Enabling IoT self-localization using ambient 5G mmWave signals

Abstract

The small cell size, wide bandwidth, and MIMO antenna arrays in 5G mmWave networks provide great opportunities for IoT localization. However, low-power and low-cost IoT devices are incapable of leveraging these benefits. We present mm-ISLA: a system that enables IoT nodes to localize themselves using ambient 5G mmWave signals without any coordination with the base stations. mm-ISLA leverages MEMS Spike-Train filters to access the wideband 5G signals and estimates the Angle of Departure from the base station MIMO antenna arrays to accurately localize the IoT nodes.