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Record W2123239283 · doi:10.1109/wcnc.2007.520

A Pulse Shape Design Method for Ultra-Wideband Communications

2007· article· en· W2123239283 on OpenAlexaff
Weihua Gao, R. Venkatesan, Cheng Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceUltra-widebandWidebandElectronic engineeringBandwidth (computing)Pulse shapingBit error rateSpectral densityPulse (music)GaussianInterference (communication)AlgorithmPower (physics)TelecommunicationsPhysicsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

A new design method to generate pulses for ultra-wideband (UWB) communication systems is presented. The systematic searching method is based on the linear combination of a number of Gaussian derivative pulses to form one single pulse whose power spectral density (PSD) not only conforms to the FCC spectral mask, but also effectively exploits the allowable bandwidth and power. In addition, the effect of pulse shaping on the mitigation of multiuser interference (MUI) is also taken into consideration in the proposed pulse shape design method. In particular, the authors develop a parameter called normalized mean-squared partial pulse correlation, which only has relation with the pulse shape and can directly affect the signal-to-noise ratio (SNR) of the receiver in a multi-user environment. By choosing a pulse which minimizes this parameter, the authors obtain the desired pulse shape. Compared with the widely used single Gaussian derivative pulses, the pulses they designed achieve better bit error rate performance in both single link and multi-user TH-PPM and TH-BPSK UWB communication systems.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.041
GPT teacher head0.309
Teacher spread0.268 · 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

Citations17
Published2007
Admission routes1
Has abstractyes

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