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Record W2574457189 · doi:10.1049/iet-com.2016.0902

Augmented Hammerstein model for six‐port‐based wireless receiver calibration

2017· article· en· W2574457189 on OpenAlexaff
Nadia Chagtmi, Noureddine Boulejfen, Fadhel M. Ghannouchi

Bibliographic record

VenueIET Communications · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPort (circuit theory)Computer scienceCalibrationWirelessComputer networkTelecommunicationsMathematicsStatisticsElectronic engineering

Abstract

fetched live from OpenAlex

An augmented Hammerstein model (AHM) is proposed for the first time to calibrate a six‐port‐based receiver (SPR) and to recover the in‐phase I and the quadrature Q components of a modulated wideband RF signal. The proposed model is intended to provide a good compromise between complexity and accuracy. To verify the performance of the resulting calibration approach, an experimental validation has been performed with an SPR driven by wideband modulated signals with different bandwidths. An error vector magnitude between the transmitted and received signals of 2.17 and 1.33% has been reported for 64‐QAM signals with 4 and 2 MHz bandwidths, respectively. The obtained measurement results have revealed the robustness and the superiority of the AHM‐based single‐step calibration technique compared with the two‐step conventional procedure. Moreover, the AHM achieved the same performance as the published memory polynomial model while requiring ∼60% less coefficients leading to a less complexity calibration procedure with a lower number of calibration parameters and flops.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.468

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 science0.0010.000
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 head0.096
GPT teacher head0.302
Teacher spread0.207 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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