RIS‐Aided MISO Channel Estimation Using Fuzzy Embedded Recurrent Neural Network and Binary Kepler Optimization Algorithm
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Le résumé fourni par la source
ABSTRACT Multi‐antenna wireless systems enhanced by reconfigurable intelligent surfaces (RISs) offer improved spectral and energy efficiency. RIS improves coverage and energy efficiency, but accurate channel estimation is challenging. The least‐squares (LS) strategy is sub‐optimal, while the MMSE estimator is difficult due to nonlinearity and non‐Gaussianity. To overcome these issues, RIS‐Aided MISO Channel Estimation using Fuzzy Embedded Recurrent Neural Network and Binary Kepler Optimization Algorithm (RIS‐MISO‐ CE ‐FERNN‐BKOA) is proposed. Initially, the Linear Minimum Mean Square Error (LMMSE) estimator, optimized with BKOA for RIS phase shifts, achieved higher accuracy than the LS approach. To further enhance the efficiency and better approximate the globally optimal MMSE channel estimator, Fuzzy Embedded Recurrent Neural Network (FERNN) is proposed. The RIS‐MISO‐ CE ‐FERNN‐BKOA method attain 34.56%, 25.63%, and 18.89% higher accuracy; 28.63%, 25.41%, and 19.23% lower MMSE; and 33.56%, 29.78%, and 25.74% higher SNR when analyzed with the existing techniques. The proposed technique achieves better accuracy when compared with the conventional models, making it a robust solution for RIS‐assisted MISO communication systems.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- RIS‐Aided MISO Channel Estimation Using Fuzzy Embedded Recurrent Neural Network and Binary Kepler Optimization Algorithm
- Date Crossref
- 13/04/2025
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Sathyabama Institute of Science and Technology Department of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
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Saveetha University pays non établi dans la noticeUniversité ou école supérieure
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Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India pays non établi dans la noticeUniversité ou école supérieure
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Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India pays non établi dans la noticeUniversité ou école supérieure
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Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Gandipet Hyderabad India pays non établi dans la noticeStructure de recherche
Department of Computer Science and Engineering — Sathyabama Institute of Science and Technology, Saveetha University et Department of Electronics and Communication Engineering Annapoorana Engineering College Salem Tamil Nadu India, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.