Automatic Modulation Classification for Cognitive Radio Systems using CNN with Probabilistic Attention Mechanism
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
This paper studies automatic modulation classification (AMC) for cognitive radio systems. We propose a deep learning neural network approach enhanced with an intelligent attention mechanism to correctly classify, detect, and segment spatially distributed modulation data. AMC is achieved by training the neural network to focus only on specific significant regions learnt using the attention mechanism. The proposed approach is tested for detection efficiency and accuracy to distinguish different modulation data using the publicly available RML2016.10a dataset. The outcome shows the accuracy of the proposed scheme is comparable to other state-of-the-art deep learning algorithms with a reduced complexity. The real-time assessment of the temporal states is achieved based on the spectral characteristics of modulation constellation images at various signal-to-noise ratio (SNR) values. The model performance is evaluated using mean average precision (mAP), F1 score, and speed-accuracy trade-off.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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