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

Manoj, B. R. ; Tian, Guoda LU ; Gunnarsson, Sara LU ; Tufvesson, Fredrik LU orcid and Larsson, Erik G. (2021) In IEEE Wireless Communications Letters 10(12). p.2649-2653
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... (More)

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.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Activity classification, Antenna measurements, Correlation, Feature extraction, large-scale sensing, massive MIMO, neural network, Radio frequency, Sensors, tensor decomposition., Tensors, Time measurement
in
IEEE Wireless Communications Letters
volume
10
issue
12
pages
2649 - 2653
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85114733008
ISSN
2162-2337
DOI
10.1109/LWC.2021.3110463
language
English
LU publication?
yes
additional info
Publisher Copyright: IEEE
id
79038056-a966-4bb4-9ebf-d79d7ba6bf4c
date added to LUP
2021-10-12 15:23:37
date last changed
2025-10-14 10:36:34
@article{79038056-a966-4bb4-9ebf-d79d7ba6bf4c,
  abstract     = {{<p>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.</p>}},
  author       = {{Manoj, B. R. and Tian, Guoda and Gunnarsson, Sara and Tufvesson, Fredrik and Larsson, Erik G.}},
  issn         = {{2162-2337}},
  keywords     = {{Activity classification; Antenna measurements; Correlation; Feature extraction; large-scale sensing; massive MIMO; neural network; Radio frequency; Sensors; tensor decomposition.; Tensors; Time measurement}},
  language     = {{eng}},
  number       = {{12}},
  pages        = {{2649--2653}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Wireless Communications Letters}},
  title        = {{Sensing and Classification Using Massive MIMO : A Tensor Decomposition-Based Approach}},
  url          = {{http://dx.doi.org/10.1109/LWC.2021.3110463}},
  doi          = {{10.1109/LWC.2021.3110463}},
  volume       = {{10}},
  year         = {{2021}},
}