Enhancing TCP performance in wireless mesh networks by cross layer design
Bibliographic record
Abstract
Wireless mesh network (WMN) is an emerging technology for last-mile broadband Internet access. Despite extensive research on and even commercial implementations of WMNs, there are still some serious performance issues in the transport layer, where the performance of Transmission Control Protocol (TCP) degrades dramatically as the number of hops increases. Improving TCP performance over WMNs is a research area that has attracted a lot of attention in recent years and the focus of this paper. We take a cross-layer design approach to improve the performance of TCP for nodes farther from the Internet gateways by giving a higher priority to the packets that have traversed a larger number of hops over the WMN. The proposal changes the way that routing and scheduling algorithms work together and can be easily implemented in IEEE 802.16d WMNs. Extensive simulation results show that the proposed method successfully improve the TCP throughput by as much as three times over a small number of hops, whereas TCP generally performs poorly when the number of hops is large.
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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 | 0.004 | 0.005 |
| 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 | 0.002 |
| 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".