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Record W2000477931 · doi:10.1109/vetecf.2007.264

Improved Estimation of the Ricean K Factor from I/Q Samples

2007· article· en· W2000477931 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Vehicular Technology Conference · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsCommunications Research Centre Canada
FundersDefence Research and Development Canada
KeywordsEstimatorFadingRobustness (evolution)Computer scienceMaximum likelihoodWirelessStatisticsAlgorithmChannel (broadcasting)Estimation theoryMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Knowledge of the Ricean K factor is important in many communication applications as it reveals the fading severity experienced by a wireless receiver. In this paper, a statistical analysis of the maximum likelihood estimator (MLE) of K from complex (I/Q) Ricean channel samples is derived. Based on our analysis, an improved estimator of the K factor is proposed with superior bias and efficiency properties for short data records. The lack of robustness of the tractable MLE in mobile applications is addressed by incorporating a reliable estimator of the specular Doppler shift. The simulation results show that the proposed technique can outperform prior methods for I/Q mobile channel snapshots.

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)0.000
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: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.467

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 science0.0010.000
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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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