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Record W4405303896 · doi:10.1109/mmm.2024.3474388

Recent Advances in Linearization Techniques for Frequency Multiplier-Based Transmitters Driven With Vector-Modulated Signals

2024· article· en· W4405303896 on OpenAlexaff
Ahmed Ben Ayed, Patrick Mitran, Slim Boumaiza

Bibliographic record

VenueIEEE Microwave Magazine · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPredistortionLinearizationMultiplier (economics)Electronic engineeringTransmitterFrequency multiplierComputer scienceControl theory (sociology)Bandwidth (computing)EngineeringTelecommunicationsPhysicsNonlinear systemAmplifierArtificial intelligence

Abstract

fetched live from OpenAlex

The shift toward millimeter-wave and sub-terahertz frequencies in communication systems presents significant challenges in transmitter design, particularly in generating wideband, high-data-rate signals with adequate power and linearity. Frequency multiplier (FX)-based architectures have emerged as promising solutions for signal generation at these frequencies, but the inherent nonlinearity of FXs poses challenges to maintaining signal integrity. This article reviews recent advancements in digital predistortion (DPD) techniques aimed at mitigating the nonlinear distortions in FX-based transmitters, particularly when driven by vector-modulated signals. By providing a comparative analysis of state-of-the-art DPD approaches and highlighting key innovations, this work offers critical insights into the strengths and limitations of current methods and outlines future research directions for enhancing the linearization of FX-based transmitters in next-generation communication systems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.867
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
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.246
Teacher spread0.234 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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