Approach for cluster-based spectrum sensing over band-limited reporting channels
Bibliographic record
Abstract
<p>In this study, the authors address the problem of bandwidth limitations of the reporting channels in cognitive radio (CR) networks. They propose a cluster-based spectrum-sensing approach that minimizes the bandwidth requirements by reducing the number of terminals reporting to the fusion centre to a minimal reporting set. The approach replaces the secondary base station by a local fusion centre and combats the destructive channel conditions by replacing the global reporting channels with local channels. They also propose a new approach to select the location of the local fusion centre using the general centre scheme in graph theory. The minimal dominating set (MDS) clustering algorithm is used to obtain the minimal set of clusters that keep the network connected. This study investigates how the sensing efficiency, the sensing accuracy, and the per-node throughput are affected by the cluster size, the number of clusters, and the reporting channels error. The results obtained reveal that the cluster-based cooperative sensing system outperforms the conventuional cooperative sensing system in terms of throughout capacity especially when the reporting channels are subjected to a high probability of error. A systematic way to find the optimal number of cooperative clusters that gives a minimum probability of false alarm is presented.</p>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".