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Record W2160975276 · doi:10.1109/glocom.2008.ecp.930

Orthogonal Wavelet Based Dynamic Pulse Shaping for Cognitive Ultra-Wideband Communications

2008· article· en· W2160975276 on OpenAlexaff
Xuanli Wu, Xuejun Sha, Cheng Li, Naitong Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWaveletComputer sciencePulse shapingWidebandUltra-widebandElectronic engineeringCognitive radioSpectral efficiencyWavelet transformPower (physics)Orthogonal waveletDiscrete wavelet transformTelecommunicationsChannel (broadcasting)EngineeringWirelessArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

In order to achieve efficient dynamic spectrum access (DSA) in Cognitive Ultra-Wideband (C-UWB) systems, an orthogonal wavelets based dynamic pulse shaping method is proposed to obtain pulses which can adapt to any given spectral requirements. Using Meyer wavelet set as the orthogonal basis, pulses with high power efficiency can be achieved. The compact support property of Meyer wavelets enables us to efficiently reduce the computational complexity in dynamic pulse shaping calculation. Moreover, we demonstrate that the proposed method is easy to implement and can achieve a good balance between power efficiency and system complexity.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.870
Threshold uncertainty score0.857

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 science0.0010.000
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 head0.040
GPT teacher head0.268
Teacher spread0.228 · 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 teacher head, 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

Citations3
Published2008
Admission routes1
Has abstractyes

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