Cooperative sensing scheduling in Cognitive Radio Networks with multiple Primary Networks
Bibliographic record
Abstract
With the emergence of secondary spectrum markets, it is envisioned that multiple Primary Networks (PRNs) with non-overlapping spectrum pools will be incorporated into Cognitive Radio Networks (CRNs). As a result, CRNs will have a greatly enhanced choice of accessible spectrum resources available to them, which, in turn, brings a significant increase in the diversity of available PRNs; this can greatly increase reliability and stability in the system performance experienced by secondary users (SUs) on the network. However, due to the nature of dynamic network environments, it is hard to meet the requirements of sensing task when the sensing resources, such as the number of participating SUs and A/D sampling capability, are limited. In this paper, we address this issue by studying the problem of cooperative sensing scheduling of CRNs for a dynamic multi-PRN environment. By jointly considering the dynamics of spectrum usage, and the channel conditions of SUs, cooperative spectrum sensing scheduling is formulated as two optimization problems, from the perspectives of primary users (PUs) and SUs, respectively. To solve these problems, two straightforward scheduling schemes are discussed: Random Scheduling and SNR-based Greedy Scheduling. To further improve the sensing performance, a cross entropy (CE) method-based sensing scheduling scheme is proposed. At last, simulation results validate the effectiveness of the proposed CE-based sensing scheduling scheme.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".