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Record W2922235965 · doi:10.3390/app9061082

Reception of OAM Radio Waves Using Pseudo-Doppler Interpolation Techniques: A Frequency-Domain Approach

2019· article· en· W2922235965 on OpenAlexaff
Marek Klemes

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsHuawei Technologies (Canada)
Fundersnot available
KeywordsPhysicsOpticsDoppler effectMIMOAcousticsAntenna (radio)Computer scienceTelecommunicationsBeamforming

Abstract

fetched live from OpenAlex

This paper presents a practical method of receiving waves having orbital angular momentum (OAM) in the far field of an antenna transmitting multiple OAM modes, each carrying a separate data stream at the same radio frequency (RF). The OAM modes are made to overlap by design of the transmitting antenna structure. They are simultaneously received at a known far-field distance using a minimum of two antennas separated by a short distance tangential to the OAM conical beams’ maxima and endowed with different pseudo-Doppler frequency shifts by a modulating arrangement that dynamically interpolates their phases between the two receiving antennas. Subsequently down-converted harmonics of the pseudo-Doppler shifted spectra are linearly combined by sets of weighting coefficients which effectively separate each OAM mode in the frequency domain, resulting in a higher signal-to-noise ratios (SNR) than possible using spatial-domain OAM reception techniques. Moreover, no more than two receiving antennas are necessary to separate any number of OAM modes in principle, unlike conventional MIMO (Multi-Input, Multi-Output) which requires at least K antennas to resolve K spatial modes.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.403

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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations10
Published2019
Admission routes1
Has abstractyes

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