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Record W4414008202 · doi:10.1109/lawp.2025.3606542

Multibit Electrically Reconfigurable Circularly Polarized Reflectarray Elements Based on Pancharatnam–Berry Phase Principle

2025· article· en· W4414008202 on OpenAlexaff
Guowei Li, Yuehe Ge, Ziheng Zhou, Zhechen Zhang, Jiade Yuan, Zhimeng Xu, Zhizhang Chen

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsGeometric phaseCircular polarizationPhysicsPhase (matter)OpticsBit (key)Computer scienceCondensed matter physicsQuantum mechanicsMicrostrip

Abstract

fetched live from OpenAlex

PIN diode-based reconfigurable reflectarray antennas have gained attention for their flexible beam-steering, simplified design, and cost effectiveness. However, many existing designs offer only 1- or 2-bit phase reconfigurability, limiting radiation performance. In this letter, we present Ku-band reflectarray elements with both 2-bit and 3-bit electrical phase control for circular polarization. By leveraging the Pancharatnam-Berry phase principle, dynamic phase control is achieved through PIN diode switching. The proposed design is validated using a waveguide simulator (WGS), which confirms the 2-bit phase responses and shows excellent agreement with full-wave simulations. Measured results demonstrate 2-bit phase quantization over an 11.1-12.5 GHz range (11.9% bandwidth) with insertion losses below 1.0 dB and cross-polarization levels under -15 dB. Furthermore, simulated performance of the 3-bit element reveals consistent 45° phase intervals. To verify array-level performance, 16×16 RRAs based on both 2-bit and 3-bit elements are implemented. This multi-bit reconfigurable strategy enhances reflectarray performance and offers increased design versatility for advanced Ku-band applications.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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