Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data

TL;DR

This study demonstrates that machine learning applied to real data from a massive MIMO system at 3.7 GHz can classify indoor moving objects with up to 98% accuracy, outperforming limited antenna setups.

Contribution

It introduces algorithms leveraging amplitude and phase features for activity classification using real massive MIMO data in indoor environments.

Findings

Achieved up to 98% classification accuracy.

Massive MIMO outperforms limited antenna systems.

Effective with a small number of experiments.

Abstract

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.