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20212024
most citedDeep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements

6 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IT2024

WiDRa -- Enabling Millimeter-Level Differential Ranging Accuracy in Wi-Fi Using Carrier Phase

Vishnu V. Ratnam, Bilal Sadiq, Hao Chen +5

Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of m. F…

eess.SP2022

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

Shunyao Wu, Chaitali Chakrabarti, Ahmed Alkhateeb

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 chall…

eess.SP2021★ 2 cited

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

Shunyao Wu, Chaitali Chakrabarti, Ahmed Alkhateeb

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 t…

eess.SP2021★ 2 cited

Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration

Shunyao Wu, Muhammad Alrabeiah, Chaitali Chakrabarti +1

Overcoming the link blockage challenges is essential for enhancing the reliability and latency of millimeter wave (mmWave) and sub-terahertz (sub-THz) communication networks. Previ…

cs.LG2021★ 6 cited

Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements

Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak +2

Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by…