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Record W2118536257 · doi:10.1109/vtcf.2006.348

An Adaptive-Scaling Algorithm for OFDM PAR Reduction Using Active Constellation Extension

2006· article· en· W2118536257 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.

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClipping (morphology)AlgorithmReduction (mathematics)Orthogonal frequency-division multiplexingConstellationComputer scienceConstellation diagramLossless compressionScalingMathematicsBit error rateData compressionTelecommunicationsChannel (broadcasting)Decoding methods

Abstract

fetched live from OpenAlex

The active constellation extension (ACE) technique is a lossless (in terms of throughput) peak-to-average power ratio (PAR) reduction technique that adaptively extends the signal constellation while ensuring that the minimum distance between any two constellation points does not decrease. However, this technique increases the average transmit power. In this paper, we propose an adaptive-scaling algorithm for the implementation of ACE. This algorithm, based on the clipping and filtering technique, uses only the peak samples of clipping noise to reduce PAR. Simulation results show that the proposed algorithm has better PAR reduction, lower complexity and smaller BER than the previously proposed Smart Gradient-Project ACE algorithm.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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