New structures for modulation classification and SNR estimation with applications to Cognitive Radio and Software Defined Radio
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it