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We experimentally demonstrate the use of a feed-forward photonic neural network (PNN) for chromatic dispersion compensation in fiber transmission within IM-DD protocols. The PNN device is constituted by an 8-channel all-optical delayed complex perceptron integrated on a Silicon-On-Insulator platform. The PNN device is inserted after the transmitter and before the fiber, thus acting as a pre-compensator. The training is performed via a Particle Swarm Optimizer and aims to provide an open eye diagram at the end-of-line receiver. We observe a 5-order of magnitude Bit Error Rate reduction for -7 dBm of power at the receiver between bare and equalized transmission for 10 Gbps Non-Return-to-Zero signals in a 125 km fiber link (average excess loss of 15 dB). We also perform a study on the minimum number of channels in the PNN needed for full equalization. Overall, the experimental results validate our solution for channel equalization via a PNN with negligible latency and a power consumption of 250 mW on average.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Emiliano Staffoli,Gianpietro Maddinelli,Mattia Mancinelli,Paolo Bettotti, andLorenzo Pavesi
"Chromatic dispersion compensation via an all-optical perceptron", Proc. SPIE 12894, Next-Generation Optical Communication: Components, Sub-Systems, and Systems XIII, 128940G (8 March 2024); https://doi.org/10.1117/12.2692979
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Emiliano Staffoli, Gianpietro Maddinelli, Mattia Mancinelli, Paolo Bettotti, Lorenzo Pavesi, "Chromatic dispersion compensation via an all-optical perceptron," Proc. SPIE 12894, Next-Generation Optical Communication: Components, Sub-Systems, and Systems XIII, 128940G (8 March 2024); https://doi.org/10.1117/12.2692979