Leonardo Minelli
Neural networks for optical links nonlinear equalization.
Rel. Roberto Gaudino, Monica Visintin, Pablo Torres Ferrera. Politecnico di Torino, Master of science program in Communications And Computer Networks Engineering, 2021
Abstract
The continuously increasing Internet traffic demand, driven by modern technologies such as cloud services Internet of Things and 5G, is posing the request for an upgrade in the Data Center Interconnects (DCI) above their current maximum capacity. Current DCI commercial solutions are based on Intensity Modulation and Direct Detection optical links, able to transmit up to 28 Gbps per lane over nearly 100 m, using Multi-Modal Fiber and low-cost Vertical-Cavity Emitting Surface Lasers (VCSEL). The next step is then to achieve 100 Gbps per lane, but this target seems unfeasible using the current technologies. At high bitrates indeed, several impairments distort the transmitted signals: bandwidth limitations at receiver and transmitter side, modal and chromatic dispersion along with the fiber, and nonlinear effects introduced by components such as the VCSEL.
The work of this Thesis aims therefore to overcome these limitations, by designing and studying the Artificial Neural Networks (ANN) to use them as nonlinear post- and pre- equalizers on PAM-4 modulated optical signals
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