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Record W2165275312 · doi:10.1109/tcsii.2006.882182

A QRD-RLS-Based Predistortion Scheme for High-Power Amplifier Linearization

2006· article· en· W2165275312 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.

Bibliographic record

VenueIEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing · 2006
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPredistortionLinearizationBasebandAmplifierQR decompositionRecursive least squares filterPolynomialPower (physics)Control theory (sociology)InverseMathematicsComputer scienceAlgorithmNonlinear systemTelecommunicationsAdaptive filterMathematical analysisArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

A digital baseband predistortion (PD) scheme for high-power amplifier (HPA) linearization is proposed and analyzed in this brief. The proposed approach utilizes the QR-decomposition-based recursive least squares (QRD-RLS) algorithm to estimate the memoryless complex polynomial coefficients that characterize the HPA. The inverse polynomial model coefficients corresponding to the PD are similarly extracted using QRD-RLS. The performance of the proposed PD scheme is analyzed via simulations and compared with previously published techniques. Results show that the QRD-RLS-based solution offers improved performance over its comparatives

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 score0.977
Threshold uncertainty score0.918

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 science0.0000.000
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 head0.013
GPT teacher head0.209
Teacher spread0.196 · 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