When NOMA Meets Multiuser Cognitive Radio: Opportunistic Cooperation and User Scheduling
Bibliographic record
Abstract
This correspondence paper investigates a novel non-orthogonal multiple access (NOMA) assisted overlay spectrum sharing framework for multiuser cognitive radio networks toward an enhanced spectrum utilization. In particular, one secondary user is scheduled to help forward the primary signal and convey its own signals as well by applying the NOMA principle. A reliability-oriented secondary user scheduling (R-SUS) scheme is first proposed with a target at minimal primary and secondary outage probabilities. Then, a fairness-oriented secondary user scheduling (F-SUS) scheme is proposed, such that all the candidate secondary users have an equal opportunity to be scheduled for the cooperation. Expressions of primary and secondary outage probabilities are derived in closed form to evaluate the resultant network reliability performance. The results reveal that: (1) the proposed R-SUS and F-SUS schemes can achieve a full diversity order for the primary and secondary transmissions, and (2) although the F-SUS scheme enhances user fairness, it suffers a higher secondary outage probability compared with the R-SUS scheme.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".