Spectrum Sensing Based on Novel Blind Pilot Detection Algorithm
Bibliographic record
Abstract
In this paper, we attempt to explore a new spectrum-sensing scheme, which involves a novel robust pilot-detection mechanism. Conventional signal (pilot) detection approaches rely on sampling the signal time-waveform or the corresponding frequency-spectrum. These approaches are seriously restricted to temporal variations and high noise-levels. We propose a new paradigm to transform the original received-signal waveform to the power spectrum and then the ultimate probabilistic function. Thus robust signal processing method such as clustering can be utilized to lead to the better pilot-detection performance of frequency-modulation (FM) broadcasted signals. To demonstrate the performance of our proposed pilot-tone detection and pilot-frequency estimation scheme, the corresponding Monte Carlo simulation results are compared with the conventional spectral-difference detector. Our proposed new pilot-tone detection and pilot-frequency estimation scheme lead to a significant performance margin compared to the conventional method, especially in low signal-to-noise ratios.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".