Modulation Classification for Overlapped Signals via Clustering Analysis of Super-Constellations
Bibliographic record
Abstract
Modulation recognition (MR) plays an important role in military and civilian applications of cooperative and non-cooperative communications. The existing literature has introduced several MR methods for single-user scenarios but few papers have studied multi-user MR. This work proposes an MR method that employs a clustering analysis of super-constellation in a completely overlapped MU scenario. A super-constellation refers to the mapping of superposed symbols in the I/Q plane. A blind MR for stealthy decoding conversation between two users is considered with parameters like user gain, noise variance etc. being unknown in practical impairments such as carrier frequency offset, timing and phase offsets. The proposed algorithm utilizes agglomerative hierarchical clustering along with various cluster validation techniques to determine the optimal number of clusters and their respective centroids within the super-constellation. Subsequently, amplitude and phase-based features are extracted from these centroids to enable accurate MR. The simulation results demonstrate that the classification accuracy of the proposed method is i) significantly better than the features-based methods like cumulants and higher-order statistics; and ii) comparable with the deep learning-based methods that crucially rely on the availability of training data. Furthermore, our analysis reveals that the proposed method has significantly lower complexity than the existing techniques.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".