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

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-
time applications, attributable to their compactness, cost-effectiveness, and ease of …

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

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 …

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 …

Deep learning-based joint source-channel coding (DJSCC) is expected to be a key
technique for {the} next-generation wireless networks. However, the existing DJSCC …

Although Shannon theory states that it is asymptotically optimal to separate the source and
channel coding as two independent processes, in many practical communication scenarios …

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 …

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