Application‐specific spectrum sensing method for cognitive sensor networks
Bibliographic record
Abstract
The authors address an important aspect of spectrum sensing that has been often overlooked in the cognitive radio (CR) research. Although CR is supposed to be aware of its surrounding, most existing articles do not consider the characteristics of secondary users in the optimisation of sensing period. In this study, based on a continuous‐time Markov chain model for cognitive sensor networks and energy detection method, the authors propose an application‐specific spectrum sensing method that obtains the optimal sensing period according to the characteristics of both ‘primary and secondary’ users (hybrid scheme). The authors define and analytically derive two parameters, the interference ratio and the undetected opportunity ratio, and analytically find the optimum sensing period. Numerical and simulation results indicate that our proposed method is able to provide an optimal sensing period, that is customised for different cognitive networks. The proposed method significantly increases the system throughput by up to 11% and reduces the network's power consumption by as low as 33%. Finally, the trade‐off between the throughput maximisation and power consumption minimisation is 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".