A channel allocation scheme for both uniform and non-uniform traffic environment in cellular networks
Bibliographic record
Abstract
We propose a channel allocation scheme called unified channel allocation (UCA) appropriate for both uniform and non-uniform traffic environment. The currently available channel allocation schemes such as fixed, dynamic and hybrid schemes are not suitable for all traffic conditions. The fixed channel allocation scheme does not perform well in non-uniform traffic environment and the dynamic scheme does not do well in uniform heavy traffic conditions. In real situations, however, the user traffic pattern changes with time and space. The UCA scheme adaptively changes its channel recommendation pattern for each cell to satisfy the current demand under all traffic conditions. We demonstrate through simulation that the proposed scheme, using the virtual channel set (VCS), performs better than the other schemes in all traffic conditions. Furthermore, the UCA scheme is a practical scheme, because it can be implemented without knowledge of the traffic fluctuations with time and space, since the underlying VCSs will adapt to traffic fluctuations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".