A Goal-Oriented Context-Aware Adaptive Semantic Communication Scheme Using a Semantic Mask Module
Bibliographic record
Abstract
By extracting and transmitting semantic information (SI), semantic communications (SC) can achieve communication goal and reliable transmission with much lower data rate among devices. However, devices in SC typically need to perform concurrent tasks, subject to varying available computational resources. Additionally, dynamic communication environments could dramatically deteriorate communication performance. To achieve robust SC with aforementioned uncertainties, this paper proposes a context-aware, adaptive rate, multitask SC scheme (CAAR-MTSC) to optimize the latent representation of transmitted data. The proposed scheme comprehensively considers the user’s perceptual needs, current channel conditions, and task-relevant information within the data. Specifically, we introduce a Semantic Mask Module (SMM) that dynamically controls the data rate based on channel conditions and user-defined perceptual metrics to obtain the best trade-off between task performance and data rate. To enhance multi-task performance, we formulate a triple trade-off information bottleneck (IB) optimization problem using rate-distortion perception. We further incorporate a knowledge distillation (KD) strategy to reduce the model size while maintaining performance. Simulation results indicate that, in low-SNR conditions, the proposed framework achieves up to a 12% improvement in SSIM and a 3.4% increase in classification accuracy while reducing data rate by 11%, thereby demonstrating its efficacy under resource-constrained scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".