1 citations · 1 across the 5 of their papers we have counts for
6 papers
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
Jinxiang Song, Christian Häger, Jochen Schröder +3
End-to-end autoencoder (AE) learning has the potential of exceeding the performance of human-engineered transceivers and encoding schemes, without a priori knowledge of communicati…
End-to-End Learning for Integrated Sensing and Communication
José Miguel Mateos-Ramos, Jinxiang Song, Yibo Wu +4
Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint s…
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…
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…
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Jinxiang Song, Christian Häger, Jochen Schröder +2
End-to-end data-driven machine learning (ML) of multiple-input multiple-output (MIMO) systems has been shown to have the potential of exceeding the performance of engineered MIMO t…
Learning Physical-Layer Communication with Quantized Feedback
Jinxiang Song, Bile Peng, Christian Häger +2
Data-driven optimization of transmitters and receivers can reveal new modulation and detection schemes and enable physical-layer communication over unknown channels. Previous work…