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Record W2143548765 · doi:10.1109/glocom.2006.779

WLC28-5: Impact of Frequency Offset and Timing Offset on the Performance of SC-FDE UWB

2006· article· en· W2143548765 on OpenAlexaff
Yue Wang, Xiaodai Dong

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCarrier frequency offsetFrequency offsetSC-FDEOffset (computer science)Computer scienceSampling (signal processing)Orthogonal frequency-division multiplexingNyquist rateChannel (broadcasting)Electronic engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

We investigate in this paper the effect of carrier frequency offset (CFO) and sampling timing offset (STO) on the performance of single carrier block transmission with frequency domain equalization (SC-FDE) over ultra-wideband (UWB) channels. Signal-to-interference-plus-noise ratio (SINR) of SC-FDE UWB in the presence of CFO is analyzed and compared with that of OFDM, where OFDM is shown to be more sensitive to CFO. We also study the effect of STO on the performance of SC-FDE. Two forms of STO are considered, i.e., a shifted sampling timing instant other than the optimal timing point and a distorted sampling rate other than the symbol rate. Our results show that the performance of SC-FDE is rather channel dependent when sampling at a non-optimal time instant within one symbol duration, where the energy of the sampled equivalent channel at the sampling point determines its BER performance. Moreover, an SC-FDE system is fairly sensitive to a distorted sampling rate at the receiver, where severe performance degradation can occur when the received signal is sampled at a sampling rate rather than symbol rate.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.288

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 head0.009
GPT teacher head0.215
Teacher spread0.207 · 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 designObservational
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

Citations1
Published2006
Admission routes1
Has abstractyes

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