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Record W2903442432 · doi:10.23919/eumc.2018.8541722

Accurate Millimeter-wave Carrier Frequency Offset Measurement Using the Six-port Interferometric Technique

2018· article· en· W2903442432 on OpenAlexaff
Mansoor Dashti Ardakani, C. Hannachi, B. Zouggari, E. Moldovan, Serioja Ovidiu Tatu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsInterferometryOffset (computer science)AmplitudeLocal oscillatorExtremely high frequencyCarrier frequency offsetFrequency offsetMillimeterCalibrationDoppler effectPhysicsOpticsRadio frequencyFrequency bandFrequency synthesizerPhase noiseBandwidth (computing)Computer scienceOrthogonal frequency-division multiplexingTelecommunicationsChannel (broadcasting)Phase-locked loop

Abstract

fetched live from OpenAlex

A V-band front-end based on a six-port interferometer is proposed for the accurate measurement of Carrier Frequency Offset (CFO) and carrier recovery in a wireless system. The interferometer uses power readings for amplitude, phase or frequency comparison between its two RF inputs. If the circuit is carefully designed, this low-cost technique allows precise phase and frequency measurements without the need of any calibration. Furthermore, the ability to work with a reduced local oscillator (LO) power is a significant advantage of interferometry versus conventional mixing techniques. Measurements performed at 64 GHz show a resolution of a few Hz, as low as permitted by today's DC coupled measurement equipment. A CFO of 3 Hz, equivalent with a Doppler shift corresponding to a velocity of 7 mm/s, has been successfully measured.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.083
GPT teacher head0.260
Teacher spread0.177 · 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 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

Citations16
Published2018
Admission routes1
Has abstractyes

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