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A Wideband Metasurface Waveguide Antenna with Low Sidelobe Levels at Ka Band

2025· article· W4417403100 on OpenAlexaff
Zhao Wang, Zhen Hu, Yuehe Ge, Ziheng Zhou, Zhechen Zhang, Zhizhang Chen

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsDalhousie University
FundersNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsWidebandSlotted waveguideBandwidth (computing)Ka bandRadiationWaveguideSlot antennaAntenna efficiency

Abstract

fetched live from OpenAlex

This paper presents a wideband metasurface waveguide (MSWG) antenna with low sidelobe levels (SLLs) for high-performance leaky-wave applications. The design integrates a metasurface layer into the waveguide's broad wall, replacing conventional slots to enable precise phase/amplitude modulation of guided waves. Leveraging Taylor synthesis ($\overline{\boldsymbol{n}}=\mathbf{5}$, SLL =-25 dB) and a power-transmission method, the design ensures sidelobe levels below −20 dB and a 17.1° beam-scanning range. Full-wave simulations validate the antenna's performance, demonstrating stable radiation patterns, a 5.4 % 3-dB gain bandwidth (27-28.5 GHz), with a peak gain of 20 dBi and radiation efficiency exceeding 60 %, and low SLLs from 26.7-28.7 GHz. The proposed architecture offers a compact, cost-effective alternative to traditional waveguide slot arrays, addressing their limitations in bandwidth and fabrication complexity while enhancing beamscanning capabilities for next-generation 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 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.014
GPT teacher head0.243
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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