Cross‐layer aware joint design of sensing and frame durations in cognitive radio networks
Bibliographic record
Abstract
The tradeoff between increasing secondary users’ (SUs’) throughput and decreasing interferences to primary user (PU) is an important problem in cognitive radio networks. Joint design of sensing duration and frame duration has a crucial impact on both these two conflicting attributes but has not been studied yet. In this study, using a cross‐layer approach, the authors investigate joint design of sensing duration and frame duration for the tradeoff. Specially, they consider that PU's traffic randomly changes within a secondary frame and multiple SUs contend to use the licensed channel based on widely used large/small‐scale‐backoff‐based MAC protocols. By modelling more realistic PU's traffic, imperfect spectrum sensing in PHY and multiple SUs’ access contention in MAC, the authors reformulate the sensing‐throughput tradeoff problem to maximise SUs’ throughput while restricting interference probability to PU under a tolerable level. Moreover, the optimal solution is analysed and a bi‐dimensional search algorithm is presented. Simulation results show that the authors’ proposal achieves better throughput performance than conventional approaches. They also show how the optimal solution varies with received PU's signal‐to‐noise ratio and PU's traffic distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".