45 citations · 71 across the 4 of their papers we have counts for
9 papers
Massive-MIMO Iterative Channel Estimation and Decoding (MICED) in the Uplink
Daniel Verenzuela, Emil Björnson, Xiaojie Wang +2
Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It i…
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…
Decoder-in-the-Loop: Genetic Optimization-based LDPC Code Design
Ahmed Elkelesh, Moustafa Ebada, Sebastian Cammerer +2
LDPC code design tools typically rely on asymptotic code behavior and are affected by an unavoidable performance degradation due to model imperfections in the short length regime.…