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Found 11 papers, 3 papers with code
SwinJSCC: Taming Swin Transformer for Deep Joint Source-Channel Coding
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 years.
Toward Intelligent and Efficient 6G Networks: JCSC Enabled On-Purpose Machine Communications
Driven by the vision of "intelligent connection of everything" toward 6G, the collective intelligence of networked machines can be fully exploited to improve system efficiency by shifting the paradigm of wireless communication design from naive maximalist approaches to intelligent value-based approaches.
NeurJSCC Enabled Semantic Communications: Paradigms, Applications, and Potentials
We first focus on those two paradigms of NeurJSCC by identifying their common and different components in building end-to-end communication systems.
Improved Nonlinear Transform Source-Channel Coding to Catalyze Semantic Communications
First, we introduce a contextual entropy model to better capture the spatial correlations among the semantic latent features, thereby more accurate rate allocation and contextual joint source-channel coding are developed accordingly to enable higher coding gain.
Toward Adaptive Semantic Communications: Efficient Data Transmission via Online Learned Nonlinear Transform Source-Channel Coding
Specifically, we update the off-the-shelf pre-trained models after deployment in a lightweight online fashion to adapt to the distribution shifts in source data and environment domain.
WITT: A Wireless Image Transmission Transformer for Semantic Communications
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).
Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding
Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications.
Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission
However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM).
Wireless Deep Video Semantic Transmission
In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels.
Distributed Image Transmission using Deep Joint Source-Channel Coding
In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node.
Nonlinear Transform Source-Channel Coding for Semantic Communications
In the considered model, the transmitter first learns a nonlinear analysis transform to map the source data into latent space, then transmits the latent representation to the receiver via deep joint source-channel coding.
