27 citations · 41 across the 12 of their papers we have counts for
13 papers
Joint Source-Channel Coding: Fundamentals and Recent Progress in Practical Designs
Deniz Gündüz, Michèle A. Wigger, Tze-Yang Tung +2
Semantic- and task-oriented communication has emerged as a promising approach to reducing the latency and bandwidth requirements of next-generation mobile networks by transmitting…
Multi-level Reliability Interface for Semantic Communications over Wireless Networks
Tze-Yang Tung, Homa Esfahanizadeh, Jinfeng Du +1
Semantic communication, when examined through the lens of joint source-channel coding (JSCC), maps source messages directly into channel input symbols, where the measure of success…
AdaSem: Adaptive Goal-Oriented Semantic Communications for End-to-End Camera Relocalization
Qi Liao, Tze-Yang Tung
Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source…
Generative Joint Source-Channel Coding for Semantic Image Transmission
Ecenaz Erdemir, Tze-Yang Tung, Pier Luigi Dragotti +1
Recent works have shown that joint source-channel coding (JSCC) schemes using deep neural networks (DNNs), called DeepJSCC, provide promising results in wireless image transmission…
Deep Joint Source-Channel Coding for Semantic Communications
Jialong Xu, Tze-Yang Tung, Bo Ai +3
Semantic communications is considered as a promising technology to increase the efficiency of next-generation communication systems, particularly targeting human-machine and machin…
Deep Joint Source-Channel and Encryption Coding: Secure Semantic Communications
Tze-Yang Tung, Deniz Gunduz
Deep learning driven joint source-channel coding (JSCC) for wireless image or video transmission, also called DeepJSCC, has been a topic of interest recently with very promising re…
DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that modern machine learning techniques can provide an alternative approach to the long-standing joint source-channel coding (JSCC) problem. Very promising…
DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding
Tze-Yang Tung, David Burth Kurka, Mikolaj Jankowski +1
Recent works have shown that the task of wireless transmission of images can be learned with the use of machine learning techniques. Very promising results in end-to-end image qual…
DeepWiVe: Deep-Learning-Aided Wireless Video Transmission
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…
Federated mmWave Beam Selection Utilizing LIDAR Data
Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung +2
Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection. For vehicle-to-infra…
Effective Communications: A Joint Learning and Communication Framework for Multi-Agent Reinforcement Learning over Noisy Channels
Tze-Yang Tung, Szymon Kobus, Joan Roig Pujol +1
We propose a novel formulation of the "effectiveness problem" in communications, put forth by Shannon and Weaver in their seminal work [2], by considering multiple agents communica…
SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity
Tze-Yang Tung, Deniz Gündüz
A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved…
SPRT Based Transceiver for Molecular Communications
Tze-Yang Tung, Urbashi Mitra
Achieving precise synchronisation between transmitters and receivers is particularly challenging in diffusive molecular communication environments. To this end, point-to-point mole…