Semantic communications is considered a promising technology that will increase the
efficiency of next-generation communication systems, particularly human-machine and …

As one of the key techniques to realize semantic communications, end-to-end optimized
neural joint source-channel coding (JSCC) has made great progress over the past few …

Semantic communication is a newly emerged communication paradigm that exploits deep
learning (DL) models to realize communication processes like source coding and channel …

Semantic communications are expected to accomplish various semantic tasks with relatively
less spectrum resource by exploiting the semantic feature of source data. To simultaneously …

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-
generation applications, where semantic features of data are transmitted using artificial …

In this paper, we propose a class of high-efficiency deep joint source-channel coding
methods that can closely adapt to the source distribution under the nonlinear transform, it …

Recent deep learning methods have led to increased interest in solving high-efficiency end-
to-end transmission problems. These methods, we call nonlinear transform source-channel …

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 …

We consider the wireless text transmission using joint source-channel coding (JSCC).
Classical source coding only considers the syntactic information based on probabilistic …

Recent works have shown that joint source-channel coding (JSCC) schemes using deep
neural networks (DNNs), called DeepJSCC, provide promising results in wireless image …