Effective capacity optimization for cognitive radio network based on underlay scheme in gamma fading channels
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Bibliographic record
Abstract
As fundamental spectrum sensing and access techniques in cognitive radio networks (CRN) matured in last decade, the satisfaction of quality-of-service (QoS) demands for cognitive users (CU) has attracted lots of research attention. In this paper, we study how the delay QoS requirements affect the dynamic spectrum access (DSA) strategy on network performance. We first treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity theory, focusing on the dominant DSA scheme: underlay. We show that the roles that the transmit-power/interference-power constraints play in optimizing CUs' throughput vary significantly with the delay QoS requirements. Performance analysis and numerical evaluations are provided to demonstrate the effective capacity of CRN based on underlay scheme, taking into consideration the impact of delay QoS requirements and other related parameters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it