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Record W4242931390 · doi:10.1109/pimrc.2003.1264239

Enhancing fairness and throughput of tcp in heterogeneous wireless networks

2004· article· en· W4242931390 on OpenAlexaff
Fei Peng, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer networkComputer scienceTCP Westwood plusTCP Friendly Rate ControlTCP accelerationWireless networkNetwork congestionZeta-TCPTCP global synchronizationTCP tuningThroughputCUBIC TCPWirelessNetwork packetPacket lossTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.813
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.239
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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