Cluster Size Optimization in Cooperative Spectrum Sensing
Bibliographic record
Abstract
In this paper, we study and optimize the cooperation cluster size in cooperative spectrum sensing to maximize the throughput of secondary users (SUs). To calculate the effective throughput, we assume each SU spends just 1 symbol to negotiate with the other SUs in its transmission range. This is the minimum overhead required for each SU to broadcast its sensing decision to the other members of the cluster. When the number of SUs is large, the throughput spent for the negotiation is noticeable and therefore increasing the cooperation cluster size does not improve the effective throughput anymore. In this paper, we calculate the effective throughput as a function of the cooperation cluster size, and then we maximize the throughput by finding the optimal cluster size. Various numerical results show that when decisions are combined by the OR-rule, the optimum cooperation cluster size is less than when the AND-rule is used. On the other hand, the optimum cluster size monotonically decreases with the increase in the average SNR of the SUs. Another interesting result is that when the cluster size is optimized the OR-rule always outperforms the AND-rule.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".