Network coding based wideband compressed spectrum sensing
Bibliographic record
Abstract
One of the fundamental components in cognitive radios (CRs) is spectrum sensing. For sensing the wide range of frequency bands, CRs need high sampling rate analog to digital converters (ADCs) which have to operate at or above the Nyquist rate. The high operating rate constitutes a major implementation challenge. Compressive sensing (CS) is a method that may overcome this problem. Sub-Nyquist rate can be used for CS recovery algorithms such as ℓ1-minimization. While boundary information of all frequency sub-bands is available, a more efficient recovery algorithm based on ℓ2/ℓ1-minimization can be used instead of ℓ1-minimization. In cognitive radio systems, network coding could be used for primary users (PUs) to increase packet transmissions. Furthermore, network coding provides a structure for vacant sub-bands of spectrum and makes the spectrum more predictable. Using this information that network coding provides us, we combine ℓ1-minimization and ℓ2/ℓ1-minimization algorithms with network coding for compressive spectrum sensing. Our methods require reduced signal sampling rate and result in improved false alarm (FA) and missed detection (MD) probabilities for idle band detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".