Classification of RF Signals Based on Image and Sequence Inputs
Why this work is in the frame
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Bibliographic record
Abstract
Radio frequency (RF) signals are widely used in commercial and military wireless communications, and the accurate classification of such signals is of great theoretical significance and practical application value. This study aims to solve the practical problem of RF signal classification based on a 5-classified signal dataset (abbreviated as 2021 dataset) provided by a company. In this paper, we design ResNet18 based on constellation map input, ResNet18 based on hybrid map input and CNN network based on sequence input, and compare and analyze the performance of the deep learning algorithms by exploring the deep learning algorithms under two input modes: image and sequence. It is shown that the classification accuracy of ResNet18 using hybrid graph input reaches 95.79%, while the CNN model with sequence input performs better in terms of classification accuracy and real-time performance, with an accuracy of 98.22%, and the number of parameters is only about 1/8 of that of ResNet18.
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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 | 0.000 |
| 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