Secondary VoIP Capacity in Opportunistic Spectrum Access Networks with Friendly Scheduling
Bibliographic record
Abstract
In conventional cognitive radio, the primary network usually remains unchanged. In some cases, however, the primary network operator may wish to accommodate secondary user access. In this paper, we assess the secondary user VoIP capacity when primary basestation scheduling is designed to be secondary network friendly. Friendliness is measured by the number of connections that can be supported subject to typical quality of service constraints in the presence of delay tolerant primary traffic. An offline scheduler is first derived that maximizes friendliness using an integer linear program formulation. We show that this schedule can be found using a minimum cost flow graph construction in time complexity that is polynomial in the number of time slots. Two online scheduling algorithms are then compared that achieve various levels of friendliness. The first algorithm operates by having the primary network temporally shape its residual capacity subject to satisfying its own packet deadline constraints. The second algorithm assumes virtual secondary calls and applies scheduling to both primary traffic and virtual secondary traffic. Results are presented for a variety of parameters that show the degree to which friendly scheduling can improve secondary user VoIP capacity compared to non-friendly primary scheduling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".