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Record W1534293170 · doi:10.1109/ccece.2001.933729

Nonlinear channel estimation using correlation properties of PN sequences

2002· article· en· W1534293170 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Adaptive Filtering Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNonlinear systemChannel (broadcasting)CorrelationComputer scienceMathematicsAlgorithmStatisticsTelecommunicationsPhysicsGeometry

Abstract

fetched live from OpenAlex

Nonlinear distortion of the radio-over-fiber (ROF) link and, multipath dispersion of the wireless link are the two major factors that limit the performance of a fiber based wireless system. This is especially true when the radio frequency is only a few GHz. The fact that both of these impairing factors are generally unknown, makes any equalization effort very difficult. In this paper, an algorithm that estimates both the nonlinear transfer function of the ROF link plus the impulse response of the wireless channel is described. Correlation properties of pseudonoise (PN) sequences are used for this estimation. This eases the implementation because, PN sequences are widely used in spread spectrum systems and their properties are well understood. An efficient, Vandermonde matrix approach is used to separate the Volterra kernels of the fiber-wireless channel which, eliminates the computation of higher order correlation functions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.261

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 head0.062
GPT teacher head0.241
Teacher spread0.179 · 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

Quick stats

Citations12
Published2002
Admission routes1
Has abstractyes

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