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

• URL: http://arxiv.org/abs/2209.07350v2
• Date: Mon, 22 May 2023 13:32:54 GMT
• Title: Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing
• Authors: Matteo Nerini, Bruno Clerckx
• Abstract summary: 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. Numerical simulations show that blockage detection is successful with 95% accuracy.
• Score: 58.73924499067486
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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