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Record W4401507783 · doi:10.1109/lwc.2024.3442170

RIS-Assisted Receive Generalized Space-Shift Keying and Receive Generalized Spatial Modulation

2024· article· en· W4401507783 on OpenAlexaff
Porfirio A. Marín, Muhammad Hanif, Ebrahim Bedeer

Bibliographic record

VenueIEEE Wireless Communications Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsThompson Rivers UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsSpatial modulationModulation (music)Computer scienceKeyingSpace (punctuation)Phase-shift keyingTopology (electrical circuits)TelecommunicationsMathematicsPhysicsBit error rateCombinatoricsDecoding methodsAcousticsChannel (broadcasting)MIMO

Abstract

fetched live from OpenAlex

In this letter, we enhance the performance of wireless communication systems by utilizing reconfigurable intelligent surface (RIS) and index modulation (IM). In particular, we introduce two schemes: RIS-assisted receive generalized space-shift keying (RIS-RGSSK) and RIS-assisted receive generalized spatial modulation (RIS-RGSM). In the RIS-RGSSK scheme, information bits are conveyed through selection of multiple receive antennas. The RIS-RGSM scheme takes RIS-RGSSK a step further by conveying information bits not only through the selection of multiple receive antennas, but also through embedding information bits in the phase of the received signals using M-ary phase shift keying (PSK) modulation. We also present simple yet efficient greedy detectors (GDs) for non-coherent detection of both schemes. Simulation results demonstrate the advantages of our proposed methods over existing schemes such as the RIS-assisted receive quadrature space-shift keying (RIS-RQSSK).

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)
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 score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.268
Teacher spread0.240 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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