Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing
This addresses efficient MIMO communication in dynamic environments, offering an incremental improvement by integrating LIDAR and ray tracing to reduce overhead.
The paper tackled blockage detection and precoder design for MIMO links without communication overhead by using a physics-based GNN on LIDAR data for detection and ray tracing for channel estimation, achieving 95% accuracy in blockage detection and digital precoding at 90% of capacity.
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.