Asymptotic Scheduling Gains in Point-to-Multipoint Cognitive Networks
Bibliographic record
Abstract
We study simultaneous channel sharing of collocated primary and secondary networks at three different levels of coexistence: pure interference, asymmetric, and symmetric. At the pure interference level, both networks operate simultaneously in the same frequency band regardless of their interference to each other. At the asymmetric level, only the secondary network performs user scheduling based on various degrees of interference and channel gain knowledge while at the symmetric level both networks do so. Using a lemma on the asymptotic behavior of the largest order statistic and a proposition on the asymptotic sum of lower order statistics, we derive asymptotic primary and secondary sum-rates under simultaneous channel sharing at each coexistence level. As a baseline comparison, time-division (TD) channel sharing is considered. While maintaining the same asymptotic primary sum-rate, the asymptotic secondary sum-rate under TD is compared with that achievable by simultaneous channel sharing. The results indicate that simultaneous channel sharing at both asymmetric and symmetric co-existence levels can outperform TD. Furthermore, this enhancement is achievable asymptotically when user scheduling in uplink mode is based only on the interference gains to the opposite network and not on a network's own channel gains.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".