Capacity Analysis and Call Admission Control in Distributed Cognitive Radio Networks
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Bibliographic record
Abstract
In this paper, homogeneous voice traffic in a single-channel cognitive radio network (CRN) is considered. We analyze the constant-rate voice capacity of a fully-connected network with slot-ALOHA and round-robin channel access, and propose two call admission control (CAC) algorithms for a non-fully-connected network with slot-ALOHA channel access. Different from the existing work in literature, transmission of multiple packets in a single time-slot is considered. Two discrete-time Markov chain based approaches are used for the capacity analysis of the two channel access schemes, respectively. It is shown that the number of voice packets that can be transmitted in a time-slot has a significant impact on the system capacity. The capacity analysis results of the slot-ALOHA scheme is used to develop a CAC procedure when all the voice flows have an identical statistical delay requirement. Further, two CAC algorithms (A1 and A2) are developed for a network with voice traffic flows having different delay requirements in which one (A1) is based on the theory of effective capacity and is considered as a benchmark to compare with the other. Simulation results demonstrate that algorithm A2 performs better than algorithm A1, and that a relaxed delay requirement leads to an increase in the network capacity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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