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Record W1730511642

Compact multiport antennas for high spectral efficiency

2013· article· en· W1730511642 on OpenAlexaff
Rodney G. Vaughan

Bibliographic record

VenueEuropean Conference on Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWirelessComputer scienceElectronic engineeringSpectral efficiencyAntenna (radio)Smart antennaDirectional antennaConformal antennaPoint (geometry)TelecommunicationsChannel (broadcasting)Electrical engineeringEngineeringSlot antennaMathematics
DOInot available

Abstract

fetched live from OpenAlex

The pursuit of wireless spectral efficiency draws on many different research areas. The area of largest potential impact is the deployment of multiport antennas. This is because it is the only technology that allows simultaneous sharing of the spectrum between many users, including full duplex operation in some circumstances. The spectral efficiencies of current communications system designs are still a long way from their information-theoretic limits, and similarly, current multi-element antenna designs seem to fall short of compactness limits. This invited paper, in tutorial style, touches on how wireless has reached this point, and the need to address grand challenges in information theory, communications techniques, networking, antenna elements and arrays, and signal theory. These aspects converge to set the scene for a new generation of adaptive antenna technology. The motivation is from basic energy and communications considerations. The design of compact multiport antennas requires an extension of classical performance metrics and new approaches to measurement and evaluation. Tools such as physics-based statistical channel and circuit models are likely to play a future role in the design of large-scale multiport antennas.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.645

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 head0.021
GPT teacher head0.227
Teacher spread0.206 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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