Semantic communication is a new paradigm that exploits deep learning models to enable
end-to-end (E2E) communications processes, and recent studies have shown that it can …

Semantic communication is considered the future of mobile communication, which aims to
transmit data beyond Shannon's theorem of communications by transmitting the semantic …

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 …

Classical communication paradigms focus on accurately transmitting bits over a noisy
channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable …

Deep learning (DL) based semantic communication methods have been explored for the
efficient transmission of images, text, and speech in recent years. In contrast to traditional …

Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have
recently achieved remarkable performance for wireless image transmission. However, these …

This paper introduces a vision transformer (ViT)-based deep joint source and channel
coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple …

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 …

Considering the problem of joint source-channel coding (JSCC) for multi-user transmission
of images over noisy channels, an autoencoder-based novel deep joint source-channel …

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G)
networks need to support new AI tasks such as classification and clustering apart from data …