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

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 …

The emerging field semantic communication is driving the research of end-to-end data
transmission. By utilizing the powerful representation ability of deep learning models …

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

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 …

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 …

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 …

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

Multi-task learning (MTL) is an efficient way to improve the performance of related tasks by
sharing knowledge. However, most existing MTL networks run on a single end and are not …