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Record W2519326593 · doi:10.1049/iet-map.2016.0738

Compact 4G MIMO antenna integrated with a 5G array for current and future mobile handsets

2016· article· en· W2519326593 on OpenAlexaff
Rifaqat Hussain, Ali T. Alreshaid, Symon K. Podilchak, Mohammad S. Sharawi

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsRoyal Military College of Canada
FundersKing Fahd University of Petroleum and Minerals
KeywordsMIMOAntenna (radio)Electronic engineeringCurrent (fluid)Antenna array3G MIMOElectrical engineeringComputer scienceTelecommunicationsEngineeringPhysicsBeamforming

Abstract

fetched live from OpenAlex

A novel integrated antenna solution for wireless handheld devices is proposed for the existing 4G standards and upcoming 5G systems for broadband, high data rate communications. The complete antenna system is a unique combination of a multiple‐input–multiple‐output (MIMO) antenna system at microwave frequencies and a millimetre (mm)‐wave antenna array. The MIMO antenna system consists of two reactive loaded monopoles while the mm‐wave array consists of a planar 2 by four slot antennas. The integrated antenna system covers the frequency bands from 1870 to 2530 MHz for 4G standards along with the upcoming 5G mm‐wave band at 28 GHz. In addition, the integrated antenna system is planar and is designed for typical smart phone devices with a standard 60 mm by 100 mm by 0.965 mm back plane. Excellent field correlation values were obtained across the 4G band while realised peak gain values of 4 and 8 dBi were, respectively, measured for the MIMO and mm‐wave antenna arrays. The proposed antenna design may also be useful for other compact implementations that support 4G and 5G communications.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.009
GPT teacher head0.219
Teacher spread0.210 · 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 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

Citations201
Published2016
Admission routes1
Has abstractyes

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