Models for Call Acceptance Based on Handoff Guarantees
Bibliographic record
Abstract
Call admission control (CAC) is important for cellular wireless networks to provide quality-of-service (QoS) requirements to users. Static and adaptive CAC schemes, respectively, make unrealistic assumptions about the distributions of the handoff call arrival process and the number of users in a cell. Handoff arrivals are usually assumed to follow Poisson process in static CAC schemes for Poisson new call arrivals and exponentially distributed call holding and cell residence times. We use a simple proof to show that this assumption of Poisson handoff arrival process is not justified for a two-cell wireless network. In general, we find that the handoff process can be captured by a two-dimensional Markov chain. We propose a novel adaptive CAC scheme for the two-cell system which accepts a new call if it can guarantee, with a certain probability, that a user's call will be maintained irrespective of its (his/her) movement in the system. Then, we extend this adaptive scheme for multiple-cell network. We develop another variant of this adaptive scheme which we call fractional adaptive scheme. Both the adaptive and fractional adaptive schemes are found to outperform the guard channel scheme in controlling the handoff failure probability in a cellular wireless network.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".