Sensing and Classification Using Massive MIMO : A Tensor Decomposition-Based Approach
(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.
(Less)
- author
- Manoj, B. R.
; Tian, Guoda
LU
; Gunnarsson, Sara
LU
; Tufvesson, Fredrik
LU
and Larsson, Erik G.
- organization
- publishing date
- 2021
- 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
- 2022-05-05 04:49:16
@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}}, }