Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems

TL;DR

This paper introduces radar-based methods to proactively predict blockages in millimeter wave communication systems, significantly improving reliability by forecasting obstructions up to one second in advance using real-world data.

Contribution

It develops two novel solutions leveraging radar data and deep learning for proactive blockage prediction, validated on a large-scale real-world dataset.

Findings

Achieves over 90% F1 score in predicting blockages 1 second ahead.

Demonstrates the effectiveness of radar sensors for environmental sensing in mmWave systems.

Provides a large real-world dataset for future research in blockage prediction.

Abstract

Millimeter wave (mmWave) and sub-terahertz communication systems rely mainly on line-of-sight (LOS) links between the transmitters and receivers. The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks. In this paper, we propose to utilize radar sensors to provide sensing information about the surrounding environment and moving objects, and leverage this information to proactively predict future link blockages before they happen. This is motivated by the low cost of the radar sensors, their ability to efficiently capture important features such as the range, angle, velocity of the moving scatterers (candidate blockages), and their capability to capture radar frames at relatively high speed. We formulate the radar-aided proactive blockage prediction problem and develop two solutions for this problem based on classical radar object tracking and deep neural networks. The two solutions are designed to leverage domain knowledge and the understanding of the blockage prediction problem. To accurately evaluate the proposed solutions, we build a large-scale real-world dataset, based on the DeepSense framework, gathering co-existing radar and mmWave communication measurements of more than $10$ thousand data points and various blockage objects (vehicles, bikes, humans, etc.). The evaluation results, based on this dataset, show that the proposed approaches can predict future blockages $1$ second before they happen with more than $90\%$ $F_1$ score (and more than $90\%$ accuracy). These results, among others, highlight a promising solution for blockage prediction and reliability enhancement in future wireless mmWave and terahertz communication systems.