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Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
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.
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
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.
Blockage Prediction Using Wireless Signatures: Deep Learning Enables Real-World Demonstration
In this paper, we propose a novel solution that relies only on in-band mmWave wireless measurements to proactively predict future dynamic line-of-sight (LOS) link blockages.
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences.
