Design of Passive Equalizer for Space Wire Links via Support Vector Machine

This paper presents an example of use of the support vector machine regression for the design of a passive equalizer network in a SpaceWire communication channel. The support vector machine is a machine learning technique, here applied to generate a compact surrogate model of the eye diagram heights at the end of the differential communication channel. The surrogate model is estimated from a limited set of randomly selected training samples. It provides the designer with a powerful and accurate tool for the optimization of the equalizer network, in presence of different lengths of the SpaceWire cable. The benefits of the proposed equalizer solution, combined with the SVM-based modeling tool, are investigated on a realistic SpaceWire link.