Data and Knowledge Dual-Driven Automatic Modulation Classification for 6G Wireless Communications
Bibliographic record
Abstract
Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".