Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach
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.
Cite
@article{arxiv.2109.00821,
title = {Sensing and Classification Using Massive MIMO: A Tensor Decomposition-Based Approach},
author = {B. R. Manoj and Guoda Tian and Sara Gunnarsson and Fredrik Tufvesson and Erik G. Larsson},
journal= {arXiv preprint arXiv:2109.00821},
year = {2021}
}
Comments
accepted for publication in IEEE Wireless Communications Letters