5 citations · 6 across the 5 of their papers we have counts for
10 papers · 1 filter
MinGRU-Based Encoder for Turbo Autoencoder Frameworks
Rick Fritschek, Rafael F. Schaefer
Early neural channel coding approaches leveraged dense neural networks with one-hot encodings to design adaptive encoder-decoder pairs, improving block error rate (BLER) and automa…
Diffusion Models for Accurate Channel Distribution Generation
Muah Kim, Rick Fritschek, Rafael F. Schaefer
Strong generative models can accurately learn channel distributions. This could save recurring costs for physical measurements of the channel. Moreover, the resulting differentiabl…
Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding
Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtai…
Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison
Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
Deep learning based physical layer design, i.e., using dense neural networks as encoders and decoders, has received considerable interest recently. However, while such an approach…
Deep Learning for Channel Coding via Neural Mutual Information Estimation
Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
End-to-end deep learning for communication systems, i.e., systems whose encoder and decoder are learned, has attracted significant interest recently, due to its performance which c…
Deep Learning for the Gaussian Wiretap Channel
Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder
End-to-end learning of communication systems with neural networks and particularly autoencoders is an emerging research direction which gained popularity in the last year. In this…