Modulation classification based on Gaussian mixture models under multipath fading channel
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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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".