Search Results for author: Tze-Yang Tung

Found 4 papers, 1 papers with code

DeepWiVe: Deep-Learning-Aided Wireless Video Transmission

no code implementations25 Nov 2021 Tze-Yang Tung, Deniz Gündüz

We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combining video compression, channel coding, and modulation steps into a single neural transform.

MS-SSIM SSIM +1

DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding

no code implementations25 Nov 2021 Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski, Deniz Gündüz

Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques.

Federated mmWave Beam Selection Utilizing LIDAR Data

3 code implementations4 Feb 2021 Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung, Szymon Kobus, Deniz Gunduz

Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection.

Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels

no code implementations2 Jan 2021 Tze-Yang Tung, Szymon Kobus, Joan Roig Pujol, Deniz Gunduz

Specifically, we consider a multi-agent partially observable Markov decision process (MA-POMDP), in which the agents, in addition to interacting with the environment can also communicate with each other over a noisy communication channel.

Multi-agent Reinforcement Learning reinforcement-learning

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