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Record W2907269937 · doi:10.1109/pimrc.2018.8580725

Performance Gains of Three-Dimensional MIMO Antenna Structures

2018· article· en· W2907269937 on OpenAlexaff
Samer Henry, Ahmed Alsohaily, E.S. Sousa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAntenna (radio)Computer science3G MIMOOmnidirectional antennaMIMOElectronic engineeringAntenna arrayComputer networkTelecommunicationsEngineeringBeamforming

Abstract

fetched live from OpenAlex

In order to face rising traffic demands and reduce the rigid requirements for antenna array design and operation, antenna placement geometry is being embraced by multiple automotive and cellular companies. Despite being supported in IMT2020 and 3GPP standards, MIMO antenna placement has been limited to two dimensional arrays, restricting antenna orientation flexibility and lack of scalability for Multi-user MIMO capabilities. This paper highlights the benefits of arbitrary placement of MIMO elements in 3D and introduces reconfigurable antenna structures for the base station as well as the UE, represented as a vehicular antenna array to expand the technology to Internet-of-Things (IoT) devices. The parameters of the patch antenna used are outlined for novel vehicular antenna structures. A 40% improvement is observed in user throughput for urban and suburban areas over typical 2D grid arrays.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.228
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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