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Record W2899573884 · doi:10.1109/tcsi.2018.2877940

Statistics-Based Approach for Blind Post-Compensation of Modulator’s Imperfections and Power Amplifier Nonlinearity

2018· article· en· W2899573884 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Circuits and Systems I Regular Papers · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta Innovates - Technology Futures
KeywordsAmplifierProbability density functionStatisticsNonlinear systemTransmitterAdjacent channel power ratioHigher-order statisticsMathematicsControl theory (sociology)Hellinger distanceMean squared errorTelecommunicationsPhysicsChannel (broadcasting)RF power amplifierBandwidth (computing)Computer scienceSignal processing

Abstract

fetched live from OpenAlex

Power amplifier (PA) nonlinearity and in-phase and quadrature-phase (I/Q) imbalance are major concerns for wireless transmitters. In this paper, we present a new closed-form expression for the probability density function (PDF) of I and Q components in the presence of transmitter's impairments and propose a blind post-compensation approach for the mitigation of these impairments. These impairments include static PA nonlinearity and frequency-independent I/Q imbalance. The accuracy of the analytical PDF is evaluated using Kullback-Leibler divergence and Hellinger square distance. Simulation results show a reasonable correspondence between the derived PDF and non-parametric kernel density estimation-based PDF. After a closed-form PDF is obtained, higher order statistics-based method is used to estimate PA nonlinearity in the presence of I/Q impairments. Finally, a maximum-likelihood estimation of I/Q imbalance parameters is obtained using the analytical PDF. Simulation results show a normalized mean-squared error (NMSE) of around -40 dB and an adjacent channel power ratio of around -53 dBc, along with an error vector magnitude (EVM) of around 1%, for a 3-MHz local thermal equilibrium signal. Using laboratory measurements, an NMSE of around -35 dB and an EVM of 1.5% are achieved.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.865

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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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