Effective capacity optimization based on overlay cognitive radio network in gamma fading environment
Bibliographic record
Abstract
Traditionally, the frequency spectrum is licensed to users who have the exclusive right to access the allocated band. However, an unlicensed (cognitive) user may share a frequency band with a licensed (primary) owner as long as the interference is below a certain threshold. This makes capacity analysis a critical important issue in these networks. Lots of research on cognitive radio (CR) networking have now focused on the satisfaction of quality-of-service (QoS) demands for cognitive users (CU). In this paper, we study how the delay QoS requirements affect the dynamic spectrum access (DSA) strategy on network performance. We treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity concept, focusing on one of the dominant DSA schemes: overlay. Optimal power allocation scheme is obtained. This scheme considers the transmit-power/interference-power constraints and the primary user activity. Performance analysis and numerical evaluations demonstrate the proposed effective capacity optimization on the DSA overlay scheme. The impact of delay QoS requirements and other related parameters are evaluated as well.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".