Bandwidth reservation policy for multimedia wireless cellular networks and its analysis
Bibliographic record
Abstract
This paper examines quality of service (QoS) guarantees for mobile users in future wireless cellular networks supporting multiple classes of traffic with focus on reducing dropped handoff connections. We achieve this by proposing a threshold-based bandwidth reservation policy. The policy gives priority to handoff calls over new calls and prioritizes between different classes of handoff calls according to their QoS constraints by reserving a maximum occupancy, i.e., a threshold, to each call class. The policy can be modeled as a multidimensional Markov chain where each dimension is represented as M/M//spl infin/ queuing system, and therefore, a product form solution is provided. The QoS metrics - new call blocking probability, handoff call dropping probability, and probability of unsuccessful call completion - are derived. The analytical results are supported by simulation and show that the policy is able to reduce the connection-level QoS handoff call dropping probability for each class of traffic. Thus, it satisfies mobile user's needs and thus resulting in a stable performance levels during heavy load periods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".