Queue-aware uplink bandwidth allocation and rate control for polling service in IEEE 802.16 broadband wireless networks
Bibliographic record
Abstract
IEEE 802.16 standard defines the air interface specifications for broadband access in wireless metropolitan area networks. Although the medium access control signaling has been well-defined in the IEEE 802.16 specifications, resource management and scheduling, which are crucial components to guarantee quality of service performances, still remain as open issues. In this paper, we propose adaptive queue-aware uplink bandwidth allocation and rate control mechanisms in a subscriber station for polling service in IEEE 802.16 broadband wireless networks. While the bandwidth allocation mechanism adaptively allocates bandwidth for polling service in the presence of higher priority unsolicited grant service, the rate control mechanism dynamically limits the transmission rate for the connections under polling service. Both of these schemes exploit the queue status information to guarantee the desired quality of service (QoS) performance for polling service. We present a queuing analytical framework to analyze the proposed resource management model from which various performance measures for polling service in both steady and transient states can be obtained. We also analyze the performance of best-effort service in the presence of unsolicited grant service and polling service. The proposed analytical model would be useful for performance evaluation and engineering of radio resource management alternatives in a subscriber station so that the desired quality of service performances for polling service can be achieved. Analytical results are validated by simulations and typical numerical results are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".