Modulation classification based on Gaussian mixture models under multipath fading channel
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the classification of digital modulation schemes in the presence of multipath fading channels and additive noise. A novel modulation recognition approach is proposed based on Gaussian Mixture Models (GMM). Our basic procedure involves parameter estimation using GMM to set up an offline database and then to classify the received signal into different modulation schemes based on the database by using Kullback-Leibler (K-L) Divergence. In order to mitigate the negative impact from multipath fading channels, an iterative Maximum A Posteriori (MAP)-based channel estimation is used in conjunction with the Expectation-Maximization (EM) algorithm. Furthermore, Gaussian approximation is carried out to decrease the computational complexity. Monte Carlo simulations are conducted to evaluate the performance of individual modulation scheme classification. Numerical results show that the proposed approach is capable of recognizing various modulated signals with improved performance under AWGN and multipath fading channels.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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