On-off voice capacity of single-hop cognitive radio networks with distributed channel access control
Bibliographic record
Abstract
Cognitive radio networks (CRNs) have emerged as a promising solution to spectrum under-utilization and congestion. Supporting quality of service (QoS) aware services over CRNs is always challenging due to the randomness of the spectrum availability. In this paper, we consider a set of fully-connected cognitive radios (secondary users) operating over a time-slotted primary network, accessing the channel at the spectrum holes without interfering with primary users. As the capacity analysis is one of the basic steps to guarantee QoS, we analyze the on-off voice capacity of single-channel single-hop fully-connected CRNs under distributed channel access control. The voice capacity is represented in terms of the number of simultaneous independent voice calls that the secondary network can support, providing stochastic delay guarantee. Our analytical results have a close match with the simulation results. With proper medium access control, capacity analysis can help to develop a call admission control policy for QoS provisioning in non-fully-connected CRNs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".