Virtual Partitioning Resource Allocation for Multiclass Traffic in Cellular Systems With QoS Constraints
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".