LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems

Wu, Shiqian; Chakrabarti, Chaitali; Alkhateeb, Ahmed

doi:10.48550/arxiv.2111.09581

Cited by 2 publications

(2 citation statements)

References 11 publications

(22 reference statements)

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“…These solutions, however, are mainly capable of predicting immediate blockages and are hard to scale to complex/crowded scenarios. To enable predicting blockages early enough before they block the links, solutions based on radar and LiDAR sensory data were proposed for the first time in [8], [9]. Despite their promising results, easy sensing modality has its advantages and drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

Charan¹,

Alkhateeb²

2022

Preprint

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This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen. Proactively predicting line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make proactive network management decisions, such as proactive beam switching and hand-off) before a link failure happens. This can significantly enhance the network reliability and latency while efficiently utilizing the wireless resources. To evaluate this gain in reality, this paper (i) develops a computer vision based solution that processes the visual data captured by a camera installed at the infrastructure node and (ii) studies the feasibility of the proposed solution based on the large-scale real-world dataset, DeepSense 6G, that comprises multi-modal sensing and communication data. Based on the adopted real-world dataset, the developed solution achieves ≈ 90% accuracy in predicting blockages happening within the future 0.1s and ≈ 80% for blockages happening within 1s, which highlights a promising solution for mmWave/sub-THz communication networks.

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Section: Introductionmentioning

confidence: 99%

Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

Charan¹,

Alkhateeb²

2022

Preprint

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“…While cameras are relatively simple to deploy [7], their usage is sometimes associated with privacy concerns and its operation may be limited scenarios with low light or bad weather conditions. Another promising approach is to use LiDAR sensory data, as proposed in [8]. Using LiDAR, however, is mainly suitable for low-range scenarios and is associated with relatively high cost.…”

Section: Introductionmentioning

confidence: 99%

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

Demirhan¹,

Alkhateeb²

2021

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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 obtain 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% F1 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.

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LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems

Cited by 2 publications

References 11 publications

Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

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

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