Virtual Partitioning Resource Allocation for Multiclass Traffic in Cellular Systems With QoS Constraints
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Bibliographic record
Abstract
Resource allocation is a vital component of call-admission control that determines the amount of resource to assign to new and handoff connections for quality-of-service (QoS) satisfaction. In this paper, we present approximate analytical formulations of virtual partitioning resource-allocation schemes for handling multiclass services with guard channels in a cellular system. Resource-allocation models for best effort and guarantee access with preemption for best effort traffic and virtual partition with preemption for all classes are investigated. The analytical models, derived using a K-dimensional Markov chain, are solved using preemption rules for these schemes. Call-level grade of service, such as new-call-blocking probability, handoff-call-blocking probability, and system utilization, and packet-level QoS, such as packet-loss probability, are used as performance metrics. The performances of fast and slow mobile users are evaluated analytically and by simulation. The analytical and simulation results show excellent agreement. A method to maximize system utilization through joint optimization of call-/packet-level parameters is proposed. Numerical results indicate that significant gain in system utilization is achieved.
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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 | 0.001 |
| 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