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A MIMO Joint Communication-Radar Measurement Platform at the Millimeter-Wave Band : (Invited Paper)

2020· article· en· W3041643240 on OpenAlexaff
Preeti Kumari, Amine Mezghani, Robert W. Heath

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Photonic Communication Systems
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceRadarElectronic engineeringExtremely high frequencyWidebandMIMOBandwidth (computing)Remote sensingChannel (broadcasting)TelecommunicationsEngineeringGeography

Abstract

fetched live from OpenAlex

A fully-digital wideband joint communication-radar (JCR) at the millimeter-wave (mmWave) band will simultaneously enable high communication and radar performances with enhanced design flexibility. In this paper, we present a measurement platform with a software-defined architecture to evaluate and demonstrate the performance of these JCR systems using real channel measurements. We develop this platform by extending a mmWave communication set-up with an additional full-duplex radar receiver and by capturing the MIMO JCR channel using a moving antenna on a sliding rail. To characterize the JCR performance, we conduct indoor experiments and apply traditional/advanced processing algorithms on the measured data. The results demonstrate that our testbed at 73 GHz with 2 GHz bandwidth can capture the JCR channel with high range/direction estimation accuracy. The comparison between the communication and radar channel shows the potential for improving JCR performance by exploiting the antenna diversity due to widely-separated communication and radar receivers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.611

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)0.0010.000

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 head0.100
GPT teacher head0.227
Teacher spread0.126 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2020
Admission routes1
Has abstractyes

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