45 citations · 54 across the 2 of their papers we have counts for
6 papers
Serial vs. Parallel Turbo-Autoencoders and Accelerated Training for Learned Channel Codes
Jannis Clausius, Sebastian Dörner, Sebastian Cammerer +1
Attracted by its scalability towards practical codeword lengths, we revisit the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer communications. For this, we study t…
Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer +4
We compare the potential of neural network (NN)-based channel estimation with classical linear minimum mean square error (LMMSE)-based estimators, also known as Wiener filtering. F…
Deep-learning Autoencoder for Coherent and Nonlinear Optical Communication
Tim Uhlemann, Sebastian Cammerer, Alexander Span +2
Motivated by the recent success of end-to-end training of communications in the wireless domain, we strive to adapt the end-to-end-learning idea from the wireless case (i.e., linea…
On Recurrent Neural Networks for Sequence-based Processing in Communications
Daniel Tandler, Sebastian Dörner, Sebastian Cammerer +1
In this work, we analyze the capabilities and practical limitations of neural networks (NNs) for sequence-based signal processing which can be seen as an omnipresent property in al…
Enabling FDD Massive MIMO through Deep Learning-based Channel Prediction
Maximilian Arnold, Sebastian Dörner, Sebastian Cammerer +3
A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for…
Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes
Stefan Schibisch, Sebastian Cammerer, Sebastian Dörner +2
We demonstrate that error correcting codes (ECCs) can be used to construct a labeled data set for finetuning of "trainable" communication systems without sacrificing resources for…