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

Wu, Shunyao; Chakrabarti, Chaitali; Alkhateeb, Ahmed

doi:10.1109/wcnc51071.2022.9771651

Cited by 26 publications

(15 citation statements)

References 12 publications

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“…Given the complex nature of these pre-blockage signatures, we leverage ML (and in particular deep learning models) to utilize them as in [1], [4], [5], [12]- [14], [30]. While the proposed baseline LiDAR methods work well for simple scenarios, complex scenarios such as blockage severity level prediction benefit from more advanced ML-based solutions.…”

Section: ML Based Methodsmentioning

confidence: 99%

“…LiDAR has also been used to aid other communication tasks such as beam prediction/tracking [17]- [19]. Motivated by the initial results in [1], this paper extends this work and explores the potential of utilizing both LiDAR and in-band mmWave measurements to predict the occurrence, severity, and direction of dynamic link blockages.…”

Section: A Related Workmentioning

confidence: 95%

“…This prompted the research for using other data modalities for future blockage prediction. With this objective, in [1], we proposed for the first time to use LiDAR sensory data for proactively predicting link blockages. Since LiDAR provides high resolution for the objects and it won't be affected by daylight compared to the camera.…”

Section: A Related Workmentioning

confidence: 99%

“…Problem 3 focuses on proactively identifying the type of the incoming blockage from the perspective of its severity. We define blockage-severity index as the average blockage time interval measured in a number of instances (or blocks τ B ) for each object 1 . We classify objects based on this index.…”

Section: Problem Formulationmentioning

confidence: 99%

“…To proactively predict the blockage for longer time intervals, additional data modality such as LiDAR is needed. In [1], we showed that LiDAR data can help achieve high accuracy for longer prediction intervals. In this paper, we investigate the potential of leveraging LiDAR and wireless mmWave/sub-THz signatures to proactively predict future link blockages.…”

Section: Introductionmentioning

confidence: 99%

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Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

Chakrabarti

Alkhateeb

2023

IEEE Open J. Commun. Soc.

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Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this challenge, this paper leverages mmWave and LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they occur. This allows the network to make proactive decisions for hand-off/beam switching, enhancing the network reliability and latency. More specifically, this paper addresses the following key questions: (i) Can we predict a line-of-sight link blockage, before it happens, using in-band mmWave/THz signal and LiDAR sensing data? (ii) Can we also predict when this blockage will occur? (iii) Can we predict the blockage duration? And (iv) can we predict the direction of the moving blockage? For that, we develop machine learning solutions that learn special patterns of the received signal and sensory data, which we call pre-blockage signatures, to infer future blockages. To evaluate the proposed approaches, we build a large-scale real-world dataset that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising algorithm that applies some pre-processing to the LiDAR data. Based on the real-world dataset, the developed approaches are shown to achieve above 95% accuracy in predicting blockages occurring within 100 ms and more than 80% prediction accuracy for blockages occurring within one second. Given this future blockage prediction capability, the paper also shows that the developed solutions can achieve an order of magnitude saving in network latency, which further highlights the potential of the developed blockage prediction solutions for wireless networks.

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Section: ML Based Methodsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 95%

Section: A Related Workmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

Chakrabarti

Alkhateeb

2023

IEEE Open J. Commun. Soc.

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6G: Vision, Applications, and Challenges

da Costa,

Zhao,

Chafii

et al. 2023

Signals and Communication Technology

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Adaptive Smart Radio Environment (ASRE): New Paradigm for Wireless Communication Networks

Luo,

Yan,

Xuan

et al. 2024

IEEE Access

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ABSRACT Recent efforts have promoted Smart Radio Environment (SRE) to enhance the reception quality in high-frequency bands via reconfigurable intelligent surfaces (RISs) for supporting hyperconnectivity of Beyond 5G/6G Era. The functionality of SRE is based on the more degrees of freedom that come from electronically controlling the environment itself rather than transmitter and receiver. Accordingly, the spectral efficiency and the sum rate throughput can be enhanced by applying customized transformations to the electromagnetic radio waves. However, futuristic applications that are envisioned to be time-critical demand much more than what SRE can handle because that too much time is spent on sensing the environment and applying customized transformations.In this work, we propose a novel concept of adaptive SRE (ASRE), which intends to start from the semantic perception of a wireless environment to explore the learning and evolutionary mechanisms of SRE through the loop of recognition, adaptation, and proaction. In combination with the techniques known in artificial intelligence (AI), such as deep reinforcement learning, knowledge graph, etc., this technology can provide enhanced ultra-reliable lowlatency communication (URLLC) services. Furthermore, the wireless environment is expected to become not just adaptive, but partially intelligent by predicting and utilizing the changes in the communication environment. To corroborate the rationality and superiority of ASRE, we also present simulation results related to the typical dynamic NLOS scenario. Finally, we highlight numerous open challenges and research directions.

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

Cited by 26 publications

References 12 publications

Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures

6G: Vision, Applications, and Challenges

Adaptive Smart Radio Environment (ASRE): New Paradigm for Wireless Communication Networks

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