Matching-Based Channel Assignment for Cooperative Sensing in Full-Duplex Cognitive Radio Networks
Bibliographic record
Abstract
In cognitive radio networks (CRNs), cooperative spectrum sensing (CSS) has been widely used to increase the sensing accuracy. Also, in recent years, full duplex techniques have been introduced to CRNs, which allows secondary users (SUs) to send data while sensing the spectrum. In this work, we investigate the problem of cooperative spectrum sensing in multi-channel full-duplex (FD) CRNs. Specifically, we study the problem of channel assignment for CSS in FD-CRNs, which is optimizing the assignment of SUs for cooperatively sensing different channels. And an optimization problem is formulated for maximizing the throughput of FD-CRNs. The optimization problem is an NP-hard non-linear integer programming problem. For large scale CRNs, getting an accurate solution of the original problem in a predictable time is difficult. Inspired by matching theory, we model the formulated problem as a matching game and propose an efficient algorithm for obtaining the solution. We analyze the complexity of the algorithm and prove that a stable matching can be reached. Numerical results show that the proposed matching algorithm is more efficient with the increasing number of SUs when comparing with the other three algorithms.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".