Proactive Millimeter-wave Link Quality Prediction Utilizing Out-of-Band CSI Fingerprinting and Supervised Learning: An Experimental Study

This paper demonstrates the feasibility of millimeter-wave (mmWave) link quality prediction based on out-of-band channel state information (CSI) fingerprinting. To overcome the pedestrian blockage problem of mmWave communications, a large number of computer vision-aided mmWave link quality prediction methods have been investigated. However, the use of cameras and LiDAR to acquire computer vision information entails privacy risks. In this paper, we employ 5 GHz band CSI fingerprinting- an aggregation of CSI measured at multiple locations, for mmWave link quality prediction. CSI reflects the propagation environment of the wireless communication channel and thus includes information on pedestrians that block mmWave communications. CSI fingerprinting, aggregated from various measurement locations, enables future mmWave link quality prediction owing to its sufficient spatial information. We conducted a real-world wireless communication experiment with commercial devices compliant with IEEE 802.11ad for the mmWave, and nine IEEE 802.11ac CSI measurement devices for 5 GHz, to experimentally evaluate our method. The experimental result revealed that our proposed method can deterministically and numerically predict the mmWave link quality deterioration caused by pedestrian blockage 500 ms in advance.