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Record W2100802439 · doi:10.1109/wiopt.2006.1666503

Queue Management Strategies to Improve TCP Fairness in IEEE 802.11 Wireless LANs

2006· article· en· W2100802439 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer networkComputer scienceTCP accelerationTCP global synchronizationZeta-TCPFairness measureTCP Friendly Rate ControlTCP Westwood plusIEEE 802.11Service setIEEE 802.11e-2005Network packetWireless networkWi-FiThroughputWirelessTransmission Control ProtocolWi-Fi arrayTelecommunications

Abstract

fetched live from OpenAlex

Wireless Local Area Networks (WLANs) based on the IEEE 802.11 technology have become increasingly popular and ubiquitous. The 802.11 standard allows each station in a WLAN equal opportunity to access the wireless channel, which can result in unfair sharing of network bandwidth between upstream and downstream TCP flows at an AP. In this paper, we propose two different queue management techniques to alleviate the unfairness problem, with one based on Selective Packet Marking (SPM), and the other based on Least Attained Service (LAS) scheduling. We evaluate these proposed solutions using the ns-2 network simulator. The simulation results show that, compared to a conventional DropTail queue mechanism for NewReno TCP sources, the proposed solutions improve the fairness index by 20-40%, while achieving comparable aggregate throughput.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.242
Teacher spread0.235 · 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

Quick stats

Citations24
Published2006
Admission routes1
Has abstractyes

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