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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2021
- DOI:
- 10.48550/arXiv.2109.00821
- arXiv:
- arXiv:2109.00821
- Bibcode:
- 2021arXiv210900821M
- Keywords:
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- Computer Science - Information Theory;
- Electrical Engineering and Systems Science - Signal Processing
- E-Print:
- accepted for publication in IEEE Wireless Communications Letters