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Record W3030578636 · doi:10.1109/access.2020.2998439

Non-Reciprocity View of the MIMO Antenna Arrays in Transmitting and Receiving Modes Using the Maximized Unique Receiving Pattern Theory Resulted by Angle-Wise Array Factor

2020· article· en· W3030578636 on OpenAlexaff
Babak Molaei, Ahmed A. Kishk

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsMIMOReciprocity (cultural anthropology)Mode (computer interface)UniquenessRadiation patternComputer scienceAntenna arrayAntenna (radio)GratingTopology (electrical circuits)Electronic engineeringAlgorithmMathematicsControl theory (sociology)TelecommunicationsPhysicsOpticsEngineeringMathematical analysisChannel (broadcasting)Artificial intelligence

Abstract

fetched live from OpenAlex

A new approach to employ MIMO array behavior in system design calculations by distinguishing the transmitting and receiving modes' characteristics is presented. It shows that the conventional array formulation complies merely with the transmitting mode behavior of MIMO arrays, while the newly proposed formulation eases the estimation of the receiving mode behavior by introducing the Angle Wise Array Factor (AWAF). A novel theory of “Non-reciprocity view of transmitting and receiving modes in MIMO arrays” is being discussed along with “Pattern non-uniqueness in transmitting mode” and “Pattern uniqueness in receiving mode” theories. The grating lobe definition is also re-introduced, proposing that this definition is supposed to be reserved merely for transmitting mode.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.033
GPT teacher head0.250
Teacher spread0.217 · 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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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