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Record W4205984437 · doi:10.1109/tvt.2022.3143841

RIS-Aided Communications in Indoor and Outdoor Environments: Performance Analysis With a Realistic Channel Model

2022· article· en· W4205984437 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChannel (broadcasting)Computer scienceElectronic engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Reconfigurable intelligent surfaces (RIS) hold the potential to revolutionize the wireless communications industry via the dynamic control of the wireless channels to help achieve high data rates, high spectral and energy efficiencies, and low error rates, towards meeting the advanced specifications of B5G. In this context, this paper proposes a practical channel model for RIS-aided MIMO communications which considers the effects of RIS configurations, operating environments, path loss, scattering, etc. With this comprehensive channel model, the probability density function and the cumulative distribution function of the received signal-to-noise power ratio are derived by utilizing a double generalized K distribution, and closed-form expressions for the system’s error rate, outage probability, and channel capacity, are obtained. The analysis highlights the outperforming of the RIS-aided communications in indoor environments as compared to outdoor use cases due to the presence of less scatterers in the former.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.675
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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