Search Results for author: Rick Fritschek

Found 6 papers, 1 papers with code

Diffusion Models for Accurate Channel Distribution Generation

no code implementations19 Sep 2023 Muah Kim, Rick Fritschek, Rafael F. Schaefer

In this paper, we address this channel approximation challenge with diffusion models (DMs), which have demonstrated high sample quality and mode coverage in image generation.

Image Generation Scheduling

Learning End-to-End Channel Coding with Diffusion Models

no code implementations3 Feb 2023 Muah Kim, Rick Fritschek, Rafael F. Schaefer

This places the challenge to approximate or generate the channel or its derivative from samples generated by pilot signaling in real-world scenarios.

Generative Adversarial Network

Concatenated Classic and Neural (CCN) Codes: ConcatenatedAE

no code implementations4 Sep 2022 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.

A Reverse Jensen Inequality Result with Application to Mutual Information Estimation

no code implementations12 Nov 2021 Gerhard Wunder, Benedikt Groß, Rick Fritschek, Rafael F. Schaefer

The Jensen inequality is a widely used tool in a multitude of fields, such as for example information theory and machine learning.

Mutual Information Estimation

Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding

no code implementations1 Jun 2021 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 obtained for general coding and decoding tasks for wireless communication systems.

Mutual Information Estimation reinforcement-learning +1

Deep Learning for Channel Coding via Neural Mutual Information Estimation

1 code implementation7 Mar 2019 Rick Fritschek, Rafael F. Schaefer, Gerhard Wunder

However, one of the drawbacks of current learning approaches is that a differentiable channel model is needed for the training of the underlying neural networks.

Decoder Mutual Information Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.