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Record W3124305129 · doi:10.1049/ell2.12018

Impact of bandwidth on antenna‐array noise matching

2021· article· en· W3124305129 on OpenAlexafffund
Roshaan Ali, Leonid Belostotski, Geoffrey G. Messier, Adrian Sutinjo

Bibliographic record

VenueElectronics Letters · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsWidebandDecorrelationAntenna noise temperatureBandwidth (computing)Computer scienceNoise figureAcousticsNoise (video)Low-noise amplifierAntenna arrayElectronic engineeringAmplifierPhysicsAntenna (radio)TelecommunicationsEngineeringAntenna measurementAntenna factor

Abstract

fetched live from OpenAlex

Abstract This letter expands the treatments of wideband noise analysis of antenna arrays by including bandwidth effects on beam‐equivalent receiver noise temperature, , and the active reflection coefficient, . The particular focus of the letter is on receiver noise decorrelation in wideband systems having noise bandwidth 1 Hz. The new analysis and simulations show increase in and the departure of from that obtained using contemporary analyses for 1 Hz. Although the paper also shows that for many applications over moderate bandwidths and close connection between the receiver and array the influence of on is not significant, the simulations of a 71‐element array demonstrate that the noise decorrelation due to wide can result in tens of percent (as much as 45.5% in simulations described in this letter) increase in above the low‐noise amplifier minimum noise temperature, which should be taken into account at the design stage of ultra‐wide band systems, such as those under investigation by, for example, the Defense Advanced Research Project Agency (DARPA) in its wideband adaptive RF protection (WARP) program and ultra‐sensitive active electronically scanned array (AESA) radars for tracking stealth objects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2021
Admission routes2
Has abstractyes

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