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Record W4229715808 · doi:10.1002/wcm.525

An efficient MAC protocol for cooperative diversity in mobile ad hoc networks

2007· article· en· W4229715808 on OpenAlexafffund
Md. Rajibul Islam, Walaa Hamouda

Bibliographic record

VenueWireless Communications and Mobile Computing · 2007
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsComputer scienceComputer networkCooperative diversityRelayThroughputFadingBlocking (statistics)Wireless ad hoc networkMobile ad hoc networkProtocol (science)Diversity gainChannel (broadcasting)WirelessTelecommunicationsNetwork packet

Abstract

fetched live from OpenAlex

Abstract Cooperative diversity is proposed to combat the detrimental effects of channel fading. In this paper, we investigate the effectiveness of cooperative diversity in interference limited ad hoc networks. The negative effects due to relay blocking on the network throughput are investigated. We show that the relay blocking problem is mainly dependent on the relay selection criterion. To overcome this problem, we propose a new cooperative diversity technique based on a modified IEEE 802.11 Medium Access Control (MAC) protocol. The throughput performance of the proposed MAC protocol is analyzed using a random structured network where nodes are assumed to be equipped with multiple antennas. In our simulations, we consider both single‐ and multiple‐relay scenarios over fading channels. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.004
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
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 science0.0020.001
Research integrity0.0010.001
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 head0.051
GPT teacher head0.352
Teacher spread0.301 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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