Compressed Wavelet Packet-Based Spectrum Sensing With Adaptive Thresholding for Cognitive Radio
Bibliographic record
Abstract
Cognitive radio is a system to utilize spectrum holes efficiently as a solution of spectrum scarcity. The availability of channels for secondary users is determined in the spectrum sensing phase by energy detection. Energy levels of sampled primary user's (PU's) signal can be measured by wavelet transform with more accuracy compared with Fourier-based methods. Wavelet packet-based spectrum sensing measures the energy level at each subcarrier and sets the decision threshold. However, at the first step of energy detection for wideband spectrum sensing, high-rate analog-to-digital converter (ADC) sampling requires a large dynamic range and high-speed signal processors. In this paper, compressed sampling for PU's signal acquisition is proposed to reduce the rate of sampling and solve the implementation complexity of ADC. The simulation results verify that this mechanism is promising to estimate the power spectrum density (PSD) of PU's signals. The graphs prove low side-lobes of the detected PSD and acceptable probability of detection and false alarm due to the target values and certain compression ratio.
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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 | 0.000 | 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) | 0.001 | 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".