Optimal multi-class guard channel admission policy under hard handoff constraints
Bibliographic record
Abstract
Summary form only given. In this paper we present a call admission control (CAC) policy for multimedia services that characterize the next generation of wireless cellular networks. The well-known CAC guard-channel policy is modified to maintain a pre-specified level of quality of services for multimedia calls. A semi-Markov decision process (SMDP) is used to represent the multi-class guard channel CAC policy with constraints on the dropping probabilities of multimedia handoff calls. The SMDP is formulated as a linear programming problem with the objectives of maximizing the system utilization and guaranteeing QoS of multiple classes of handoff calls with each class having particularly different QoS requirements. We show numerically that the multi-class guard channel policy deploying SMDP outperforms the existing upper-limit CAC policy as it improves the service quality by maximizing the bandwidth utilization while stratifying the quality of service constraint to upper bound of the handoff dropping probability.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".