Optimal Wideband Spectrum Sensing Framework for Cognitive Radio Systems
Bibliographic record
Abstract
An optimal wideband spectrum sensing framework which identifies secondary transmission opportunities over multiple nonoverlapping narrowband channels is presented. The framework, which is referred to as multiband sensing-time-adaptive joint detection, improves the overall secondary user performance while protecting the primary network and keeping the harmful interference below a desired low level. Considering a periodic sensing scheme, the detection problem is formulated as a joint optimization problem to maximize the aggregate achievable secondary throughput capacity given a bound on the aggregate interference imposed on the primary network. It is demonstrated that the problem can be solved by convex optimization if certain practical constraints are applied. Simulation results attest that the proposed wideband spectrum sensing framework achieves superior performance compared to contemporary frameworks. An efficient iterative algorithm which solves the optimization problem with much lower complexity compared to other numerical methods is presented. It is established that the iteration-complexity and the complexity-per-iteration of the proposed algorithm increases linearly as the number of optimization variables (i.e., the number of narrowband channels) increases. The algorithm is evaluated via simulation and is shown to obtain the optimal solution very quickly and efficiently.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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".