The joint source-channel coding (JSCC) framework leverages deep learning to learn from
data the best codes for source and channel coding. When the output signal, rather than …

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 …

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 …

Deepjoint source-channel coding (D-JSCC) provides several advantages over conventional
coding schemes, in which source and channel coding are designed separately. For …

Recent works have shown that the task of wireless transmission of images can be learned
with the use of machine learning techniques. Very promising results in end-to-end image …

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

In this paper, we propose an iterative source error correction (ISEC) decoding scheme for
deep-learning-based joint source-channel coding (Deep JSCC). Given a noisy codeword …

Learning-based image/video codecs typically utilize the well known auto-encoder structure
where the encoder transforms input data to a low-dimensional latent representation. Efficient …

Almost all wireless communication sys-tems today are designed based on essentially the
same digital approach, that separately optimizes the compression and channel coding …

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; …