Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS
Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path...
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MDPI AG
2025-07-01
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| author | Ural Mutlu Yasin Kabalci |
| author_facet | Ural Mutlu Yasin Kabalci |
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| description | Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-172ef438436240618db82d4c4909cf2b2025-08-20T03:16:56ZengMDPI AGSensors1424-82202025-07-012513414010.3390/s25134140Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoSUral Mutlu0Yasin Kabalci1Bor Vocational School, Nigde Ömer Halisdemir University, Nigde 51700, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Engineering, Nigde Ömer Halisdemir University, Nigde 51240, TurkeyReconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR).https://www.mdpi.com/1424-8220/25/13/4140reconfigurable intelligent surfaceOff-Grid Sparse Bayesian Learningcompressed sensing: angle estimationOrthogonal Matching Pursuit |
| spellingShingle | Ural Mutlu Yasin Kabalci Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS Sensors reconfigurable intelligent surface Off-Grid Sparse Bayesian Learning compressed sensing: angle estimation Orthogonal Matching Pursuit |
| title | Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS |
| title_full | Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS |
| title_fullStr | Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS |
| title_full_unstemmed | Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS |
| title_short | Off-Grid Sparse Bayesian Learning for Channel Estimation and Localization in RIS-Assisted MIMO-OFDM Under NLoS |
| title_sort | off grid sparse bayesian learning for channel estimation and localization in ris assisted mimo ofdm under nlos |
| topic | reconfigurable intelligent surface Off-Grid Sparse Bayesian Learning compressed sensing: angle estimation Orthogonal Matching Pursuit |
| url | https://www.mdpi.com/1424-8220/25/13/4140 |
| work_keys_str_mv | AT uralmutlu offgridsparsebayesianlearningforchannelestimationandlocalizationinrisassistedmimoofdmundernlos AT yasinkabalci offgridsparsebayesianlearningforchannelestimationandlocalizationinrisassistedmimoofdmundernlos |