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Record W4293863365 · doi:10.1109/siu55565.2022.9864821

Comparison of Relay and Reconfigurable Intelligent Surfaces for Millimeter-Wave Communication Systems

2022· article· en· W4293863365 on OpenAlexaff
Burak Ahmet Celebi, Ubeydullah Erdemir, Ali Görçin

Bibliographic record

Venue2022 30th Signal Processing and Communications Applications Conference (SIU) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsRelayBandwidth (computing)WirelessComputer scienceExtremely high frequencyPath lossBit error rateCommunications systemElectronic engineeringEnergy consumptionEnergy exchangeElectrical engineeringTelecommunicationsEngineeringChannel (broadcasting)Physics

Abstract

fetched live from OpenAlex

New generation wireless communication technologies require a higher data rates day by day. Since low frequencies are mostly in use, it is necessary to go to higher frequencies in order to reach the required bandwidth. High frequencies are more affected by path losses than lower frequencies, therefore it becomes logical to use systems that increase signal strength at the receiver, such as amplify and forward (AF), decode and forward (DF), and reconfigurable intelligent surfaces (RIS). Considering all this information, relay and reconfigurable intelligent surfaces are compared for 28 GHz band which is experimental models are made, frequently used in literature, and mostly standardised. By comparing the bit error rate and energy consumption, it is investigated which systems would be more efficient to use under which scenarios, and the results were supported by simulations.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.306
Teacher spread0.235 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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