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Record W2113691179 · doi:10.1109/twc.2007.060141

SNR Estimation Methods for UWB Systems

2007· article· en· W2113691179 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2007
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdditive white Gaussian noiseEstimatorBandwidth (computing)Computer sciencePhase-shift keyingKeyingTime-hoppingBinary numberAlgorithmMultipath propagationFadingGaussian noiseChannel (broadcasting)GaussianUltra-widebandTelecommunicationsElectronic engineeringBit error rateMathematicsStatisticsPulse-amplitude modulationPulse (music)PhysicsEngineering

Abstract

fetched live from OpenAlex

The problem of estimating the signal-to-noise ratio for time-hopping binary pulse position modulated signals, time- hopping binary phase shift keying signals, and direct-sequence binary phase shift keying signals in an ultra-wide bandwidth (UWB) system is studied. Both an additive white Gaussian noise channel and a multipath fading channel are considered. Several new estimators are derived by making use of different properties of the UWB signals. The performances of the estimators are examined and compared in terms of their root-mean-squared errors. Numerical results show that they have excellent performances when operating in an ultra-wide bandwidth system.

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.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.332
Teacher spread0.300 · 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