Strategies to maximize carried traffic in dual-mode cellular systems
Bibliographic record
Abstract
Dual-mode cellular systems based on the EIA/TIA IS-54 standard offer the eventual prospect of carrying up to six digital calls in the same bandwidth as a single analog call. During the transition from analog to digital service, however, the call-carrying capacity of such systems will be limited by the presence of existing analog users. In this situation, it is reasonable to ask if there are call-handling strategies that could increase the total traffic carried by providing preferential treatment to digital users. We consider four such strategies for maximizing the total traffic carried by a dual-mode cellular system. For two of these strategies, including the baseline "no-control" strategy we develop closed-form solutions for carried traffic and other related service statistics. The closed-form solution for the no-control case is then extended to provide a tight upper bound on carried traffic for any control strategy. We also present a method for finding the optimal control strategy by applying linear programming (LP) techniques. The strategies are compared for various proportions of analog and digital users and offered traffic levels. The findings show that it is actually quite difficult to obtain gains using strategies that exploit the difference in spectral efficiency between analog and digital calls, even with formally optimal strategies. While this is an unexpected finding, we feel the conclusion has been well validated and is now understood and explained in the paper.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".