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Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS.

Ural Mutlu1, Yasin Kabalci2

  • 1Bor Vocational School, Nigde Ömer Halisdemir University, Nigde 51700, Turkey.

Sensors (Basel, Switzerland)
|July 12, 2025
PubMed
Summary

This study introduces a hybrid framework for Reconfigurable Intelligent Surfaces (RIS) 6G wireless systems, enhancing channel estimation and mobile station localization using sparse Bayesian learning for improved accuracy in non-line-of-sight conditions.

Keywords:
Off-Grid Sparse Bayesian LearningOrthogonal Matching Pursuitcompressed sensing: angle estimationreconfigurable intelligent surface

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Area of Science:

  • Wireless communication systems
  • Signal processing
  • Electromagnetics

Background:

  • Reconfigurable Intelligent Surfaces (RIS) are crucial for 6G wireless systems, but their passive nature and non-line-of-sight (NLoS) challenges complicate uplink channel estimation and localization.
  • Dense urban environments exacerbate these challenges due to signal blockage and multipath propagation.

Purpose of the Study:

  • To develop a robust hybrid channel parameter estimation framework for Reconfigurable Intelligent Surfaces (RIS)-assisted 6G Multiple-Input Multiple-Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems.
  • To address the challenges of channel estimation and mobile station (MS) localization in NLoS urban environments.

Main Methods:

  • Modeling the RIS-MS channel using a double-sparse angular structure.
  • Implementing a hybrid framework combining Simultaneous Orthogonal Matching Pursuit (SOMP) for coarse support estimation and Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) for refinement.
  • Utilizing an Angle of Arrival (AoA)-Angle of Departure (AoD) matching algorithm and a 3D localization procedure based on NLoS multipath geometry.

Main Results:

  • The proposed framework achieves high angular resolution and precise localization accuracy, with 97% of results within 0.01 m.
  • Demonstrates a low channel estimation error of 0.0046% at a 40 dB signal-to-noise ratio (SNR).
  • Effectively handles NLoS conditions in urban environments.

Conclusions:

  • The hybrid framework significantly enhances channel estimation and localization performance in RIS-assisted 6G MIMO-OFDM systems.
  • The VBEM-based OG-SBL refinement and AoA-AoD matching are effective for accurate parameter estimation and 3D localization.
  • This approach offers a promising solution for reliable wireless communication in challenging NLoS scenarios.