Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Task-oriented semantic communications have achieved significant performance gains.
However, the employed deep neural networks in semantic communications have to be …

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

Wireless technologies are growing unprecedentedly with the advent and increasing
popularity of wireless services worldwide. With the advancement in technology, profound …

End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost
communication efficiency by only transmitting the semantics of data, showing great potential …

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 …

We present a learning-based channel-adaptive joint source and channel coding (CA-JSCC)
scheme for wireless image transmission over multipath fading channels. The proposed …

In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-
adaptive digital semantic communications. In semantic communication systems with digital …

In existing semantic communication systems for image transmission, some images are
generally reconstructed with considerably low quality. As a result, the reliable transmission …

We introduce a vision transformer (ViT)-based deep joint source and channel coding
(DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output …