Novel Cooperative Automatic Modulation Classification Based on Received-Signal Quality
Bibliographic record
Abstract
A novel cooperative automatic modulation classification (CAMC) scheme based on the new fusion rule related to the received-signal quality is proposed in this paper. A twostage CAMC technique is proposed for the wireless sensor network containing a fusion center. In the first stage, each individual sensor in the network undertakes a graph-based automatic modulation classification (AMC) mechanism to identify the modulation type of an unknown target signal and also estimates the quality of the received signal. In the second stage, the fixed fusion center combines (accumulates) the local decisions from all sensors’ individual decision-weights dependent on blind estimation of the received-signal quality. Through sensors’ cooperation and the aforementioned decision fusion based on the individual received-signal qualities, our new CAMC scheme could mitigate the negative effects of channel distortion and noise often encountered in the existing single-node AMC methods. Furthermore, by allocating the majority of the computational load to the local sensors instead of the fusion center, our new approach can reduce the overall computational complexity while maintaining a low network-communication overhead. Extensive Monte Carlo simulations demonstrate the superior performance and robustness of our new CAMC scheme in comparison with the existing single-node AMC and CAMC methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".