81 citations · 749 across the 99 of their papers we have counts for
5 papers · 2 filters
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Jinxiang Song, Christian Häger, Jochen Schröder +2
We propose an AE-based transceiver for a WDM system impaired by hardware imperfections. We design our AE following the architecture of conventional communication systems. This enab…
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
Yibo Wu, Jinxiang Song, Christian Häger +3
We propose an over-the-air digital predistortion optimization algorithm using reinforcement learning. Based on a symbol-based criterion, the algorithm minimizes the errors between…
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Jinxiang Song, Zonglong He, Christian Häger +4
We demonstrate, for the first time, experimental over-the-fiber training of transmitter neural networks (NNs) using reinforcement learning. Optical back-to-back training of a novel…
Low Complexity Joint Impairment Mitigation of I/Q Modulator and PA Using Neural Networks
Yibo Wu, Ulf Gustavsson, Alexandre Graell i Amat +1
Neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two…
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment
Jinxiang Song, Christian Häger, Jochen Schröder +2
We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component. Our system achieves up to 60% SER reduction and up to 50% guard…