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Record W2114394372 · doi:10.1109/glocom.2007.842

On Frequency Domain Doppler Diversity Using Basis Expansion Model and EM-Based Algorithms in CDMA Systems

2007· article· en· W2114394372 on OpenAlexaff
Tianqi Wang, Cheng Li, Hsiao‐Hwa Chen, Mohsen Guizani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDoppler effectAlgorithmCramér–Rao boundComputer scienceCode division multiple accessFrequency domainChannel (broadcasting)Basis (linear algebra)Expectation–maximization algorithmEstimation theoryMaximum likelihoodTelecommunicationsMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

In this paper, we propose an algorithm for frequency domain adaptive Doppler shift estimation in the presence of multiple Doppler subpaths in DS-CDMA systems. By modeling doubly selective channel with a basis expansion model (BEM), the proposed method works based on expectation-maximization (EM) algorithm and can accurately estimate Doppler shifts. A complete Cramer-Rao lower bound (CRLB) analysis of the estimation algorithms is also provided.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.403
Threshold uncertainty score0.460

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.059
GPT teacher head0.301
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations0
Published2007
Admission routes1
Has abstractyes

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