Leveraging Stable Diffusion with Context-Aware Prompts for Semantic Communication
Why this work is in the frame
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Bibliographic record
Abstract
Semantic communication in wireless image transmission leverages the meaning embedded in the image data, aiming to compress, transmit, and reconstruct images based on their semantic content rather than purely pixel data. This paradigm shift allows more efficient utilization of bandwidth and computational resources, focusing on extracting key features and contextual information that is critical for ensuring that the essential content of the image is preserved and accurately conveyed. In this study, we present a novel Stable Diffusion-based semantic communication (SDSC) framework that demonstrates high performance, characterized by an elevated bandwidth compression ratio (BCR) and robust noise tolerance achieved by diffusion mechanism integrating supplementary prompts. Our approach utilizes pre-trained modules of a Variational autoencoder (VAE) and a modified U-shaped network (UNet) to enable robust semantic encoding, decoding, and effective channel denoising. This scheme significantly enhances the system's ability to preserve data integrity and meaning in noisy environments. By introducing additional context-aware prompts during transmission, we improve the accuracy of received information and mitigate the adverse effects of interference and noise. Extensive simulations show that our framework outperforms previous innovative models, demonstrating superior communication fidelity and efficiency under various challenging conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it