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Record W2780913761 · doi:10.1109/wincom.2017.8238189

Digital predistorter design for 110 W class AB UHF power amplifier for DVB-T transmitter

2017· article· en· W2780913761 on OpenAlexfundno aff
Haithem Rezgui, Fatma Rouissi, Rim Barrak, Adel Ghazel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsnot available
FundersUniversity of Calgary
KeywordsAdjacent channel power ratioUltra high frequencyTransmitterBasebandAmplifierDigital Video BroadcastingPredistortionDVB-TAdjacent channelElectronic engineeringTransmitter power outputElectrical engineeringComputer scienceChannel (broadcasting)EngineeringOrthogonal frequency-division multiplexing

Abstract

fetched live from OpenAlex

A Baseband Digital Predistorter design is proposed in this paper for a UHF 8 MHz Power Amplifier (PA) for DVB-T transmitter. A Memory Polynomial (MP) model is considered for the PA characterization and its coefficients are estimated based on Least Square Estimation (LSE) algorithm. The Indirect Learning Architecture (ILA) approach is used to estimate the coefficients of the predistorter. Simulation and FPGA implementation tests hase been carried out by using measured input/output samples of a 110 W Class AB UHF LDMOS PA. Test results showed an improvement of 21 dB in Adjacent Channel Power Ratio (ACPR) and the Normalized Mean Square Error (NMSE) reached -56 dB.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.896
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.030
GPT teacher head0.257
Teacher spread0.227 · 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.

Study designNot applicable
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

Citations2
Published2017
Admission routes1
Has abstractyes

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