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Record W2155233959 · doi:10.1109/glocom.2009.5425983

Near-Complementary Sequences of Various Lengths and Low PMEPR for Multicarrier Communications

2009· article· en· W2155233959 on OpenAlexaff
Nam Yul Yu, Guang Gong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of WaterlooLakehead University
Fundersnot available
KeywordsComplementary sequencesBinary Golay codeSequence (biology)Envelope (radar)MathematicsBoolean functionPower (physics)Power of twoEncoding (memory)AlgorithmTopology (electrical circuits)Computer scienceDiscrete mathematicsCombinatoricsTelecommunicationsBiologyArtificial intelligenceGeneticsPhysics

Abstract

fetched live from OpenAlex

New families of near-complementary sequences are presented for peak power control in multicarrier communications. A framework for near-complementary sequences is given by the explicit Boolean expression and the equivalent array structure. The framework transforms the seed pairs to the near-complementary sequences by the aid of Golay complementary sequences. As the examples, new families of near-complementary sequences with the peak-to-mean envelope power ratio (PMEPR) < 4 are presented, where the sequences provide various lengths by employing the seeds of shortened or extended Golay complementary pairs. The sequence families can find the potential applications for peak power control requiring codewords or sequences of various lengths as well as low PMEPR.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.286
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

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
Published2009
Admission routes1
Has abstractyes

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