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Record W2035035823 · doi:10.1142/s0219799504000192

POWER AND BANDWIDTH-EFFICIENT CIRCULAR M-ARY APSK SCHEMES WITH LOW PAPR

2004· article· en· W2035035823 on OpenAlexaff
Thai Hoa Vo, Tho Le‐Ngoc

Bibliographic record

VenueInternational Journal on Wireless & Optical Communications · 2004
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsMcGill University
Fundersnot available
KeywordsAmplitude and phase-shift keyingPhase-shift keyingMathematicsKeyingQuadrature amplitude modulationBandwidth (computing)AmplifierAmplitude-shift keyingQAMTopology (electrical circuits)Electronic engineeringTelecommunicationsAlgorithmComputer scienceBit error rateEngineeringDecoding methodsCombinatorics

Abstract

fetched live from OpenAlex

This paper presents a class of circular M-ary Amplitude-Phase Shift Keying (APSK) signaling techniques with high bandwidth efficiency and low peak-to-average power ratio (PAPR) suitable for systems using nonlinear power amplifiers to achieve high power efficiency. For nonlinear amplification, good performance requires not only high squared minimum distance to average power ratio (DPR) but also low PAPR. A search algorithm for M-ary APSK schemes using circular constellations suitable for nonlinear amplification with minimum PAPR and maximum DPR is developed. Results for 8-, 16-, 32-, 64- and 128-APSK signalling schemes are presented. The effects of bandlimiting filter sharpness on the PAPR are examined and considered in the development. The analytical and simulation results show that this class of circular M-ary APSK signals outperforms the square and cross M-ary QAM schemes.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.256
Teacher spread0.247 · 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
Published2004
Admission routes1
Has abstractyes

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