ABConv: Attention Based Convolution for Automatic Modulation Recognition
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 recognition (AMR), an intermediate process of signal detection and signal demodulation, has been widely used in various fields. We introduce ABConv, a novel machine learning (ML) filter designed for AMR tasks, leveraging Attention-Based Convolution. Unlike the feature extraction approach using deep learning in AMR tasks, we focus on signal processing methods. Drawing parallels between attention and autocorrelation functions, our model dynamically generates convolution kernels based on attention scores to perform channel blind equalization. ABConv demonstrates state-of-the-art (SOTA) performance with in AMR tasks, surpassing multiple existing advanced algorithms. Additionally, we provide a preliminary visual analysis.
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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 | 0.001 |
| 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