Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing
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.
Keywords
Cite
@article{arxiv.2209.07350,
title = {Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing},
author = {Matteo Nerini and Bruno Clerckx},
journal= {arXiv preprint arXiv:2209.07350},
year = {2023}
}
Comments
Accepted by IEEE for publication