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Numrering	Referens	Omslagsbild	Hitta
1.	Manoj, B. R., et al. (författare) MOVING OBJECT CLASSIFICATION WITH A SUB-6 GHZ MASSIVE MIMO ARRAY USING REAL DATA 2021 Ingår i: 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021). - : IEEE. - 1520-6149 .- 2379-190X. - 9781728176055 - 9781728176062 ; , s. 8133-8137 Konferensbidrag (refereegranskat)abstract Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

Manoj, B. R., et al. (författare)
MOVING OBJECT CLASSIFICATION WITH A SUB-6 GHZ MASSIVE MIMO ARRAY USING REAL DATA
2021
Ingår i: 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021). - : IEEE. - 1520-6149 .- 2379-190X. - 9781728176055 - 9781728176062 ; , s. 8133-8137
Konferensbidrag (refereegranskat)abstract
- Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices.

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