We consider the problem of joint source and channel coding of structured data such as
natural language over a noisy channel. The typical approach to this problem in both theory …

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

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 …

For reliable transmission across a noisy communication channel, classical results from
information theory show that it is asymptotically optimal to separate out the source 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 …

We consider wireless transmission of images in the presence of channel output feedback.
From a Shannon theoretic perspective feedback does not improve the asymptotic end-to …

We propose a joint source and channel coding (JSCC) technique for wireless image
transmission that does not rely on explicit codes for either compression or error correction; …

We consider an information transmission scheme where a stream of messages is encoded
using a variable length code, and the code sequence, obtained as a concatenation of …

We consider the case of two correlated binary information sequences. Instead of
compressing the information using source coding, both sequences are independently …

We propose deep learning based communication methods for adaptive-bandwidth
transmission of images over wireless channels. We consider the scenario in which images …