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Record W2247727845

Indoor wireless reception improvement using cross-polarized multipath signals

2006· article· en· W2247727845 on OpenAlexafffund
Luis E. Gurrieri, Sima Noghanian, T.J. Willink

Bibliographic record

VenueuO Research (University of Ottawa) · 2006
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsCommunications Research Centre CanadaUniversity of Manitoba
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsMultipath propagationNon-line-of-sight propagationOmnidirectional antennaComputer sciencePolarization (electrochemistry)Electronic engineeringAcousticsWirelessPhysicsOpticsTelecommunicationsAntenna (radio)Engineering
DOInot available

Abstract

fetched live from OpenAlex

Previous work has noted the distinct characteristics of vertically and horizontally polarized multipath components for indoor non-line-of-sight (NLOS) environments. Using measured data, the receiver signal-to-noise ratios (SNRs) for coherent and noncoherent combining of the co- and cross-polarized multipath components are compared to those obtained with omnidirectional reception for vertical or horizontal polarizations. It is seen that significant improvements in SNR can be achieved using intelligent combining with polarization diversity. Furthermore, it is observed that when the receiver is unable to resolve multipath components, coherent combination of both orthogonally polarized signals components provides a consistent advantage over vertically or horizontally polarized omnidirectional reception. At higher bandwidths, the increased resolution improves the performance of all techniques however the dual polarization multipath combining retains its relative advantage over the other techniques.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.310
Teacher spread0.261 · 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 designBench or experimental
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 routes2
Has abstractyes

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