Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

Understand

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks.

• Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs.
• We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead.
• The key idea is to leverage the camera imagery and machine learning (ML).