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Record W2089257167 · doi:10.1109/tcsi.2004.834510

New Peak-to-Average Power-Ratio Reduction Algorithms for Multicarrier Communications

2004· article· en· W2089257167 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 Transactions on Circuits and Systems I Fundamental Theory and Applications · 2004
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsReduction (mathematics)AlgorithmBasebandModulation (music)PassbandOrthogonal frequency-division multiplexingComputer sciencePower (physics)MathematicsElectronic engineeringBandwidth (computing)TelecommunicationsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

New peak-to-average power-ratio (PAPR) reduction algorithms for multicarrier systems are developed by modifying the modulation constellation in active subcarriers and the modulation symbols in unused subcarriers. The proposed algorithms yield optimal PAPR-reduction solutions. For real-baseband multicarrier systems, the proposed PAPR-reduction algorithm is developed using a fast linear programming approach and considerable performance improvement can be achieved relative to that achieved with several existing algorithms. For passband multicarrier systems, a new PAPR-reduction algorithm is constructed whereby the associated minimax optimization problem is solved using an accelerated least-p th algorithm. Simulation results are presented which demonstrate that the proposed algorithm outperforms an algorithm due to Jones and that improved PAPR reduction can be achieved when the proposed algorithm is combined with another algorithm known as selective mapping scheme.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.899

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 studies0.0010.000
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 head0.026
GPT teacher head0.272
Teacher spread0.246 · 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