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Record W4391759835 · doi:10.1109/lawp.2024.3364718

Dual-Band Metasurface-Based Closely Packed Antennas by Controlling Surface Wave Propagation

2024· article· en· W4391759835 on OpenAlexaff
Qi Zheng, Judao Wang, Peyman PourMohammadi, Xiaoyan Pang

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
FundersNatural Science Foundation of Shanghai
KeywordsMulti-band deviceBandwidth (computing)MIMOSlot antennaAntenna (radio)PhysicsRadiation patternOpticsRadiationImpedance matchingComputer scienceElectrical impedanceTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a dual-band metasurface (MS)-based slot antenna with bidirectional and unidirectional radiation patterns is proposed. The MS is simultaneously used to reduce mutual coupling for the closely packed antenna system. The proposed antenna is a single-layer CPW-fed slot antenna integrated with an MS-based superstrate. The lower band and the higher band are generated by the slot and the MS, respectively, which yields the advantages of independent and flexible frequency control. The impedance matching bandwidths (|S11|<-10 dB) are 3.44-3.54 GHz (a relative bandwidth of 2.9%) and 4.38-5.84 GHz (28.6%). Bidirectional and unidirectional radiation patterns are obtained in the two bands, respectively. Moreover, a 2 X 1 edge-to-edge closely packed (∼0 space) dual-band antenna array is proposed by simply reusing the MS for multiple-input multiple-out (MIMO) systems. Over 18.3 dB and 19.7 dB isolations are obtained by suppressing the surface wave in the lower band and steering the radiation pattern in the higher band. The proposed antennas can find applications in dual-band MIMO and 5G communication systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.891
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.012
GPT teacher head0.207
Teacher spread0.195 · 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.

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

Citations14
Published2024
Admission routes1
Has abstractyes

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