no code implementations • 2 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 +1
3 code implementations • 4 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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 16 Jun 2022 • Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski, Deniz Gunduz
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem.
no code implementations • 24 Nov 2022 • Ecenaz Erdemir, Tze-Yang Tung, Pier Luigi Dragotti, Deniz Gunduz
In GenerativeJSCC, we carry out end-to-end training of an encoder and a StyleGAN-based decoder, and show that GenerativeJSCC significantly outperforms DeepJSCC both in terms of distortion and perceptual quality.