Cyclostationary Spectrum Sensing in Cognitive Radios at Low SNR Regimes
Bibliographic record
Abstract
Spectrum sensing is one of the key elements in the establishment of cognitive radio. One of the most effective approaches for spectrum sensing is cyclostationary feature detection. Since modulated signals can be modeled as cyclostationary random signals, this feature can be used to recognize the cyclostationary modulated signal in a background of stationary noise even at low SNR regimes. This chapter reviews non-cooperative cyclostationary sensing approaches and reports recent advances in cooperative cyclostationary sensing algorithms. New results for cooperative cyclostationary spectrum sensing are then presented, which ensure better performance as well as faster and simpler operation. In the proposed schemes, each Secondary User (SU) performs Single-Cycle (SC) cyclostationary detection for fast and simple implementation, while collaboration between SUs in final decision on the presence or absence of the PU is explored to improve its performance. Furthermore, this chapter presents another look at the performance evaluation of cyclostationary detectors in terms of deflection coefficients.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".