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

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 …

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

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 present a novel adaptive deep joint source-channel coding (JSCC) scheme for wireless
image transmission. The proposed scheme supports multiple rates using a single deep …

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 …

Recent research on joint source channel coding (JSCC) for wireless communications has
achieved great success owing to the employment of deep learning (DL). However, the …

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 …

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 …

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