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Record W2038761453 · doi:10.1109/rws.2010.5434267

Throughput performance of a busy-tone protocol in CCA modified, long range IEEE 802.11 networks

2010· article· en· W2038761453 on OpenAlexaff
Haiying Zhu, John Sydor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Networks and Protocols
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsComputer networkComputer scienceInter-Access Point ProtocolTelecommunications linkThroughputNetwork allocation vectorIEEE 802.11Backward compatibilityProtocol (science)Carrier sense multiple access with collision avoidanceIEEE 802Channel (broadcasting)Media access controlChannel access methodIEEE 802.15WirelessWireless networkWi-FiWireless sensor networkTelecommunicationsQuality of service

Abstract

fetched live from OpenAlex

Media access protocols define network performance in WLAN/WMAN systems. Such wireless networks are generally short range and use the CSMA (Carrier Sense Multiple Access) protocol which forms the basis to the ubiquitous IEEE 802.11 standard. In this paper we examine the behavior of a hybrid CSMA protocol called the ¿busy-tone protocol¿ which uses a dedicated physical channel to indicate media availability to a highly distributed, wide ranging (3-48 km) hidden set of mobile users. Using the busy tone to gate the radios, we force the radio link into a time domain duplex-like operation with the downlink channel consisting of scheduled concatenated bursts, effectively bypassing the CSMA, while the uplink channel still employs CSMA-like operation. The focus of the simulations is to understand performance of the proposed approach in long range, low S/N scenarios, and consider a hybrid IEEE 802.11 solution that would have improved performance compared to traditional applications of the standard.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.291
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2010
Admission routes1
Has abstractyes

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