LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring

Cherif, Baya; Ghazzai, Hakim; Alsharoa, Ahmad

doi:10.1109/jsyst.2024.3425541

IEEE Systems Journal

2024

DOI: 10.1109/jsyst.2024.3425541

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LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring

Baya Cherif,

Hakim Ghazzai,

Ahmad Alsharoa

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2024

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Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming

Nazar,

Selim,

Qiao

2024

Sensors

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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.

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Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming

Nazar,

Selim,

Qiao

2024

Sensors

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LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring

Cited by 1 publication

References 47 publications

Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming

Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming

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