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Record W2143516379 · doi:10.1109/glocom.2006.504

WSN07-5: Performance of Directional MAC Protocols in Ad-Hoc Networks over Fading Channels

2006· article· en· W2143516379 on OpenAlexaff
Kun Liu, Walaa Hamouda, Amr Youssef

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceFadingComputer networkDirectional antennaThroughputAdditive white Gaussian noiseWireless ad hoc networkOmnidirectional antennaChannel (broadcasting)Transmission (telecommunications)Mobile ad hoc networkAntenna (radio)WirelessTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

The application of directional antennas in mobile ad-hoc networks (MANETs) has proved to offer large throughput gains relative to single-antenna systems. Recent works on directional medium-access-control (MAC) protocols have been only focused on their performance over additive white-Gaussian noise (AWGN) channels. In this paper, we evaluate the performance of directional MAC protocols when the channel is modeled as a slow-fading one. In this case, directional antennas are used for the transmission of data frames while control frames are sent using an omnidirectional antenna. Considering a slow-fading channel, our results show large throughput improvements when using directional MAC protocols relative to the IEEE 802.11 standard. Furthermore we show that the throughput loss on the fading channels relative to the AWGN channel, can be well compensated using antenna arrays. All our results show that the use of directional antennas at the mobile station can improve the channel efficiency in an ad-hoc network.

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: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.725

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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