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

TL;DR

This paper introduces a physics-based graph neural network approach for overhead-free blockage detection and precoder design in MIMO systems using LIDAR data and ray tracing, achieving high accuracy and capacity.

Contribution

It presents a novel physics-based GNN method for blockage detection and precoder design that eliminates communication overhead and leverages LIDAR data with ray tracing.

Findings

Blockage detection accuracy of 95%.

Digital precoding achieves 90% of the channel capacity.

Analog precoding outperforms previous LIDAR-based methods.

Abstract

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.