Strong Masting Conjecture for Multiple Size Hexagonal Tessellation in GSM Network Design
Bibliographic record
Abstract
One way to improve cellular network performance is to use efficient handover method and design pattern among other factors. The efficient design pattern has been proven geometrically to be hexagonal (Hales, 2001, pp. 1- 22) due to its maximum tessellable area coverage. But uneven geographical distribution of subscribers requires tessellable hexagons of different radii due to variation of costs of GSM masts. This will call for an overlap difference. The constraint of minimum overlap difference for multiple cell range is a new area that is untapped in cell planning. This paper addresses such multiple size hexagonal tessellation problem using a conjecture. Data from MTN River State-Nigeria, was collected. Multiple Size Hexagonal Tessellation Model (MSHTM) conjecture for masting three (3) different size MTN GSM masts in River State, accounted for least overlap difference with area of 148.3km2 using 36 GSM masts instead of the original 21.48 km2 for 50 GSM masts. Our conjecture generally holds for k-different (k>=2 ) cell range.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".