New structures for modulation classification and SNR estimation with applications to Cognitive Radio and Software Defined Radio
Bibliographic record
Abstract
Modulation classification structures for M-PSK (M-ary Phase Shift Keying) and D-MPSK (Differential M-ary Phase Shift Keying) are presented. The modulation classifiers estimate the most likely value of the modulation index M that is present at the input of the receiver. The modulation classifiers are NDA (Non Data Aided) and are shown to have the following advantages: (1) they do not require prior carrier synchronization; (2) they have a compact fixed-point hardware implementation suitable for implementation in devices such as FPGAs (Field Programmable Gate Arrays) and ASICs (Application Specific Integrated Circuits); (3) they require only 1 sample/symbol; (4) relatively few symbol intervals are needed in order to achieve good detection certainty; (5) the decision thresholds for the modulation classifiers are not dependent upon the signal amplitude and are resilient to imperfections in the AGC (Automatic Gain Control) circuit; and (6) parts of the circuits can be used concurrently to perform SNR (Signal to Noise Ratio) estimation. Applications of the proposed structures to CR (Cognitive Radio) and SDR (Software Defined Radio) are discussed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".