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Record W2132129449 · doi:10.1109/vetecf.2002.1040361

Performance of frequency-hopping multicarrier CDMA in Rayleigh fading

2003· article· en· W2132129449 on OpenAlexafffund
Maged Elkashlan, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsFrequency-hopping spread spectrumCode division multiple accessRayleigh fadingElectronic engineeringTelecommunications linkNarrowbandComputer scienceFadingWidebandSpread spectrumBit error rateDiversity schemeOrthogonal frequency-division multiplexingTelecommunicationsEngineeringDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

A new transmission scheme, frequency hopping multicarrier code division multiple access (FH-MC-CDMA), is proposed and investigated. This scheme can be compatible with existing narrowband second-generation frequency hopping and third-generation wideband CDMA systems. Two receiver combining techniques, namely equal-gain-combining and maximum-ratio-combining, are considered. An analysis of the downlink bit error rate in slow frequency-selective Rayleigh fading is provided. Two frequency-hopping (FH) schemes, namely random and coordinated FH are used. The performance improvement of the FH-MC-CDMA system over conventional frequency hopping is demonstrated, with a capacity gain approaching that of MC-CDMA but using a much lower number of subcarriers.

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)0.005
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0010.000

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 head0.030
GPT teacher head0.279
Teacher spread0.248 · 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

Citations7
Published2003
Admission routes2
Has abstractyes

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