Wireless Local Area Networks Quality of Service: An Engineering Perspective
Bibliographic record
Abstract
This book is part of the IEEE Standards Wireless Networks Series. This book describes Wireless Local Area Networks Quality of Service enhancements. Those enhancements enable the support of real-time applications such as voice and video. The scope of the book includes: - Overview of QoS mechanisms and architectures. - WLAN QoS mechanisms and relationship to QoS general architectures. - Performance analysis and case studies. WLAN QoS planning and engineering are challenging tasks to both network operators and end users. This book is an attempt to satisfy the need for better understanding of WLAN QoS features as well as the related engineering challenges. The reader will be able to understand WLAN QoS mechanisms and features in a simple and clear way beyond that obtained by just reading standard documents. The reader of the book should be able to understand: - WLAN QoS basic operation. How WLAN QoS is related to other QoS architectures especially IP QoS, and the related interworking issues. - How to tackle WLAN QoS main issues including admission control, scheduling, and buffer management. How to engineer a WLAN-based network to support QoS-based applications
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".