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Record W2150175166 · doi:10.1109/bsc.2006.1644602

Performance Modeling of Ad Hoc Networks with Multi-Cell Interference

2006· article· en· W2150175166 on OpenAlexaff
X. Wang, Mustafa Mehmet Ali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceWireless ad hoc networkThroughputComputer networkInterference (communication)Multiple Access with Collision Avoidance for WirelessVehicular ad hoc networkAd hoc wireless distribution serviceMobile ad hoc networkProtocol (science)Channel (broadcasting)Optimized Link State Routing ProtocolDistributed computingWirelessNetwork packetTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we study the performance modeling of a MAC protocol for ad hoc networks. Dual busy tone multiple access (DBTMA) is one of the MAC protocols designed for ad hoc networks. The existing performance model of DBTMA fails to take into consideration multi-cell interference, known as hidden terminal problem. We present a performance analysis that takes into account the multi-cell interference. The numerical results show lower channel throughput than that predicted by the existing performance model of DBTMA protocol. The numerical results are verified through simulation. Further, we extend our analytical model to power-controlled networks

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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
Published2006
Admission routes1
Has abstractyes

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