Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
This addresses reliability and latency issues in 5G and beyond wireless networks for users and operators, though it is incremental as it applies existing ML methods to a new domain-specific dataset.
The paper tackles the problem of sudden disconnections in millimeter wave (mmWave) communication due to blockages by using machine learning to predict dynamic blockages proactively, achieving over 85% accuracy in predicting blockage occurrence with low error in timing.
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 the sensitivity of mmWave signals to blockages; a sudden blockage in the line of sight (LOS) link leads to abrupt disconnection, which affects the reliability of the network. In addition, searching for an alternative base station to re-establish the link could result in needless latency overhead. In this paper, we address these challenges collectively by utilizing machine learning to anticipate dynamic blockages proactively. The proposed approach sees a machine learning algorithm learning to predict future blockages by observing what we refer to as the pre-blockage signature. To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences. Simulation results on a real dataset show that blockage occurrence could be predicted with more than 85% accuracy and the exact time instance of blockage occurrence can be obtained with low error. This highlights the potential of the proposed solution for dynamic blockage prediction and proactive hand-off, which enhances the reliability and latency of future wireless networks.