Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Rapid natural language processing advances, such as OpenAI's ChatGPT, promise
profound transformations across multiple domains, including software development. This …

This paper presents a novel wireless image transmission paradigm that can exploit
feedback from the receiver, called JSCCformer-f. We consider a block feedback channel …

Deep neural network (DNN)-based channel code designs have recently gained interest as
an alternative to conventional coding schemes, particularly for channels in which existing …

Ultra-reliable short-packet communication is a major challenge in future wireless networks
with critical applications. To achieve ultra-reliable communications beyond 99.999%, this …

There have been significant research activities in recent years to automate the design of
channel encoders and decoders via deep learning. Due the dimensionality challenge in …

Deep learning has been used recently to learn error-correcting encoders and decoders
which may improve upon previously known codes in certain regimes. The encoders and …

Following the great success of Machine Learning (ML), especially Deep Neural Networks
(DNNs), in many research domains in 2010s, several ML-based approaches were proposed …

A new deep-neural-network (DNN) based error correction encoder architecture for channels
with feedback, called Deep Extended Feedback (DEF), is presented in this paper. The …