Automatic Modulation Classification Based on Kernel Density Estimation
Bibliographic record
Abstract
In this paper, we propose an efficient automatic modulation classification (AMC) scheme for a group of narrowband and digitally modulated signals such as quadrature phase-shift keying (QPSK), 16-PSK, 64-PSK, 4-quadratic-amplitude modulation (QAM), 16-QAM, and 64-QAM. The classification was performed by analyzing the probability density distribution for the real and imaginary parts of the modulated signals. To simplify the complexity of the proposed approach, we performed the classification in two stages: first, we classified the modulation between QAM and PSK signaling, and then, we determined the M-ary order of the modulation by developing kernel density estimation, which is typically used in nonparametric methods for the estimation of the probability density function of a random variable with finite data samples. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. It is observed that this simple efficient technique can find applications in blind AMC, as the performance comparison with the state of the art is promising.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".