Bibliographic record
Abstract
Cognitive Radio Networks (CRNs) provide a solution for the spectrum scarcity problem facing the wireless communications community. However, due to the infancy of CRNs, further research is needed before we can truly benefit from CRNs. The basic concept of CRNs relies on utilizing the unused spectrum of a primary network, without interfering with the activity of primary users (PUs). In order to successfully achieve that, users in a CRN has to perform spectrum sensing, spectrum management, spectrum mobility, and spectrum sharing. The latter, which is the focus of our research, deals with how secondary users (SUs) share the unused spectrum. \n \nFurthermore, to be able to utilize CRNs in practical applications, a certain level of quality-of-service (QoS) should be guaranteed to SUs in such networks. QoS requirements vary according to the application. Interested in voice communications, we propose a packet scheduling scheme that orders the SUs' transmissions according to the packet dropping rate and the number of packets queued waiting for transmission. Two medium access control (MAC) layer protocols, based on the mentioned scheduling scheme, are proposed for a centralized CRN. In addition, the scheduling scheme is adapted for a distributed CRN, by introducing a feature that allows SUs to organize access to the available spectrum without the need for a central unit. \n \nFinally, extensive simulation based experiments are carried out to evaluate the proposed protocols and compare their performance with that of other MAC protocols designed for CRNs. These results reflect the effectiveness of our proposed protocols to guarantee the required QoS for voice packet transmission, while maintaining fairness among SUs in a CRN.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".