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Record W2150603025 · doi:10.1109/vetecf.2000.886670

Effects of adaptive equalization on the performance of broadband wireless communications

2002· article· en· W2150603025 on OpenAlexaff
Assia Semmar, Huu Tu Huynh, M. Lecours

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsWidebandComputer scienceEqualization (audio)WirelessPower delay profileContext (archaeology)Transmission (telecommunications)Electronic engineeringChannel (broadcasting)Monte Carlo methodFadingBroadband networksBroadbandDelay spreadTelecommunicationsEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

The performance of wireless wideband high data rate indoor communications systems is affected by the channel propagation conditions. In this context, the channel delay profile characteristics constitute a significant constraint by imposing an irreducible error rate which results in a limitation of the transmission rate. We examine the effects of equalization techniques on the performance of broadband wireless communications with CQPSK modulation. Although general in nature, this study is carried out in the context of its application to high data rare wideband transmission at millimeter-wave frequencies. The performance results are based on Monte Carlo simulations with a simplified model of the generated received signal which depend on the power delay profile characteristics. Two power delay profile models are considered: a one sided exponential model and a uniform model. Simulations results show that the relative performance of linear and non-linear equalization techniques strongly depends on the characteristics of the power delay profile model.

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)0.006
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.025
GPT teacher head0.239
Teacher spread0.214 · 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

Citations0
Published2002
Admission routes1
Has abstractyes

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