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.

翻译：在这封信中,我们处理多投入多输出(MIMO)链接的阻隔探测和预编码设计,不需要通信管理费;通过基于物理的图形神经网络(GNN)对光探测和测距(LIDAR)数据进行分类,从而实现阻隔探测;在预编码设计方面,通过对从LIDAR数据中获得的3D表面进行射线追踪,获得初步通道估计;这一估计是连续完善的,并相应设计前编码。数字模拟显示,阻隔探测以95%的精确度成功。我们的数字预编码实现了90%的容量和模拟预编码超过先前利用LIDAR进行预编码设计的工作。