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Record W2163473895 · doi:10.1155/asp.2005.306

Performance and Capacity of PAM and PPM UWB Time-Hopping Multiple Access Communications with Receive Diversity

2005· article· en· W2163473895 on OpenAlexaff
Hao Zhang, T. Aaron Gulliver

Bibliographic record

VenueEURASIP Journal on Advances in Signal Processing · 2005
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPulse-position modulationAdditive white Gaussian noiseComputer scienceTime-hoppingDiversity combiningTelecommunicationsPulse-amplitude modulationDiversity gainProbability of errorTime diversityModulation (music)Electronic engineeringChannel capacityFadingSIGNAL (programming language)Channel (broadcasting)AlgorithmPulse (music)PhysicsEngineeringAcoustics

Abstract

fetched live from OpenAlex

The error probability and capacity of a time-hopping ultra-wideband (UWB) communication system with receive diversity are investigated. We consider pulse amplitude modulation (PAM) and pulse-position modulation (PPM) over additive white Gaussian channels for a single-user system. A multiuser environment with PPM is also investigated. It is shown that the communication distance and error performance are improved by employing receive diversity. The channel capacity of PPM and PAM is determined subject to the power constraints of FCC part 15 rules to illustrate the relationship between reliable communication distance and signal-to-noise ratio. The error probability with PAM and receive diversity is derived for the single-user case. The error probability and performance bounds with PPM are derived for both the single-user and multiuser cases.

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)0.003
metaresearch head score (Gemma)0.018
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.021
GPT teacher head0.253
Teacher spread0.232 · 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

Citations22
Published2005
Admission routes1
Has abstractyes

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