A CAC Considering Both Intracell and Intercell Handoffs for Measurement-based DCA
Bibliographic record
Abstract
Most call admission control (CAC) schemes proposed for dynamic channel allocation (DCA)-based systems simply follow the same philosophy as adopted by fixed channel allocation (FCA)-based systems, i.e., only considering intercell handoffs. Doing so is acceptable for FCA and coordinated DCA systems because an intracell handoff caused by a new channel assignment rarely happens in these cases. However, as discussed in this paper, in a distributed DCA with a measurement-based channel assignment, the intracell handoff may happen frequently and can dominate handoff events because failed intracell handoffs reduce the number of intercell handoffs. In this case, the CAC considering only the intercell handoff cannot work properly for such DCA systems. In this paper, we discuss a novel CAC to consider both intercell and intracell handoffs to guarantee a handoff call dropping probability (CDP) by jointly using the channel carrying approach. We also investigate a measurement-based implementation of this proposal through computer simulations. The results show that the proposed CAC can effectively bound the CDP to a target CDP.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".