Performance analysis of hierarchical cellular networks with queueing and user retrials
Bibliographic record
Abstract
Abstract How to efficiently utilize the scarce radio channel resource while maintaining the desired user‐perceived quality level and improved network performance is a major challenge to a wireless network designer. As one solution to meet this challenge in cellular mobile networks, a network architecture with hierarchical layers of cells has been widely considered. In this paper, we study the performance of a hierarchical cellular network that allows the queueing of both overflow slow‐mobility calls (from the lower layer microcells) and macrocell handover fast‐mobility calls that are blocked due to lack of free resources at the macrocell. Further, to accurately represent the wireless user behaviour, the impact of call repeat phenomenon is considered in the analysis of new call blocking probability. Performance analysis of the hierarchical cellular structure with queueing and call repeat phenomenon is performed using both analytical and simulation techniques. Numerical results show that queueing of calls reduces forced call termination probability and increases resource utilization with minimal call queueing delay. It is also shown that ignoring repeat calls leads to optimistic estimates of new call blocking probability especially at high offered traffic. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".