论文标题
无额外的阻塞检测和通过基于物理的图形神经网络进行预编码：LIDAR数据符合射线跟踪
Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing
论文作者
论文摘要
在这封信中，我们解决了多输入多输出（MIMO）链接的阻塞检测和预编码器设计，而无需通信开销。通过基于物理学的图神经网络（GNN）对光检测和范围（LIDAR）数据进行分类来实现阻塞检测。对于预编码器设计，通过在从LiDAR数据获得的3D表面上运行射线跟踪来获得初步的通道估计。该估计值已连续完善，并且对预编码器进行了相应的设计。数值模拟表明，封锁检测成功率为95％。我们的数字编码实现了90％的容量和模拟预编码的表现优于先前的工作，从而利用LiDAR进行预编码器设计。
In this letter, we address blockage detection and precoder design for multiple-input multiple-output (MIMO) links, without communication overhead required. Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN). For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. This estimate is successively refined and the precoder is designed accordingly. Numerical simulations show that blockage detection is successful with 95% accuracy. Our digital precoding achieves 90% of the capacity and analog precoding outperforms previous works exploiting LIDAR for precoder design.
