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Record W2593711471

New adaptive polynomial and neural network predistortion techniques for satellite transmissions

2002· article· en· W2593711471 on OpenAlexaff
Rober Boutros, Mohamed Ibnkahla

Bibliographic record

VenueInternational Symposium on Antenna Technology and Applied Electromagnetics · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Power Amplifier Design
Canadian institutionsQueen's University
Fundersnot available
KeywordsQuadrature amplitude modulationPredistortionAmplifierConstellation diagramQAMElectronic engineeringCommunications satelliteSpectral efficiencyAdjacent-channel interferenceSIGNAL (programming language)Adjacent channelAdjacent channel power ratioModulation (music)Polyphase systemComputer scienceInterference (communication)Amplitude modulationControl theory (sociology)TelecommunicationsPhysicsEngineeringRadio frequencyChannel (broadcasting)Bit error rateBeamformingBandwidth (computing)Frequency modulationAcousticsSatellite
DOInot available

Abstract

fetched live from OpenAlex

In order to allow maximum exploitation of the frequency spectrum, the current trend is to increase the spectrum efficiency by using high modulation techniques e.g. multilevel quadrature amplitude modulation (QAM). The use of High Power Amplifiers (HPA), e.g., TWT, in satellite communications channels causes severe nonlinear distortions to the transmitted signal. The transmitted signal is more sensitive to these nonlinear distortions when the number of symbols in the signal constellation increases. The effect of the HPA can be sufficiently reduced by backing-off the output signal levels from the amplifier's saturation point. However, this reduces the transmitted signal power and consequently the power efficiency. Therefore, there is a tradeoff between power efficiency and spectral efficiency. Nonlinear distortions of the HPA give rise to some unwanted effects such as, warping of the signal constellation. Also, nonlinear characteristics of the TWT cause an increase of the Adjacent Channel Interference (ACI) due to widening of the transmitted signal spectrum.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.851

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 head0.008
GPT teacher head0.206
Teacher spread0.198 · 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
Published2002
Admission routes1
Has abstractyes

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