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Record W4401093961 · doi:10.1155/2024/7480655

A Novel and Compact Metamaterial‐Based Four‐Element MIMO Antenna System for Millimeter‐Wave Wireless Applications with Enhanced Isolation

2024· article· en· W4401093961 on OpenAlexaff
Iftikhar Ud Din, Nisar Ahmad Abbasi, Waheed Ullah, Sadiq Ullah, Messaoud Ahmad Ouameur, Dushantha Nalin K. Jayakody

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersFundação para a Ciência e a Tecnologia
KeywordsMIMOElectronic engineeringMicrowavePhysicsMillimeterEngineeringAcousticsComputer scienceElectrical engineeringTelecommunicationsOpticsBeamforming

Abstract

fetched live from OpenAlex

A compact fork‐shaped MIMO antenna system with a 2 × 2 arrangement with four elements is presented. The MIMO elements are arranged orthogonally to achieve a small overall size of 36 × 28 mm 2 and a wide bandwidth for 5G mm‐wave applications. MIMO elements are positioned 4 mm from each corner of the substrate to achieve compact size and minimize coupling. To improve the isolation of the proposed MIMO system, a metamaterial slab is inserted in the middle of the substrate and between radiating elements of the MIMO antenna system, which improves isolation by 10 dB within the whole operating band and achieves maximum isolation of 65 dB at 34.5 GHz. The proposed MIMO system operates in the Ka‐band frequency range of 22–50 GHz with isolation greater than 30 dB and efficiency above 80% across the entire frequency spectrum for 5G communication. Additionally, the performance parameters of MIMO are examined, including diversity gain (DG) and envelope correlation coefficient (ECC), and it is found that they meet the required standards of DG approximately equal to 10 and ECC < 0.05. The proposed MIMO system has been fabricated and tested. The measured results are consistent with the design of the simulated structure using the CST Microwave Studio (CSTMWS) simulator.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.422

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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 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

Citations10
Published2024
Admission routes1
Has abstractyes

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