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Record W4386915377 · doi:10.36227/techrxiv.24153384.v1

Noise Shaping for Phased Array with Overlapped Sub-Array System

2023· preprint· en· W4386915377 on OpenAlex
Shahin Sheikh, Ahmed A. Kishk, Tayeb A. Denidni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsConcordia UniversityInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBeamformingPhased arrayPhased-array opticsSuperposition principleComputer scienceQuantization (signal processing)Distortion (music)Electronic engineeringAcousticsTelecommunicationsEngineeringPhysicsBandwidth (computing)Algorithm

Abstract

fetched live from OpenAlex

A novel method is proposed for spectrally shaping the beamforming weights quantization error at the sub-array layer where the in-band distortion intends to move into a position where the sub-array factor has high attenuation. To do that, the sub-array factor or the composite sub-array factor is tiled by the periodicity of the ultimate-layer array factor. Then, the digital filter layout is designed based on the superposition of all tiles intersecting with the visible region of the overall array factor (O-AF), and the O-AF is spectrally shaped to minimize the number of bits quantifying the beamforming weights. The method is investigated for analog, digital, and hybrid array beamforming with multi-layer overlapped sub-array systems of different sizes and shapes. Its performance is promising and potent in alleviating the overall array factor distortion in all cases.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.938
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 head0.038
GPT teacher head0.231
Teacher spread0.193 · 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

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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