Efficient Rate Adaptation with QoS Support for Wireless Networks
Bibliographic record
Abstract
Wireless LANs (WLANs) suffer degrading performance when operating within domestic areas due to multiple reasons such as: multi-path, fading, path loss and user mobility. To overcome this, transmission rate is usually adjusted to a more error-resistant rate. In this paper, we first present a novel rate adaptation algorithm for IEEE 802.11 that can efficiently identify the threshold frame error rate (FER) at which link adjustment is required, based on a simple throughput analysis at the MAC layer. Then, we extend our rate adaptation algorithm to support IEEE 802.11e quality of service (QoS) requirements. When a real-time stream with QoS requirements is admitted, critical constraints such as delay bound and maximum packet drop count are integrated in the selection of the most convenient transmission rate that best respects the flow requirements. Moreover, we use dynamic bandwidth allocation rather than the default transmission opportunities (TXOPs) in a way that best offers a flow the required time for retransmissions due to packet failure based on the variant loss rate present in the channel. We validate our proposed rate adaptation algorithms via simulation results where the efficiency and effectiveness of the algorithm are noticed for both best effort and QoS flows.
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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) | 0.001 | 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 | 0.001 |
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".