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Performance Analysis of RIS-Assisted Receive Generalized Space Shift Keying and Generalized Spatial Modulation

2025· article· W4417282834 on OpenAlexaff
Porfirio A. Marín, Muhammad Hanif, Ebrahim Bedeer

Bibliographic record

Venuenot available
Typearticle
Language
FieldEngineering
TopicAdvanced Wireless Communication Technologies
Canadian institutionsThompson Rivers UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsModulation (music)KeyingSpatial modulationAmplitude and phase-shift keyingPairwise error probabilityOn-off keyingSpace (punctuation)Phase-shift keying

Abstract

fetched live from OpenAlex

This paper provides a comprehensive performance analysis of the reconfigurable intelligent surfaces (RIS)-assisted receive generalized space-shift keying (RGSSK) and the RIS-assisted receive generalized spatial modulation (RIS-RGSM) schemes. Specifically, we derive closed-form expressions for the pairwise error probabilities (PEPs) of the RIS-RGSSK and RIS-RGSM schemes when two antennas are activated at the receiver. Our analytical derivations eliminate the need for numerical approximations or complex integration methods. Finally, we verify our analytical results via simulations, demonstrating the accuracy of our expressions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.263
Teacher spread0.246 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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