| Presentation | 2019-08-23 Wireless Network Control Enabled by Data Assessment Using Machine Learning Ryoichi Shinkuma, Takayuki Nishio, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The real-time prediction of spatial information is promising for next-generation mobile networks. Recent developments in machine learning technology have enabled prediction of spatial information, which will be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Other key enablers for forming spatial information are image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. This talk presents a framework of data assessment and prioritization that reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In the framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. A numerical evaluation using an actual vehicle mobility dataset demonstrated the validity of the framework. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | machine learning / data importance / spatial information / real-time prediction / wireless network control |
| Paper # | RCS2019-166 |
| Date of Issue | 2019-08-15 (RCS) |
| Conference Information | |
| Committee | RCS / SAT |
|---|---|
| Conference Date | 2019/8/22(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Satellite Communications, Broadcasting, Forward Error Correction, Wireless Communications, etc. |
| Chair | Tomoaki Otsuki(Keio Univ.) / Fumihiro Yamashita(NTT) |
| Vice Chair | Satoshi Suyama(NTT DoCoMo) / Fumiaki Maehara(Waseda Univ.) / Toshihiko Nishimura(Hokkaido Univ.) / Hisashi Sujikai(NHK) / Hiroyasu Ishikawa(Nihon Univ.) |
| Secretary | Satoshi Suyama(NTT) / Fumiaki Maehara(Kyushu Univ.) / Toshihiko Nishimura(NTT) / Hisashi Sujikai(NHK) / Hiroyasu Ishikawa |
| Assistant | Kazushi Muraoka(NTT DOCOMO) / Shinsuke Ibi(Doshisha Univ.) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Shinya Kumagai(Fujitsu) / Shinobu Nanba(KDDI Research) / Takuya Okura(NICT) |
| Paper Information | |
| Registration To | Technical Committee on Radio Communication Systems / Technical Committee on Satellite Telecommunications |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Wireless Network Control Enabled by Data Assessment Using Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | machine learning |
| Keyword(2) | data importance |
| Keyword(3) | spatial information |
| Keyword(4) | real-time prediction |
| Keyword(5) | wireless network control |
| 1st Author's Name | Ryoichi Shinkuma |
| 1st Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Kyoto University(Kyoto Univ.) |
| Date | 2019-08-23 |
| Paper # | RCS2019-166 |
| Volume (vol) | vol.119 |
| Number (no) | RCS-176 |
| Page | pp.pp.109-112(RCS), |
| #Pages | 4 |
| Date of Issue | 2019-08-15 (RCS) |