11 citations · 32 across the 11 of their papers we have counts for
18 papers
Condition-Wise Sinkhorn Drifting for One-Shot Learned Channel Simulation
Rick Fritschek, Rafael F. Schaefer
Learned communication systems may evaluate stochastic channel surrogates millions of times inside differentiable training loops, making diffusion-style reverse sampling expensive.…
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…
Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE
Onur Günlü, Rick Fritschek, Rafael F. Schaefer
Small neural networks (NNs) used for error correction were shown to improve on classic channel codes and to address channel model changes. We extend the code dimension of any such…
A Reverse Jensen Inequality Result with Application to Mutual Information Estimation
Gerhard Wunder, Benedikt Groß, Rick Fritschek +1
The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning. It can be also used to derive other standard ineq…
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…