Bibliographic record
Abstract
In cognitive radio networks, a slotted time structure is widely adopted. Accordingly, the slot length is a factor that can largely affect the performance of cognitive radio networks. In this paper, a slot length configuration scheme is proposed. In the proposed scheme, we assume imperfect spectrum sensing. The spectrum sensing result is considered when configuring the slot length. Therefore, slots with different sensing results have different slot lengths. This setting fully takes into account the fact that the sojourn time of channel idle state and busy state are usually different. An optimization problem to find out the optimal slot length configuration is formulated (which maximizes the secondary throughput) and analyzed. In the formulated problem, primary activities are protected by limiting the percentage of time that the primary activities are interfered with, and the energy efficiency of the secondary system is guaranteed by limiting the percentage of time for spectrum sensing. After a theoretical analysis of the problem, an algorithm is proposed to solve it. In the case of perfect spectrum sensing, another algorithm with low complexity is developed to solve the problem. The numerical results demonstrate that, by having different slot lengths with different sensing results, largely improved performance can be achieved. Impacts of system parameters on the secondary system performance are also discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".