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Record W2593180051

Smart antenna simulation environment in ADS

2002· article· en· W2593180051 on OpenAlexaff
Yin Fung Tang, Langis Roy

Bibliographic record

VenueInternational Symposium on Antenna Technology and Applied Electromagnetics · 2002
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsAntenna (radio)Computer scienceSmart antennaBase stationElectronic engineeringBase (topology)SimulationReconfigurable antennaEmbedded systemEngineeringAntenna measurementDipole antennaTelecommunicationsAntenna efficiency
DOInot available

Abstract

fetched live from OpenAlex

An end-to-end smart antenna simulation environment has been created in the Advanced Design System (ADS®) from Agilent Technologies. Because ADS® is able to co-simulate with various other design tools, system implementation becomes much faster. Although a full-blown RF level simulation is possible, the run-time of the simulation is extremely slow. To improve the speed of the simulation, a base-band version of the antenna array model as well as the beamformer has been created and simulated in ADS®. Because of the faster run-time, the base-band simulation could be used for testing and analyzing the performance of the adaptive algorithm.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.053
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0530.017

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 head0.006
GPT teacher head0.185
Teacher spread0.179 · 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
GenreMethods

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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