48 citations · 52 across the 11 of their papers we have counts for
11 papers
Blind Channel Equalization Using Vector-Quantized Variational Autoencoders
Jinxiang Song, Vincent Lauinger, Yibo Wu +5
State-of-the-art high-spectral-efficiency communication systems employ high-order modulation formats coupled with high symbol rates to accommodate the ever-growing demand for data…
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski +8
Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-c…
Spatial Signal Design for Positioning via End-to-End Learning
Steven Rivetti, Josè Miguel Mateos-Ramos, Yibo Wu +5
This letter considers the problem of end-to-end learning for joint optimization of transmitter precoding and receiver processing for mmWave downlink positioning. Considering a mult…
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…
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…
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…