Efficient Clustering of Cognitive Radio Networks Using Affinity Propagation
Bibliographic record
Abstract
Cognitive radios must be able to form collaborative wireless network clusters in dynamically changing environments to achieve such desired objectives as interference resilience and low communications overhead. In this work, we explore the affinity propagation (AP) message-passing technique to efficiently group nodes in an ad hoc cognitive radio network (CRN). With the proposed approach, nodes exchange local messages with their immediate neighbours until a high quality set of clusterheads and a corresponding cluster structure emerges. The messages are calculated based on measures of similarity between the network nodes, which are selected based on application requirements and the objective of the grouping process. As an initial application, we focus on finding a small dominating set of a CRN with the aim of reducing the number of nodes that participate in key network functions such as resource management and routing table maintenance. To demonstrate the merits of the proposed clustering approach, the AP technique is evaluated on randomly generated open spectrum access network scenarios. The simulation results demonstrate that the proposed technique provides a smaller number of clusters than methods based on approximating a minimum size dominating set of the corresponding ad hoc network graphs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".