Strategies to maximize carried traffic in dual-mode cellular systems
Why this work is in the frame
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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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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it