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Record W4293868569 · doi:10.1109/ims37962.2022.9865247

Virtual Receiver Matrix for Future Multifunction Wireless Systems

2022· article· en· W4293868569 on OpenAlexaff
Seyed Ali Keivaan, Pascal Burasa, Ke Wu

Bibliographic record

Venue2022 IEEE/MTT-S International Microwave Symposium - IMS 2022 · 2022
Typearticle
Languageen
FieldEngineering
TopicMillimeter-Wave Propagation and Modeling
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsComputer scienceWirelessDemodulationElectronic engineeringQuadrature amplitude modulationSIGNAL (programming language)Topology (electrical circuits)Channel (broadcasting)Electrical engineeringEngineeringComputer networkTelecommunicationsBit error rate

Abstract

fetched live from OpenAlex

In this paper, a concept of Virtual Receiver Matrix (VRM) is introduced and presented for future multifunction wireless systems. This topological architecture is made possible through the development of a two-dimensional (2D) array of spatially distributed receivers based on the grouping of receiver building blocks. This matrix scheme is set to significantly enhance the number of possible virtual receivers made of combinatory unit circuits, thus opening up an unprecedented platform to create and integrate multiple wireless operations altogether. In this scheme, each cell of VRM, called half-receiver, can demodulate either in-phase or quadrature component of a QAM signal, depending on the receiving illumination angle of a wireless signal. In addition to conventional receiving functions, the devised VRM can detect the Angle of Arrival (AoA) and the polarization of incident waves. In this work, the mathematical modeling and experimental results from a fabricated VRM prototype are presented for the proof-of-concept.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.227
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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations4
Published2022
Admission routes1
Has abstractyes

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