TY - GEN
T1 - LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
AU - Wu, Shunyao
AU - Chakrabarti, Chaitali
AU - Alkhateeb, Ahmed
N1 - Funding Information:
This work is supported in part by the National Science Foundation under Grant No. 2048021.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they happen. This allows the network to make proactive decisions for hand-off/beam switching which enhances its reliability and latency. We formulate the LiDAR-aided blockage prediction problem and present the first real-world demonstration for LiDAR-aided blockage prediction in mmWave systems. In particular, we construct a large-scale real-world dataset, based on the DeepSense 6G structure, that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising (static cluster removal) algorithm and a machine learning model that proactively predicts dynamic link blockages. Based on the real-world dataset, our LiDAR-aided approach is shown to achieve 95% accuracy in predicting blockages happening within 100ms and more than 80% prediction accuracy for blockages happening within one second. If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.
AB - Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide awareness about the communication environment and proactively predict dynamic link blockages before they happen. This allows the network to make proactive decisions for hand-off/beam switching which enhances its reliability and latency. We formulate the LiDAR-aided blockage prediction problem and present the first real-world demonstration for LiDAR-aided blockage prediction in mmWave systems. In particular, we construct a large-scale real-world dataset, based on the DeepSense 6G structure, that comprises co-existing LiDAR and mmWave communication measurements in outdoor vehicular scenarios. Then, we develop an efficient LiDAR data denoising (static cluster removal) algorithm and a machine learning model that proactively predicts dynamic link blockages. Based on the real-world dataset, our LiDAR-aided approach is shown to achieve 95% accuracy in predicting blockages happening within 100ms and more than 80% prediction accuracy for blockages happening within one second. If used for proactive hand-off, the proposed solutions can potentially provide an order of magnitude saving in the network latency, which highlights a promising direction for addressing the blockage challenges in mmWave/sub-THz networks.
KW - LiDAR
KW - Millimeter wave
KW - blockage prediction
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U2 - 10.1109/WCNC51071.2022.9771651
DO - 10.1109/WCNC51071.2022.9771651
M3 - Conference contribution
AN - SCOPUS:85130689929
T3 - IEEE Wireless Communications and Networking Conference, WCNC
SP - 2631
EP - 2636
BT - 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022
Y2 - 10 April 2022 through 13 April 2022
ER -