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Record W2603224935 · doi:10.1109/pawr.2017.7875575

Joint RF pre-distortion and post-distortion linearization of small cell power amplifiers

2017· article· en· W2603224935 on OpenAlexaff
Yushi Hu, Slim Boumaiza

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLinearizationAmplifierAdjacent channelNonlinear distortionControl theory (sociology)Bandwidth (computing)Distortion (music)RF power amplifierElectronic engineeringPredistortionDynamic rangeLinearityComputer scienceMathematicsEngineeringPhysicsTelecommunicationsNonlinear system

Abstract

fetched live from OpenAlex

In this paper a power amplifier (PA) linearization technique is proposed where an RF predistorter (RFPD) is used in conjunction with a linearization amplifier (LA) to achieve a more effective joint pre-distortion and post-distortion linearization. This approach is suitable for use with small cells PAs because its power and cost overhead are scalable with the power range and cost of the PA. To demonstrate the technique's linearization capability, a prototype of the linearization scheme was implemented and applied to a 6-W class AB PA with a centre frequency of 850-MHz. When stimulated with a 15-MHz bandwidth modulated signal, the PA's adjacent channel leakage ratio and error vector magnitude were improved by more than 13 dB and 5 percentage points, respectively, at peak power, and up to 18 dB and 5.2 percentage points, respectively, at power backoff with the proposed linearization scheme. Furthermore, similar linearization was achieved when the PA was driven with a 40-MHz bandwidth modulated signal.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.625

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 head0.014
GPT teacher head0.217
Teacher spread0.202 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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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