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In this letter, we address blockage detection and precoder design formultiple-input multiple-output (MIMO) links, without communication overheadrequired. Blockage detection is achieved by classifying light detection andranging (LIDAR) data through a physics-based graph neural network (GNN). Forprecoder design, a preliminary channel estimate is obtained by running raytracing on a 3D surface obtained from LIDAR data. This estimate is successivelyrefined and the precoder is designed accordingly. Numerical simulations showthat blockage detection is successful with 95% accuracy. Our digital precodingachieves 90% of the capacity and analog precoding outperforms previous worksexploiting LIDAR for precoder design.

Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing