We propose a novel optimization method for a Neural Network based Digital Pre-Distorter (DPD), applied in Intensity Modulation-Direct Detection transmission systems leveraging Multi-Modal Fiber and Vertical-Cavity Surface-Emitting Laser. We train the DPD using End-to-end Deep Learning of the optical link, together with a Direct Learning Approach leveraging experimental measurements for modeling the transmission channel. The optimization considers VCSEL amplitude constraints, the use of an FFE at the receiver side, and the presence of a receiver non-flat Colored Gaussian Noise (CGN). We verify our optimized DPD on an experimental setup transmitting a 92 Gbps PAM-4 modulated signal. We achieve, for BER=0.01, a performance gain of more than 1 dB in terms of Optical Path Loss with respect to the best performing non-pre-distorted scenario.

End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion / Minelli, Leonardo; Forghieri, Fabrizio; Gaudino, Roberto. - ELETTRONICO. - (2022), pp. 1-4. ((Intervento presentato al convegno 2022 Italian Conference on Optics and Photonics (ICOP) tenutosi a Trento, Italy nel 15-17 June 2022 [10.1109/ICOP56156.2022.9911760].

End-to-end Deep Learning for VCSEL’s Nonlinear Digital Pre-Distortion

Minelli, Leonardo;Gaudino, Roberto
2022

Abstract

We propose a novel optimization method for a Neural Network based Digital Pre-Distorter (DPD), applied in Intensity Modulation-Direct Detection transmission systems leveraging Multi-Modal Fiber and Vertical-Cavity Surface-Emitting Laser. We train the DPD using End-to-end Deep Learning of the optical link, together with a Direct Learning Approach leveraging experimental measurements for modeling the transmission channel. The optimization considers VCSEL amplitude constraints, the use of an FFE at the receiver side, and the presence of a receiver non-flat Colored Gaussian Noise (CGN). We verify our optimized DPD on an experimental setup transmitting a 92 Gbps PAM-4 modulated signal. We achieve, for BER=0.01, a performance gain of more than 1 dB in terms of Optical Path Loss with respect to the best performing non-pre-distorted scenario.
978-1-6654-8881-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2972473