| Presentation | 2018-05-25 [Technology Exhibit] Field Experiment of Localization Based on Machine Learning in LTE Environment Noboru Kanazawa, Atsushi Nagate, Atsushi Yamamoto, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In mobile communications networks, if we can estimate the location of each user equipment (UE) with high accuracy, efficient cell planning and network optimization become possible. However, it is difficult for operators to estimate the location of most commercial UEs because they cannot feedback their location information directly to their serving base stations. Radio Frequency (RF) fingerprint method is known as an effective localization method, with which we can estimate the location of UEs with only RF signature information by preparing a database with both RF signature and location information beforehand. In LTE, RF fingerprint database can be collected by conducting drive tests or using measurement data from UEs compatible with Minimization of Drive Test (MDT). Although the estimation accuracy can be better with more RF signatures, the increase in the amount of RF signatures causes the increase in the feedback, which consumes the uplink capacity and UE battery. Furthermore, keeping all RF signatures in database is inefficient because some RF signatures do not have effect on improving estimation accuracy. Hence, it is important to clarify the effect of each RF signature and use only effective ones in the localization. In this paper, we conducted a field experiment to create an RF fingerprint database in dense urban area, and evaluated the effectiveness of each RF signature by making several localization models based on machine learning. Eventually, we clarified minimum RF signatures required to operate RF fingerprint localization in LTE network. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Localization / Machine Learning / Field Experiment / Location Fingerprint |
| Paper # | SR2018-12 |
| Date of Issue | 2018-05-17 (SR) |
| Conference Information | |
| Committee | SR |
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| Conference Date | 2018/5/24(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo big sight |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Technical Exhibition, Machine Learning, AI |
| Chair | Kenta Umebayashi(Tokyo Univ. of Agric. and Tech.) |
| Vice Chair | Masayuki Ariyoshi(NEC) / Suguru Kameda(Tohoku Univ.) |
| Secretary | Masayuki Ariyoshi(NICT) / Suguru Kameda(ATR) |
| Assistant | Mamiko Inamori(Tokai Univ.) / Hiroyuki Shiba(NTT) / Gia Khanh Tran(Tokyo Inst. of Tech.) / Syusuke Narieda(NIT, Akashi College) |
| Paper Information | |
| Registration To | Technical Committee on Smart Radio |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Technology Exhibit] Field Experiment of Localization Based on Machine Learning in LTE Environment |
| Sub Title (in English) | |
| Keyword(1) | Localization |
| Keyword(2) | Machine Learning |
| Keyword(3) | Field Experiment |
| Keyword(4) | Location Fingerprint |
| 1st Author's Name | Noboru Kanazawa |
| 1st Author's Affiliation | SoftBank(SoftBank) |
| 2nd Author's Name | Atsushi Nagate |
| 2nd Author's Affiliation | SoftBank(SoftBank) |
| 3rd Author's Name | Atsushi Yamamoto |
| 3rd Author's Affiliation | SoftBank(SoftBank) |
| Date | 2018-05-25 |
| Paper # | SR2018-12 |
| Volume (vol) | vol.118 |
| Number (no) | SR-57 |
| Page | pp.pp.71-77(SR), |
| #Pages | 7 |
| Date of Issue | 2018-05-17 (SR) |