Joint Early Exit and Structured Pruning for Automatic Modulation Classification in Vehicular Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automatic Modulation Classification (AMC) is essential for wireless communication systems, particularly in resource-constrained environments such as vehicular networks. However, deploying Deep Neural Networks (DNNs) on receivers is challenging due to computational and energy constraints. To address this, we propose the first joint integration of early exit and structured pruning for AMC, optimizing computational efficiency without significantly compromising accuracy. Through theoretical analysis, we demonstrate the unique interactions between these techniques and the non-intuitive impact of pruning on early-exit models. Our experiments evaluate the impact of various pruning criteria, pruning percentages, and early-exit thresholds. Our results reveal that the effectiveness of different pruning criteria in early-exit AMC models is highly context-dependent, with no universally optimal strategy, and that increasing pruning levels and early-exit thresholds intensifies the trade-off between computational efficiency and classification accuracy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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