Call admission control in mobile cellular networks: a comprehensive survey
Bibliographic record
Abstract
Abstract Call admission control (CAC) is a key element in the provision of guaranteed quality of service (QoS) in wireless networks. The design of CAC algorithms for mobile cellular networks is especially challenging given the limited and highly variable resources, and the mobility of users encountered in such networks. This article provides a survey of admission control schemes for cellular networks and the research in this area. Our goal is to provide a broad classification and thorough discussion of existing CAC schemes. We classify these schemes based on factors such as deterministic/stochastic guarantees, distributed/local control and adaptivity to traffic conditions. In addition to this, we present some modeling and analysis basics to help in better understanding the performance and efficiency of admission control schemes in cellular networks. We describe several admission control schemes and compare them in terms of performance and complexity. Handoff prioritization is the common characteristic of these schemes. We survey different approaches proposed for achieving handoff prioritization with a focus on reservation schemes. Moreover, optimal and near‐optimal reservation schemes are presented and discussed. Also, we overview other important schemes such as those designed for multi‐service networks and hierarchical systems as well as complete knowledge schemes and those using pricing for CAC. Finally, the paper concludes on the state of current research and points out some of the key issues that need to be addressed in the context of CAC for future cellular networks. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".