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Towards the Development of Large-Scale Multifunction Array Transceiver Systems

2023· article· en· W4392026431 on OpenAlexaff
Yasser Bigdeli, Seyed Ali Keivaan, Pascal Burasa, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsTransceiverComputer scienceScale (ratio)Computer hardwareElectronic engineeringElectrical engineeringTelecommunicationsEngineeringPhysicsWireless

Abstract

fetched live from OpenAlex

This paper reviews and examines our recent work on the development of topological transceiver array frontends to enable reconfigurable multi-functionality and deep structural integration at millimeter wave (mmW) and terahertz (THz) frequencies. The theory and principle of an unprecedented virtual transceiver matrix (VTM) technique were proposed with its demonstration through interferometric schemes. This unique topology is based on the selective allocation of receiver and transmitter units that incorporate angle-of-arrival (AoA) and beamforming operations for smart and efficient simultaneous multifunction applications. On the other hand, our proposed ultra-low-power quadrature harmonic self-oscillating mixer (QHSOM) provides the most compact stand-alone quadrature receiver unit ever-developed so far. Its special features can ideally serve in the realization of a reconfigurable distributed large-scale array transceiver. Topology-enabled superiority besides flexible transceiver units sets up a framework for 5G/6G and future development path of multifunction THz 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 score0.002
Threshold uncertainty score0.006

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 communication0.0010.001
Open science0.0010.001
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 head0.021
GPT teacher head0.218
Teacher spread0.197 · 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
Published2023
Admission routes1
Has abstractyes

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