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Record W2592799800 · doi:10.1109/mmwatt.2016.7869872

Performance investigation of space diversity for a 28/38 GHz MIMO antenna (applicable to mm-wave mobile network)

2016· article· en· W2592799800 on OpenAlexaff
Hanieh Aliakbari, Abdolali Abdipour, Alessandra Costanzo, Diego Masotti, Rashid Mirzavand, Pedram Mousavi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMIMOAntenna (radio)3G MIMOElectronic engineeringAntenna diversityDiversity gainExtremely high frequencyComputer scienceFadingAntenna factorAntenna measurementPhysicsTelecommunicationsAcousticsEngineeringChannel (broadcasting)

Abstract

fetched live from OpenAlex

A multiple-input-multiple-output (MIMO) dual-band antenna system consisting of two elements, each of which built up of circularly polarized slotted patch antenna for mm-wave band 5G wireless handheld and portable terminals is described. Space diversity is used to achieve a good independent channel fading in MIMO systems. In theoretical analysis of MIMO systems often assume that each antenna is isolated from the other elements. It has been shown that the antenna spacing has a significant effect on both near-field and far-field performance of each element in two independent working bands and should be optimized in millimeter wave compact dual-band MIMO arrays. However, the main problem is that an optimized distance for lower band may not be suitable for upper band and vice versa. If electromagnetic limitations and interactions of the antennas are ignored, the MIMO system performance may be underestimated or overestimated.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.191
Teacher spread0.173 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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