Simple Channel Sensing Order in Cognitive Radio Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In cognitive radio networks (CRNs), effective and efficient channel exploitation is imperative for unlicensed secondary users to seize available network resources and improve resource utilization. In this paper, we propose a simple channel sensing order for secondary users in multi-channel CRNs without a priori knowledge of primary user activities. By sensing the channels according to the descending order of their achievable rates with optimal stopping, we show that the proposed channel exploitation approach is efficient yet effective in elevating throughput and resource utilization. Simulation results show that our proposed channel exploitation approach outperforms its counterparts by up to 18% in a single-secondary user pair scenario. In addition, we investigate the probability of packet transmission collision in a multi-secondary user pair scenario, and show that the probability of collision decreases as the number of channels increases and/or the number of secondary user pairs decreases. It is observed that the total throughput and resource utilization increase with the number of secondary user pairs due to increased transmission opportunities and multi-user diversity. Our results also demonstrate that resource utilization can be further improved via the proposed channel exploitation approach when the number of secondary user pairs approaches the number of channels.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it