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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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 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".