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Record W2146147410 · doi:10.1109/icnp.1997.643691

Carrier-sense protocols for packet-switched smart antenna basestations

2002· article· en· W2146147410 on OpenAlexaff
Charbel Sakr, T.D. Todd

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceComputer networkSpace-division multiple accessNetwork packetThroughputSmart antennaAntenna (radio)Protocol (science)Directional antennaWirelessTelecommunications linkTelecommunications

Abstract

fetched live from OpenAlex

Researchers have recently considered the use of smart antennas in various packet-switched data networks. Previously, a single-beam system was described which employs a smart antenna basestation, operating in carrier-sense (CSMA) mode. Performance improvements are obtained by having the antenna dynamically point pattern nulls in the direction of interfering stations, thus reducing the frequency of channel collisions. In this paper, we consider the reverse-link performance of stations accessing a smart antenna basestation using multibeam SDMA. A basic CSMA/SDMA protocol is first proposed for this type of system. Following this, we also present a CSMA/SDMA protocol which incorporates basestation/portable signalling which mitigates the effects of hidden stations. The performance of these systems is characterized and compared using analytical throughput and capacity models. It is shown that when hidden stations are present, the capacity performance of the more sophisticated protocol may be much higher than that of the basic version.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.103
GPT teacher head0.344
Teacher spread0.241 · 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

Citations27
Published2002
Admission routes1
Has abstractyes

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