| Item type |
SIG Technical Reports(1) |
| 公開日 |
2020-02-24 |
| タイトル |
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タイトル |
Preliminary Investigation of Machine Learning-based Subcarrier Selection for AoA Estimation Using Wi-Fi CSI |
| タイトル |
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言語 |
en |
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タイトル |
Preliminary Investigation of Machine Learning-based Subcarrier Selection for AoA Estimation Using Wi-Fi CSI |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属 |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属 |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属 |
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Communication Science Laboratories, NTT |
| 著者所属 |
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Access Network Service Systems Laboratories, NTT |
| 著者所属 |
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Access Network Service Systems Laboratories, NTT |
| 著者所属(英) |
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en |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属(英) |
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en |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属(英) |
|
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en |
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Graduate School of Information Science and Technologies, Osaka University |
| 著者所属(英) |
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en |
|
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Communication Science Laboratories, NTT |
| 著者所属(英) |
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|
en |
|
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Access Network Service Systems Laboratories, NTT |
| 著者所属(英) |
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en |
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Access Network Service Systems Laboratories, NTT |
| 著者名 |
Zesheng, Cai
Takuya, Maekawa
Takahiro, Hara
Kazuya, Ohara
Tomoki, Murakami
Hirantha, Abeysekera
|
| 著者名(英) |
Zesheng, Cai
Takuya, Maekawa
Takahiro, Hara
Kazuya, Ohara
Tomoki, Murakami
Hirantha, Abeysekera
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
With the rapid development of wireless sensing technologies, context awareness based on Wi-Fi channel state information (CSI) has been actively studied. Specifically, methods for estimating the angle of arrival (AoA) extracted from CSI attract researchers' attention for its ability to reveal the signal paths from a transmitter to receiver. However, different subcarriers in CSI have different sensitivities and thus it is essential to exploit suitable subcarriers for AoA estimation. Traditional approaches are limited to the number of CSI packets and specialized capture devices, decreasing the opportunity for deployment. In this work, we propose to select the proper subcarriers even with one single packet for AoA estimation by utilizing machine learning without specialized devices. Our system exploits the fine-grained compressed channel state information with common IEEE 802.11ac devices and thus has the potential to be widely deployed. Instead of selecting antenna pairs from CSI measurements, which has limited combinations due to the number of receiver antennas, the developed algorithm studies the properties of subcarriers within all antenna pairs and tries to find the subcarriers with small AoA errors by using classification and regression method, e.g., Random Forest and Support Vector Regression (SVR) in our system. Our extensive experiments demonstrate that our system can accurately select the proper subcarriers with high environmental robustness for AoA estimation using Wi-Fi CSI. |
| 論文抄録(英) |
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内容記述タイプ |
Other |
|
内容記述 |
With the rapid development of wireless sensing technologies, context awareness based on Wi-Fi channel state information (CSI) has been actively studied. Specifically, methods for estimating the angle of arrival (AoA) extracted from CSI attract researchers' attention for its ability to reveal the signal paths from a transmitter to receiver. However, different subcarriers in CSI have different sensitivities and thus it is essential to exploit suitable subcarriers for AoA estimation. Traditional approaches are limited to the number of CSI packets and specialized capture devices, decreasing the opportunity for deployment. In this work, we propose to select the proper subcarriers even with one single packet for AoA estimation by utilizing machine learning without specialized devices. Our system exploits the fine-grained compressed channel state information with common IEEE 802.11ac devices and thus has the potential to be widely deployed. Instead of selecting antenna pairs from CSI measurements, which has limited combinations due to the number of receiver antennas, the developed algorithm studies the properties of subcarriers within all antenna pairs and tries to find the subcarriers with small AoA errors by using classification and regression method, e.g., Random Forest and Support Vector Regression (SVR) in our system. Our extensive experiments demonstrate that our system can accurately select the proper subcarriers with high environmental robustness for AoA estimation using Wi-Fi CSI. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AA11851388 |
| 書誌情報 |
研究報告モバイルコンピューティングとパーベイシブシステム（MBL）
巻 2020-MBL-94,
号 55,
p. 1-8,
発行日 2020-02-24
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8817 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-MBL20094055.pdf (1.9 MB)
