalizing even 80 km of 64 Gb/s PAM-4 transmission in C-band, we prove that our system can offer an appealing solution in highly dispersive channels.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptical Network Technologies · Advanced Photonic Communication Systems · Neural Networks and Reservoir Computing
