Enhancing fairness and throughput of tcp in heterogeneous wireless networks
Bibliographic record
Abstract
TCP exhibits inherent unfairness towards connections with Iong round-trip times and connections that have to traverse multiple congested routers. We have previously proposed a TCP bandwidth allocation (TBA) algorithm to solve this problem and proved its effectiveness in wireline networks. In this paper, we apply the IBA algorithm to improve TCP fairness over heterogeneous wireless networks with combined wireless and wireline links. In such networks, TCP suffers significant throughput degradations due to its window being frequently shut down, not in response to congestion, but by packet losses due to transmission errors over wireless links. We propose to apply wireless explicit congestion notification (WECN), a version of ECN enhanced for wireless networks, to decouple congestion control and loss recovery. Further enhancement is also incorporated to smooth traffic bursts. Simulation results show that not only can the combined TBA/WECN mechanism improve TCP fairness, but it can maintain good throughput performance in the presence of wireless losses as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".