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Record W2096234306 · doi:10.1109/icc.2007.710

Generalized CRLB for DA and NDA Synchronization of UWB Signals with Clock Offset

2007· article· en· W2096234306 on OpenAlexaff
S. Khalesehosseini, John Nielsen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsCramér–Rao boundFrequency offsetTransmitterUpper and lower boundsMultipath propagationUTC offsetAlgorithmOffset (computer science)Computer scienceSynchronization (alternating current)Electronic engineeringMathematicsEstimation theoryTelecommunicationsOrthogonal frequency-division multiplexingEngineeringGlobal Positioning System

Abstract

fetched live from OpenAlex

In this paper the Cramer-Rao lower bound (CRLB) of an ultra-wideband (UWB) pulse amplitude modulated (PAM) signal with time hopping (TH) code is derived for the practical case where there is an initial unknown relative clock offset between the transmitter and receiver. However, it is assumed that there is a priori information about the probability density function of clock offset. CRLB expressions are developed for both the data- aided (DA) case with an assumed deterministic pilot for synchronization and for the non data-aided (NDA) case based on blind synchronization. It is shown that the influence of the unknown clock offset on the synchronization performance becomes negligible as the number of multipath components increases emphasizing an advantage in UWB links where the multipath is typically rich. Another observation made is that as the SNR decreases, the difference in the CRLB for the NDA and DA cases increases emphasizing the significance of a pilot. Having a priori information helps to achieve a lower CRLB for the timing or clock offset estimation. This improvement is significant in the case of NDA. Finally at very low SNR CRLB of the clock offset converges to the variance of clock offset based on only a priori information.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.462
Threshold uncertainty score0.224

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 science0.0000.000
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 head0.010
GPT teacher head0.229
Teacher spread0.220 · 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 designBench or experimental
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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