Enhanced Cyclostationary Detector Complexity Based on Haar Wavelet and Signed Correlator
Bibliographic record
Abstract
The spectrum sensing function plays a significant role in the performance of cognitive radio (CR).Spectrum sensing specifies if free channels exist and identifies free channels for secondary users, actively helping in the improvement of spectrum usage and recognizing available channels in CR systems.Cyclostationary feature detection (CFD) is a spectrum sensing method that detects signals depending on different characteristics such as carrier frequency, modulation types, cyclic frequency, and symbol rates with an extremely low signal-to-noise ratio.At low SNR, CFD achieves a detection process with a high computation complexity.This paper designs Enhanced Cyclostationary Detector complexity with improved detection speed performances.For the sake of minimizing system complexity, utilizing the advantages of the Haar wavelet transform and signed correlator method for estimating the cyclic spectra of a detected signal.The proposed method performance was evaluated over Rayleigh flat fading and AWGN channel that had low SNR values.The acquired simulation results indicated the efficiency of the proposed method in terms of reduction 70% in complexity, 60% in time, and 7% in memory storage, with improved detection performance that is about 8% compared to conventional method at low SNR values reach to -30dB.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".