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Record W2130672304 · doi:10.1109/twc.2007.05804

Spectral Characteristics of M-ary Code Shift Keying Based Impulse Radios: Effects of Code Design

2007· article· en· W2130672304 on OpenAlexaff
Serhat Erküçük, Dong Sik Kim

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTime-hoppingPulse-position modulationComputer scienceCode ratePhase-shift keyingPulse-amplitude modulationConstant-weight codeBasebandCode (set theory)KeyingSpectral densityLow-density parity-check codeElectronic engineeringDecoding methodsTelecommunicationsUltra-widebandAlgorithmBit error rateConcatenated error correction codeSet (abstract data type)Bandwidth (computing)Block codePulse (music)Engineering

Abstract

fetched live from OpenAlex

This paper analyzes the power spectral density (PSD) characteristics of ultra wideband (UWB) signals modulated by M-ary code shift keying (MCSK) that can be combined with binary pulse position modulation (BPPM) and binary pulse amplitude modulation (BPAM), which we refer to as MCSK based impulse radios (IR). MCSK based IR are modified IR that were designed to increase the data rate of conventional IR by embedding the data on a time hopping (TH) code randomly selected from a set of M distinct TH codes per user. This random selection also results in increased effective TH code period, which- helps smoothing the continuous spectrum and suppressing the discrete spectral components. In combination with the random code selection, design of the TH code set for each user is very important for spectrum shaping and multiple access (MA) capability, and is addressed in detail in the paper.

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)0.003
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.245
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 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

Citations7
Published2007
Admission routes1
Has abstractyes

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