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：本文探讨了多输入多输出（MIMO）链路中无需通信开销的阻塞检测与预编码器设计问题。阻塞检测通过基于物理的图神经网络（GNN）对激光雷达（LIDAR）数据进行分类实现。在预编码器设计方面，首先对从LIDAR数据获取的三维表面进行射线追踪，获得初步信道估计，随后对该估计进行逐步优化，并据此设计预编码器。数值仿真表明，阻塞检测准确率达到95%。所提出的数字预编码方案可达到90%的信道容量，而模拟预编码性能优于此前利用LIDAR进行预编码器设计的同类工作。