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Record W286664451

A subspace method for the estimation of gain/phase mismatches and I/Q imbalances in a receive array antenna

2007· article· en· W286664451 on OpenAlexaff
Zhiwen Zhu, Xinping Huang

Bibliographic record

VenueCommunications, Internet, and Information Technology · 2007
Typearticle
Languageen
FieldEngineering
TopicRadio Frequency Integrated Circuit Design
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsSubspace topologyAntenna (radio)Signal subspaceControl theory (sociology)Constraint (computer-aided design)Compensation (psychology)Phase (matter)Antenna gainAntenna arrayElectronic engineeringComputer scienceMathematicsAlgorithmTelecommunicationsAntenna measurementEngineeringPhysicsAntenna factorMathematical analysis
DOInot available

Abstract

fetched live from OpenAlex

This paper proposes a subspace method to estimate impairment compensation coef cientsin a receive array antenna. These impairments due to imperfections in the analog front end circuits are the gain/phase mismatches among the different antenna elements and imbalances between in-phase and quadrature (I/Q) components. A signal model in the presence of the gain/phase mismatches and I/Q imbalances is developed first. Then a signal subspace constraint is formulated such that a simple method can be used to estimate the mismatches and imbalances. It is shown that the method results in an unambiguous estimate. Its performance is also evaluated using numerical simulations.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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
Published2007
Admission routes1
Has abstractyes

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