A Design of Wireless Meta Semantic Communication System: An Abstract Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we consider the channel-aware abstract (meta) semantic communication system design in wireless networks with stochastic channels. Firstly, based on the “three sigma principle” of Gaussian distribution, we give the definition of “distinguishability” of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> independent Gaussian distributions and propose an integrated transmission and discrimination model for abstract semantic communication. Then, we formulate the optimization problem as a transmission symbol adjustment optimization problem and give the optimal closed-form transmission symbol solution under the Gaussian channels. Finally, based on the abstract meta semantics, we give a deep learning based communication framework for the image classification task. Simulation results on the image classification task show that the deep learning based communication framework is adaptive to the transmission power and maintains a consistently high-level classification accuracy (above 0.99 on MNIST and 0.91 on CIFAR10) across all channel 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 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