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Record W4309196545 · doi:10.1002/mmce.23551

Twin nonlinearity generator‐based analog predistorter for small‐cell power amplifiers

2022· article· en· W4309196545 on OpenAlexaff
Gaoming Xu, Li Huang, Taijun Liu, Xiupu Zhang

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsAdjacent channel power ratioAmplifierQuadrature amplitude modulationWidebandPhase-shift keyingElectronic engineeringPredistortionIntermodulationQAMAdjacent channelBandwidth (computing)Electrical engineeringEngineeringTelecommunicationsBit error rateChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this article, a simple analog predistorter (APD), named twin nonlinearity generator based APD (TNG-APD) using the pseudomorphic high electron mobility transistor (pHEMT), is proposed for linearizing small-cell radio frequency power amplifiers (RFPAs) in the fifth-generation mobile communication system (5G). The TNG-APD is composed of two cascaded common-source field effect transistors (FETs) and a phase shifter. To verify the linearization, a TNG-APD circuit operating at central frequency of 3.5 GHz is fabricated to linearize wideband RFPAs. The RFPA under the test in this article is a 1-W RFPA for 5G small-cell basestations. The measurement results show that the adjacent channel power ratio (ACPR) of the RFPA is suppressed by more than 15 dB under a 100 MHz bandwidth 5G new radio (5G-NR) signal with simultaneous quadrature phase shift keying (QPSK) modulation and 256-quadrature amplitude modulation (256QAM), and more than 7 dB under a 200 MHz bandwidth 64 QAM signal. The measured error vector magnitude (EVM) under the 100 MHz 5G-NR signal is improved from 6.2% to 2.3%. Therefore, the proposed TNG-APD has high potential to linearize wideband RFPAs in the 5G small-cell basestation.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.708
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 communication0.0000.000
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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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