Optimal Cell Size in Multi-Hop Cellular Networks
Bibliographic record
Abstract
3G-based Multi-hop cellular networks (MCNs) inherit a special characteristic of 3G systems, namely the relationship between a cell's coverage and its capacity. At the planning stage, a cell may be statically set to have small coverage with high capacity, a large coverage with small capacity, or some other fixed settings in between. Such static settings, however, do not adapt to the projected dynamic nature of users in 3G-especially in an MCN environment. In this paper, we propose the Optimal Cell Size (OCS) scheme for a 3G TDD W-CDMA MCN multi-cell environment. Given the cell capacity function, users' distribution and demands, OCS yields optimal cell sizes that maximize the system throughput through balancing coverage and capacity. Not only can OCS cope with dynamic network conditions, but it can also be considered as an aid to the network planning process. To the best of our knowledge, this is the first multi-cell optimal cell size scheme in the context of MCNs.
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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".