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Record W1968062632 · doi:10.1109/icecs.2005.4633486

A unified framework for the construction of OFDM/OQAM systems

2005· article· en· W1968062632 on OpenAlexaff
Mohamed Siala, Tolga Kurt, Abbas Yongaçoğlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPAPR reduction in OFDM
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOrthonormalityOrthogonal frequency-division multiplexingWaveformQuadrature amplitude modulationComputer scienceQAMElectronic engineeringAlgorithmWirelessChannel (broadcasting)Orthonormal basisTelecommunicationsBit error rateEngineeringRadarPhysics

Abstract

fetched live from OpenAlex

OFDM/OQAM is a promising radio access technique for high data-rate fourth generation cellular systems, operating on time and frequency dispersive wireless channel. Contrary to the well established OFDM/QAM, it allows very well localized prototype waveforms to be used for critical unit-density time-frequency lattices, offering maximal spectrum efficiency. Starting from the set of orthonormality conditions verified by the frequency-time lattice shifted versions of the prototype waveform, we derive the general orthonormality criterion for OFDM/OQAM. We show that even and odd parity prototype waveforms are basically the unique waveforms for which the general orthonormality criterion simplifies to that of OFDM/QAM with frac12 time-frequency lattice density. Based on the simplified criterion, we propose and characterize a new orthonormalization procedure for the efficient design of very well localized prototype functions, starting from non-orthogonal mother waveforms. Finally, we show that odd parity orthonormalized mother functions lead inevitably to badly localized prototype waveforms and that only even parity prototype waveforms should be used in practice.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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
Published2005
Admission routes1
Has abstractyes

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