Decoupling congestion control from TCP (semi-TCP) for multi-hop wireless networks
Bibliographic record
Abstract
Abstract Although many problems for transmission control protocol (TCP) in multi-hop wireless networks have been studied with many proposals in the literature, they are not solved completely yet. Different from the existing proposals to mitigate the limitation of TCP in multi-hop wireless networks, we propose a framework of semi-TCP which decouples two functionalities of traditional TCP, i.e., congestion control and reliability control, in order to get rid of the constraint of TCP’s congestion window on performance enhancement. Specifically, we employ hop-by-hop congestion control which is more efficient than its end-to-end counterpart since the control efficiency of the later relies on the availability of end-to-end connectivity which is difficult to sustain in wireless networks. We implement hop-by-hop congestion control via intra-node and inter-node congestion control, and propose a distributed hop-by-hop congestion control algorithm based on the widely used request-to-send/clear-to-send protocol. Such a semi-TCP retains the reliability control in original TCP. Extensive simulations based on network simulator-2 show the promising performance of semi-TCP over traditional schemes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".