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A Multi-Beam Antenna Using a Partially Reflective Surface for 5G Applications

2025· article· W4417132114 on OpenAlexaff
Azita Goudarzi, Mohammad Mahdi Honari, Rashid Mirzavand

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRadiator (engine cooling)Antenna (radio)Beam steeringBeam (structure)RadiationPeriscope antennaSurface (topology)Reflection (computer programming)

Abstract

fetched live from OpenAlex

This paper presents a multibeam Fabry-Pérot cavity (FPC) antenna capable of radiating at different angles from broadside. The proposed design consists of a metallic wall, a main radiator with a tilted beam, and a metasurface that functions as a partially reflective surface (PRS) positioned above the structure. The metasurface is designed to generate multiple beams by leveraging a ray-tracing-based analysis, where the resonance condition is satisfied at predefined beam angles. This approach eliminates the need for complex and bulky radiators, active elements, or mechanical adjustments, thereby simplifying the design while enhancing functionality. The antenna demonstrates simultaneous dual-beam radiation at elevation angles of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta_{\text {beam }, 1}=20^{\circ}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta_{\text {beam }, 2}=42^{\circ}$</tex> with a gain of 11 dBi at 27 GHz. This performance highlights the capability of the metasurface-enhanced FPC to achieve efficient beam steering without additional complexity. The findings suggest that this approach is promising for advanced communication systems requiring high-gain, multibeam radiation pattern.

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: Methods · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.045
GPT teacher head0.339
Teacher spread0.294 · 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
GenreMethods

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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