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Sensing and Classif...
Sensing and Classification Using Massive MIMO : A Tensor Decomposition-Based Approach
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- Manoj, Banugondi Rajashekara (författare)
- Linköpings universitet,Linköping University,Kommunikationssystem,Tekniska fakulteten
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- Tian, Guoda (författare)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups,Lund Univ, Sweden
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- Gunnarsson, Sara (författare)
- Lund University,Lunds universitet,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Communications Engineering,Lund University Research Groups,Lund Univ, Sweden
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- Tufvesson, Fredrik (författare)
- Lund University,Lunds universitet,Institutionen för elektro- och informationsteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Kommunikationsteknologi,Forskargrupper vid Lunds universitet,Department of Electrical and Information Technology,Departments at LTH,Faculty of Engineering, LTH,Communications Engineering,Lund University Research Groups,Lund Univ, Sweden
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- Larsson, Erik G., 1974- (författare)
- Linköpings universitet,Linköping University,Kommunikationssystem,Tekniska fakulteten
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(creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021
- 2021
- Engelska.
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Ingår i: IEEE Wireless Communications Letters. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2162-2337 .- 2162-2345. ; 10:12, s. 2649-2653
- Relaterad länk:
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https://liu.diva-por... (primary) (Raw object)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Nyckelord
- Activity classification
- Antenna measurements
- Correlation
- Feature extraction
- large-scale sensing
- massive MIMO
- neural network
- Radio frequency
- Sensors
- tensor decomposition.
- Tensors
- Time measurement
- Tensors; Feature extraction; Sensors; Correlation; Time measurement; Radio frequency; Antenna measurements; Activity classification; large-scale sensing; massive MIMO; neural network; tensor decomposition
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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