Path diversified retransmission for TCP over wireless mesh networks
Bibliographic record
Abstract
Path diversity exploits multiple routes simultaneously, achieving higher aggregated bandwidth and potentially decreasing delay and packet loss. Unfortunately, for TCP, naive load splitting often results in inaccurate estimation of round trip time (RTT) and packet reordering. As a result, it can suffer from significant instability or even throughput reduction. This is particular severe in Wireless Mesh Networks (WMNs), as validated by our analysis and simulation. To make multi-path TCP viable over WMNs, we propose a novel cross-layer design with a smart traffic split scheme, namely, Path Diversified Retransmission (PDR). PDR differentiates the original data packets and the retransmitted packets, and works with a novel QoS-aware multi-path routing protocol, QAOMDV, to distribute them separately. PDR does not suffer from the RTT underestimation and extra packet reordering, which ensures stable throughput improvement over single path routing. Through extensive simulations, we further demonstrate that, as compared to state-of-the-art multi-path protocols, our PDR with QAOMDV noticeably enhances the TCP throughput and reduces bandwidth fluctuation, with no obvious impact to fairness.
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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 | 0.001 | 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".