Performance of packet voice transmission using IEEE 802.16 protocol
Bibliographic record
Abstract
The IEEE 802.16 standard defines three types of scheduling services for supporting real-time traffic, unsolicited grant service (UGS), real-time polling service (rtPS), and extended real-time polling service (ertPS). In the UGS service, the base station (BS) offers a fixed amount of bandwidth to a subscriber station (SS) periodically, and the SS does not have to make any explicit bandwidth requests. The bandwidth allocation in the rtPS service is updated periodically in the way that the BS periodically polls the SS, which makes a bandwidth request at the specified uplink time slots and receives a bandwidth grant in the following downlink subframe. In the ertPS service, the BS keeps offering the same amount of bandwidth to the SS unless explicitly requested by the SS. The SS makes a bandwidth request only if its required transmission rate changes. In this article we study the performance of voice packet transmissions and BS resource utilization using the three types of scheduling services in IEEE 802.16-based backhaul networks, where each SS forwards packets for a number of voice connections. Our results demonstrate that while the UGS service achieves the best latency performance, the rtPS service can more efficiently utilize the BS resource and flexibly trade-off between packet transmission performance and BS resource allocation efficiency; and appropriately choosing the MAC frame size is important in both the rtPS and ertPS services to reduce packet transmission delay and loss rate
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".