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Record W2148711981 · doi:10.1109/vetec.1993.508796

A postdistortion receiver for mobile communications

2002· article· en· W2148711981 on OpenAlexaff
L. Quach, S.P. Stapleton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTransmitterSpectral efficiencyBase stationAmplifierComputer scienceElectronic engineeringInterference (communication)Adjacent-channel interferenceMobile telephonyChannel (broadcasting)Transmitter power outputElectrical efficiencyPower (physics)Electrical engineeringMobile radioTelecommunicationsEngineeringPhysicsBandwidth (computing)

Abstract

fetched live from OpenAlex

A postdistortion receiver for possible future mobile communication systems that can increase both the spectral efficiency and the transmitter's power efficiency is presented. Postdistortion is a technique, implemented at the base-station receiver, to compensate for AM-AM and AM-PM nonlinearities of a mobile transmitter's amplifier, which, if uncompensated, would cause adjacent channel interference. A new adaptation method is demonstrated to compensate for slow variations in power amplifier characteristics without an interruption for a training period. The simulation and measured results show that the postdistortion technique can improve the out-of-band emission by up to 20 dB; the corresponding increase in mobile transmitter power efficiency is approximately a factor of 10. The spectral efficiency is increased by 20% with the postdistortion implementation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.007

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 head0.090
GPT teacher head0.324
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2002
Admission routes1
Has abstractyes

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