Performance Analysis of Cognitive Radio Spectrum Access With Prioritized Traffic
Bibliographic record
Abstract
Dynamic spectrum access (DSA) is an important design aspect for cognitive radio networks. Most of existing DSA schemes are to govern unlicensed user (i.e., secondary user, SU) traffic in a licensed spectrum without compromising the transmissions of the licensed users, in which all the unlicensed users are typically treated equally. In this paper, prioritized unlicensed user traffic is considered. Specifically, the unlicensed user traffic is divided into two priority classes (i.e., high and low priority). We consider a general setting in which the licensed users' transmissions can happen at any time instant. Therefore, the DSA scheme should perform spectrum handoff to protect the licensed user's transmission. Different DSA schemes (i.e., centralized and distributed) are considered to manage the prioritized unlicensed user traffic. These DSA schemes use different handoff mechanisms for the two classes of unlicensed users. We also study the impact of subchannel reservation for high-priority SUs in both DSA schemes. Each of the proposed DSA schemes is analyzed using a continuous-time Markov chain. For performance measures, we derive blocking probability, the probability of forced termination, call completion rate, and mean handoff delay for both high- and low-priority unlicensed users. The numerical results are verified using simulations.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".