Improving end-to-end performance of TCP using link-layer retransmissions over mobile internetworks
Bibliographic record
Abstract
TCP does not perform well in networks with high packet error rates, such as those with wireless links, since TCP assumes network congestion to be the major cause for packet losses. Wireless losses make TCP unnecessarily initiate its congestion control mechanism which results in poor performance in the form of low throughput and high interactive delay. We investigate, through computer simulations, the end-to-end effects of link-layer retransmissions on TCP Reno over a low-data-rate wireless link. Our results show that, by using the more effective selective-reject ARQ at the link layer, the problem of competitive retransmissions between TCP and link layer is much less serious than previously reported. We show that a non-sequencing link layer in combination with fragmentations of datagrams at the base stations and mobile hosts can be employed without significantly degrading TCP performance. We propose link-layer modifications for best-effort retransmissions to reduce possible adverse effect of link-layer resets on TCP.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".