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Record W3003713006 · doi:10.1109/lwc.2020.2994930

Reconfigurable Intelligent Surface-Assisted Space Shift Keying

2020· preprint· en· W3003713006 on OpenAlexafffund
Ayşe Elif Canbilen, Ertuğrul Başar, Salama Ikki

Bibliographic record

VenueIEEE Wireless Communications Letters · 2020
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaTürkiye Bilimsel ve Teknolojik Araştırma Kurumu
KeywordsTransmission (telecommunications)KeyingModulation (music)Computer scienceScheme (mathematics)DetectorElectronic engineeringSpace (punctuation)Energy (signal processing)Bit error rateSurface (topology)Molecular communicationTelecommunicationsAlgorithmMathematicsPhysicsEngineeringAcousticsStatistics

Abstract

fetched live from OpenAlex

The concept of reconfigurable intelligent surface (RIS)-assisted transmission, where phases of reflected signals are adjusted in an effective arrangement, has been recently put forward as a promising solution for 6G and beyond networks. Considering this and the undeniable potential of index modulation (IM) techniques, an RIS-based space shift keying (SSK) scheme is proposed in this letter to maintain all inherent advantages of both RISs and SSK. Specifically, a mathematical framework is presented by using a maximum likelihood (ML) detector for the calculation of the average bit error rate (ABER). Extensive computer simulation results are provided to assess the potential of the proposed RIS-based SSK system and to verify the theoretical derivations. The obtained results indicate that the proposed scheme enables highly reliable transmission with unconventionally high energy efficiency, however with the added cost of increased receiver complexity.

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 categoriesMeta-epidemiology (narrow), Open science, Research integrity
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 score0.691
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0070.002
Research integrity0.0010.004
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 head0.060
GPT teacher head0.278
Teacher spread0.218 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

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