887 citations · 943 across the 12 of their papers we have counts for
11 papers · 1 filter
Deep Learning Based Adaptive Joint mmWave Beam Alignment
Daniel Tandler, Marc Gauger, Ahmet Serdar Tan +2
The challenging propagation environment, combined with the hardware limitations of mmWave systems, gives rise to the need for accurate initial access beam alignment strategies with…
Deep Reinforcement Learning for mmWave Initial Beam Alignment
Daniel Tandler, Sebastian Dörner, Marc Gauger +1
We investigate the applicability of deep reinforcement learning algorithms to the adaptive initial access beam alignment problem for mmWave communications using the state-of-the-ar…
Introducing -lifting for Learning Nonlinear Pulse Shaping in Coherent Optical Communication
Tim Uhlemann, Alexander Span, Sebastian Dörner +1
Pulse shaping for coherent optical fiber communication has been an active area of research for the past decade. Most of the early schemes are based on classic Nyquist pulse shaping…
A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning
Florian Euchner, Marc Gauger, Sebastian Dörner +1
A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of various mac…
Adaptive Neural Network-based OFDM Receivers
Moritz Benedikt Fischer, Sebastian Dörner, Sebastian Cammerer +3
We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel cond…
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…