Nazar Ahmad M, Selim Mohamed Y, Qiao Daji
Department of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USA.
Sensors (Basel). 2024 Dec 26;25(1):75. doi: 10.3390/s25010075.
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.
随着下一代网络技术的出现，可重构智能表面（RIS）面板已引起了广泛关注。本文提出了一种新颖的数据驱动方法，该方法利用光探测和测距（LiDAR）传感器来增强RIS辅助网络中的用户定位和波束成形。将LiDAR传感器集成到网络中将发挥重要作用，即使在低光照或恶劣天气条件下，也能提供高速且精确的3D映射功能。LiDAR数据有助于用户定位，从而能够确定最佳的RIS系数。我们的方法通过将LiDAR捕获的用户位置作为输入来扩展图神经网络（GNN）。这种扩展使GNN能够有效地学习从接收到的导频到最佳波束形成器以及反射系数的映射，以最大化多个用户之间RIS辅助的和速率。事实证明，GNN的排列等变和不变特性在有效处理LiDAR数据方面具有优势。我们的仿真结果表明，与传统方法相比，和速率有了显著提高。具体而言，包含位置的情况比起不包含位置的情况提高了25%，并且在不同的下行链路功率下，比起线性最小均方误差（LMMSE）信道估计性能提高了85%，在不同的导频长度下提高了98%，并且与不包括RIS的场景相比，在不同的下行链路功率下显著提高了190%。
