Search Results for author: Jochen Schröder

Found 8 papers, 1 papers with code

Blind Channel Equalization Using Vector-Quantized Variational Autoencoders

no code implementations22 Feb 2023 Jinxiang Song, Vincent Lauinger, Yibo Wu, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Laurent Schmalen, Henk Wymeersch

Furthermore, we show that for the linear channel, the proposed scheme exhibits better convergence properties than the \ac{MMSE}-based, the \ac{CMA}-based, and the \ac{VAE}-based equalizers in terms of both convergence speed and robustness to variations in training batch size and learning rate.

Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments

1 code implementation29 Nov 2021 Jinxiang Song, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Henk Wymeersch

Simulation results show that the reinforcement-learning-based algorithm achieves similar performance to the standard supervised end-to-end learning approach assuming perfect channel knowledge.

reinforcement-learning Reinforcement Learning (RL)

Power Efficient Communication for Low Signal to Noise Ratio Optical Links

no code implementations20 Apr 2021 Ravikiran Kakarla, Mikael Mazur, Jochen Schröder, Peter A. Andrekson

Receiver sensitivity is a particularly important metric in optical communication links operating at low signal-to-noise ratios (SNRs), for example in deep-space communication, since it directly limits the maximum achievable reach and data rate.

Position

End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairment

no code implementations29 Mar 2021 Jinxiang Song, Christian Häger, Jochen Schröder, Alexandre Graell i Amat, Henk Wymeersch

We propose an end-to-end learning-based approach for superchannel systems impaired by non-ideal hardware component.

Benchmarking End-to-end Learning of MIMO Physical-Layer Communication

no code implementations19 May 2020 Jinxiang Song, Christian Häger, Jochen Schröder, Tim O'Shea, Henk Wymeersch

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 transceivers, without any a priori knowledge of communication-theoretic principles.

Benchmarking

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