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Record W2124335021 · doi:10.1109/tvt.2004.841539

Performance of Predistorted APK Modulation for One- and Two-Link Nonlinear Power Amplifier Satellite Communication Channels

2005· article· en· W2124335021 on OpenAlexaff
Yulin Zhou, P.J. McLane, C. Loo

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsCommunications Research Centre CanadaQueen's University
Fundersnot available
KeywordsPredistortionPhase-shift keyingQuadrature amplitude modulationAmplifierCommunications satelliteElectronic engineeringLinear amplifierAmplitude and phase-shift keyingPulse-amplitude modulationModulation (music)Electrical engineeringPhysicsControl theory (sociology)Bit error rateEngineeringComputer scienceRF power amplifierChannel (broadcasting)SatellitePulse (music)AcousticsCMOSVoltage

Abstract

fetched live from OpenAlex

Digital satellite communications has been restricted to date to digital modulations having an efficiency of 2 bps/Hz; that is, filtered-quadrature phase-shift keying (QPSK) with close to a constant envelope. The main limitation to higher order digital modulations is the need for linearity of a high power amplifier both in the satellite and a terminal. Many years ago, Thomas et al. conducted a complete study of the use of higher order linear modulations on nonlinear satellite channels. However, pulse-shaping and predistortion were not treated. We consider these items for 16-point amplitude phase-shift keying (APK) modulation; that is, signal points assigned on concentric circles. A simple predistortion algorithm is given for APK modulations and symbol error-rate performance is determined for both one- and two-link nonlinear channels. Our best result is for an one-link channel and (5, 11) APK-5 points on an inner circle and 11 on the other, which, for predistortion on the nonlinear channel, loses only about 1 dB to 16-point quadrature amplitude modulation transmission on the linear channel.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.611
Threshold uncertainty score0.843

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 head0.013
GPT teacher head0.233
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations12
Published2005
Admission routes1
Has abstractyes

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