Capacity improvement in cellular systems with reuse partitioning
Bibliographic record
Abstract
Reuse Partitioning (RP) is a simple technique that can be used to increase the capacity of a cellular system. With RP, a cell is divided into several concentric regions, each associated with a different cluster size. In this paper, a Markov chain model is developed to evaluate the call blocking probability, Pb, of the basic (no channel rearrangement) n-region RP using fixed channel allocation (FCA). Channel rearrangements are introduced to further improve the capacity. For a certain RP scheme with multiple channel rearrangements (MCR), Pb is shown to have a known product-form solution. It is found that a single channel rearrangement scheme performs almost as well as the MCR scheme. One advantage of MCR is that it reduces the difference in Pb experienced by calls in the different regions. It is shown that the capacity with two-region (four-region) RP with MCR is about 25% (45%) higher than that of a conventional FCA system. The effect of moving users on call blocking and dropping probabilities is also examined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".