LiDAR Aided Human Blockage Prediction for 6G

TL;DR

This paper presents a LiDAR-based system utilizing deep learning to predict human-induced blockages in 6G wireless links, aiming to improve link reliability by anticipating obstacles before they occur.

Contribution

It introduces an end-to-end infrastructure-mounted LiDAR system combined with deep learning for real-time human blockage prediction in 6G networks, a novel approach in this context.

Findings

Achieves 87% accuracy in predicting upcoming blockages

Maintains 78% precision and 79% recall within 300 ms window

Demonstrates effectiveness of LiDAR and deep learning for proactive link management

Abstract

Leveraging higher frequencies up to THz band paves the way towards a faster network in the next generation of wireless communications. However, such shorter wavelengths are susceptible to higher scattering and path loss forcing the link to depend predominantly on the line-of-sight (LOS) path. Dynamic movement of humans has been identified as a major source of blockages to such LOS links. In this work, we aim to overcome this challenge by predicting human blockages to the LOS link enabling the transmitter to anticipate the blockage and act intelligently. We propose an end-to-end system of infrastructure-mounted LiDAR sensors to capture the dynamics of the communication environment visually, process the data with deep learning and ray casting techniques to predict future blockages. Experiments indicate that the system achieves an accuracy of 87% predicting the upcoming blockages while maintaining a precision of 78% and a recall of 79% for a window of 300 ms.