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Record W2098196548 · doi:10.1109/vetecf.2007.75

Performance Evaluation of A Multiuser Detection Based MAC Design for Ad Hoc Networks

2007· article· en· W2098196548 on OpenAlexaff
Jinfang Zhang, Zbigniew Dziong, François Gagnon, Michel Kadoch

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2007
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceCode division multiple accessWireless ad hoc networkMultiuser detectionBandwidth (computing)Computer networkKey (lock)Interference (communication)Electronic engineeringWirelessTelecommunicationsEngineeringComputer securityChannel (broadcasting)

Abstract

fetched live from OpenAlex

In general, the performance and radio resource utilization of Ad Hoc networks are limited by half-duplex operation and possible collisions. In this paper, we propose a novel approach for MAC design that practically eliminates collisions and significantly increases the bandwidth utilization. The key element of this approach is the CDMA multiuser detection technology that allows receiving several signals in parallel without inflicting self-interference. These features give a promise of significant performance improvements. The main goal of this paper is to assess the range of this gain when compared to other existing alternatives. In particular, we compare the performance of the proposed multiuser detection based MAC design with MAC design based on IEEE 802.11 concept and with MAC design based on multi-code CDMA with one signal reception by one user at a time.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.073
GPT teacher head0.319
Teacher spread0.246 · 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

Citations9
Published2007
Admission routes1
Has abstractyes

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