TY - GEN
T1 - Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems
AU - Demirhan, Umut
AU - Alkhateeb, Ahmed
N1 - Funding Information:
VIII. ACKNOWLEDGEMENT This work is supported by the National Science Foundation under Grant No. 2048021.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 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 a solution with deep neural networks. 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). The evaluation results, based on this dataset, show that the proposed approaches can predicted 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.
AB - 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 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 a solution with deep neural networks. 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). The evaluation results, based on this dataset, show that the proposed approaches can predicted 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.
KW - 6G
KW - FMCW
KW - Radar
KW - blockage prediction
KW - machine learning
KW - mmWave
UR - http://www.scopus.com/inward/record.url?scp=85137271347&partnerID=8YFLogxK
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U2 - 10.1109/ICC45855.2022.9838438
DO - 10.1109/ICC45855.2022.9838438
M3 - Conference contribution
AN - SCOPUS:85137271347
T3 - IEEE International Conference on Communications
SP - 4547
EP - 4552
BT - ICC 2022 - IEEE International Conference on Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Communications, ICC 2022
Y2 - 16 May 2022 through 20 May 2022
ER -