RxGAN: Modeling High-Speed Receiver through Generative Adversarial Networks

Creating models for modern high-speed receivers using circuit-level simulations is costly, as it requires computationally expensive simulations and upwards of months to finalize a model. Added to this is that many models do not necessarily agree with the final hardware they are supposed to emulate. Further, these models are complex due to the presence of various filters, such as a decision feedback equalizer (DFE) and continuous-time linear equalizer (CTLE), which enable the correct operation of the receiver. Other data-driven approaches tackle receiver modeling through multiple models to account for as many configurations as possible. This work proposes a data-driven approach using generative adversarial training to model a real-world receiver with varying DFE and CTLE configurations while handling different channel conditions and bitstreams. The approach is highly accurate as the eye height and width are within 1.59% and 1.12% of the ground truth. The horizontal and vertical bathtub curves match the ground truth and correlate to the ground truth bathtub curves.