Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming
Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/75 |
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| author | Ahmad M. Nazar Mohamed Y. Selim Daji Qiao |
| author_facet | Ahmad M. Nazar Mohamed Y. Selim Daji Qiao |
| author_sort | Ahmad M. Nazar |
| collection | DOAJ |
| description | Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS. |
| format | Article |
| id | doaj-art-8d65ce8ac11647cdaa7d66d98fcfa753 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-8d65ce8ac11647cdaa7d66d98fcfa7532025-01-10T13:20:47ZengMDPI AGSensors1424-82202024-12-012517510.3390/s25010075Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS BeamformingAhmad M. Nazar0Mohamed Y. Selim1Daji Qiao2Department of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USADepartment of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USADepartment of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USAReconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions. LiDAR data facilitate user localization, enabling the determination of optimal RIS coefficients. Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. This extension enables the GNN to effectively learn the mapping from received pilots to optimal beamformers and reflection coefficients to maximize the RIS-assisted sumrate among multiple users. The permutation-equivariant and -invariant properties of the GNN proved advantageous in efficiently handling the LiDAR data. Our simulation results demonstrated significant improvements in sum rates compared with conventional methods. Specifically, including locations improved on excluding locations by up to 25% and outperformed the Linear Minimum Mean Squared Error (LMMSE) channel estimation by up to 85% with varying downlink power and 98% with varying pilot lengths, and showed a remarkable 190% increase with varying downlink power compared with scenarios excluding the RIS.https://www.mdpi.com/1424-8220/25/1/75LiDARbeamformingRISGNNdata-driven |
| spellingShingle | Ahmad M. Nazar Mohamed Y. Selim Daji Qiao Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming Sensors LiDAR beamforming RIS GNN data-driven |
| title | Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming |
| title_full | Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming |
| title_fullStr | Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming |
| title_full_unstemmed | Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming |
| title_short | Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming |
| title_sort | revolutionizing ris networks lidar based data driven approach to enhance ris beamforming |
| topic | LiDAR beamforming RIS GNN data-driven |
| url | https://www.mdpi.com/1424-8220/25/1/75 |
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