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Record W1986954713 · doi:10.1155/2008/437801

Power Backoff Reduction Techniques for Generalized Multicarrier Waveforms

2007· article· en· W1986954713 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEURASIP Journal on Wireless Communications and Networking · 2007
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsComputer scienceOrthogonal frequency-division multiplexingReduction (mathematics)AmplifierTransmission (telecommunications)LinearizationModulation (music)Power (physics)Channel (broadcasting)Limit (mathematics)Interference (communication)WaveformElectronic engineeringNonlinear systemTelecommunicationsMathematicsBandwidth (computing)AcousticsPhysics

Abstract

fetched live from OpenAlex

Amplification of generalized multicarrier (GMC) signals by high-power amplifiers (HPAs) before transmission can result in undesirable out-of-band spectral components, necessitating power backoff, and low HPA efficiency. We evaluate variations of several peak-to-average power ratio (PAPR) reduction and HPA linearization techniques which were previously proposed for OFDM signals. Our main emphasis is on their applicability to the more general class of GMC signals, including serial modulation and DFT-precoded OFDM. Required power backoff is shown to depend on the type of signal transmitted, the specific HPA nonlinearity characteristic, and the spectrum mask which is imposed to limit adjacent channel interference. PAPR reduction and HPA linearization techniques are shown to be very effective when combined.

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.291
Teacher spread0.264 · 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