Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach

TL;DR

This paper introduces a tensor decomposition-based method for classifying human activities using massive MIMO channel measurements, demonstrating improved accuracy over existing techniques in real-world scenarios.

Contribution

It presents a novel tensor decomposition approach combined with neural networks for activity classification using massive MIMO data, enhancing accuracy and robustness.

Findings

Significantly improved classification accuracy with massive MIMO over state-of-the-art methods.

Effective feature extraction exploiting correlations across time, frequency, and space.

Robust performance even with smaller datasets.

Abstract

Wireless-based activity sensing has gained significant attention due to its wide range of applications. We investigate radio-based multi-class classification of human activities using massive multiple-input multiple-output (MIMO) channel measurements in line-of-sight and non line-of-sight scenarios. We propose a tensor decomposition-based algorithm to extract features by exploiting the complex correlation characteristics across time, frequency, and space from channel tensors formed from the measurements, followed by a neural network that learns the relationship between the input features and output target labels. Through evaluations of real measurement data, it is demonstrated that the classification accuracy using a massive MIMO array achieves significantly better results compared to the state-of-the-art even for a smaller experimental data set.