As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image …
The recent deep learning-based joint source-channel coding (deep JSCC) framework has shown superior performance on end-to-end wireless image transmission without suffering from the “cliff effect”. However, a fundamental limit of current deep JSCC …
In this paper, we aim to redesign the vision Transformer (ViT) as a new backbone to realize semantic image transmission, termed wireless image transmission transformer (WITT). Previous works build upon convolutional neural networks (CNNs), which are …
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 can be collected under the name nonlinear transform source-channel …
In this paper, the problem of neural compression based image transmission over wireless channels is studied. Since all procedures are considered over wireless links, the quality of training is affected by wireless factors such as packet errors. In …