Power-Efficient Wideband Spectrum Sensing for Cognitive Radio Systems
Bibliographic record
Abstract
This paper proposes a wideband spectrum sensing architecture by utilizing a one-bit quantization at the cognitive radio receiver. A window-based autocorrelation is utilized to provide the power spectral density of the quantized signal. Closed-form expressions are derived for the one-bit quantized correlation, where the critical impact of the ultralow resolution is quantified. It is shown that the introduced method can still provide enough information about the sparse spectrum even at low signal-to-noise ratios (SNR). A suboptimal detection algorithm is presented to sense whether individual subbands are occupied or vacant. To evaluate the detection algorithm, the probability of false alarm and probability of detection are simulated for various system parameters. The sensing performance and the accuracy of the offered expressions are justified through comparisons with respective results from computer simulations. When compared to other methods, in addition to the significant saving in power, results indicate that, with the proper parameter selection and SNR down to -12 dBs, the proposed method provides low complexity, low sensing period, and better performance for less sparsity levels even though an aggressive quantization has been applied.
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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 | 0.001 |
| 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 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".