2021 article
High Speed Receiver Modeling Using Generative Adversarial Networks
IEEE 30TH CONFERENCE ON ELECTRICAL PERFORMANCE OF ELECTRONIC PACKAGING AND SYSTEMS (EPEPS 2021).
author keywords: eye diagram; IBIS-AMI; generative model; generative adversarial network; GAN; receiver
TL;DR:
The model is not built with domain knowledge but learned from a wide range of channel conditions and input bitstreams to generate an eye diagram, and a neural network model is developed to evaluate the generated eye diagram's relevant characteristics, such as eye height and width.
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Added: March 21, 2022
This paper presents a generative approach to modeling a high-speed receiver with a time series input. The model is not built with domain knowledge but learned from a wide range of channel conditions and input bitstreams to generate an eye diagram. The generated eye diagrams are similar to the simulated eye diagrams for the same scenario. We also developed a neural network model to evaluate the generated eye diagram's relevant characteristics, such as eye height and width. The generated eye diagrams are within 7% and 3% error to the ground-truth in eye height and eye width, respectively, based on our evaluation neural network.