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Record W2030373593 · doi:10.1049/iet-com:20080021

Cramer–Rao lower bound for data-aided and non-data-aided synchronisation of ultra-wideband signals with clock offset

2009· article· en· W2030373593 on OpenAlexaff
S. Khalesehosseini, John Nielsen

Bibliographic record

VenueIET Communications · 2009
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCramér–Rao boundMultipath propagationFrequency offsetTransmitterComputer scienceUpper and lower boundsWidebandOffset (computer science)Channel (broadcasting)Ultra-widebandElectronic engineeringAlgorithmEstimation theoryTelecommunicationsOrthogonal frequency-division multiplexingMathematicsEngineering

Abstract

fetched live from OpenAlex

The Cramér–Rao lower bound (CRLB) for the data-aided (DA) and non-data-aided (NDA) estimation of ultra-wideband (UWB) multipath channel parameters has been previously derived in the literature based on the assumption of a perfectly synchronised clock frequency between the transmitter and the receiver. These results are extended by considering the practical case where there is an initial unknown clock frequency offset that needs to be jointly estimated as part of a successful acquisition of the UWB signal. Intuitively, the additional uncertainty of the clock offset should inflate the CRLB for the estimation of the UWB channel parameters. However, as shown here, this is only valid for a small number of multipath components. As the number of multipath components is increased, the inflation of the CRLB of the channel parameter estimation because of the unknown clock offset becomes negligible. This is of practical significance for UWB propagation channels as they are typically characterised by a large number of multipath components.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0040.000
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 head0.044
GPT teacher head0.293
Teacher spread0.249 · 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.

Study designNot applicable
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

Citations2
Published2009
Admission routes1
Has abstractyes

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