| Presentation | 2018-07-12 [Poster Presentation] Deep Learning Based RSS Prediction Using RGB-D Camera for mmWave Communications Kota Nakashima, Yusuke Koda, Koji Yamamoto, Hironao Okamoto, Takayuki Nishio, Masahiro Morikura, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal strength (RSS) value prediction scheme from depth images. By modeling the relationships between time-sequential depth images and RSS values based on machine learning, it is possible to predict the future RSS values, and thereby, a predictive handover makes a moment of degradation of the RSS value avoidable. As prediction models of RSS value, two machine learning models are compared: the combination of the convolutional neural network and convolutional long short-term memory (CNN+ConvLSTM), and random forest. As the number of input images increases, the prediction accuracy generally improves, however, too numerous input images may make the prediction accuracy worse because of over-fitting. Experimental results reveal that the number of input images that are input in order to predict the RSS value the most accurately is 16. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | machine learning / deep learning / wireless communication quality prediction / millimeter-wave communications / handover |
| Paper # | RCC2018-37,NS2018-50,RCS2018-95,SR2018-34,ASN2018-31 |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) |
| Conference Information | |
| Committee | ASN / NS / RCS / SR / RCC |
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| Conference Date | 2018/7/11(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hakodate Arena |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT（Internet of Things）, etc. |
| Chair | Hiraku Okada(Nagoya Univ.) / Yoshikatsu Okazaki(NTT) / Tomoaki Otsuki(Keio Univ.) / Kenta Umebayashi(Tokyo Univ. of Agric. and Tech.) / Kazunori Hayashi(Osaka City Univ.) |
| Vice Chair | Koji Yamamoto(Kyoto Univ.) / Jin Nakazawa(Keio Univ.) / Kazuya Monden(Hitachi) / Akihiro Nakao(Univ. of Tokyo) / Eisuke Fukuda(Fujitsu Labs.) / Satoshi Suyama(NTT DoCoMo) / Fumiaki Maehara(Waseda Univ.) / Masayuki Ariyoshi(NEC) / Suguru Kameda(Tohoku Univ.) / Shunichi Azuma(Nagoya Univ.) / HUAN-BANG LI(NICT) |
| Secretary | Koji Yamamoto(NICT) / Jin Nakazawa(Sophia Univ.) / Kazuya Monden(Kanagawa Inst. of Tech.) / Akihiro Nakao(NTT) / Eisuke Fukuda(Osaka Pref Univ.) / Satoshi Suyama(Hokkaido Univ.) / Fumiaki Maehara(NTT) / Masayuki Ariyoshi(NICT) / Suguru Kameda(ATR) / Shunichi Azuma(Univ. of Electro-Comm.) / HUAN-BANG LI(Kagawa Univ.) |
| Assistant | Masafumi Hashimoto(Osaka Univ.) / Tomoyuki Ota(Hiroshima City Univ.) / Tatsuya Kikuzuki(Fujitu Lab.) / Ryo Nakano(HITACHI) / Yoshifumi Hotta(Mitsubishi Electric) / Kenichi Kashibuchi(NTT) / Kazushi Muraoka(NTT DOCOMO) / Shinsuke Ibi(Osaka Univ.) / Hiroshi Nishimoto(Mitsubishi Electric) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Gia Khanh Tran(Tokyo Inst. of Tech.) / Syusuke Narieda(Mie Univ.) / Koji Ohshima(Kozo Keikaku Engineering) / Mai Ohta(Fukuoka Univ.) / Teppei Oyama(Fujitsu Lab.) / Toshinori Kagawa(NICT) / Masateru Ogura(NAIST) |
| Paper Information | |
| Registration To | Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Network Systems / Technical Committee on Radio Communication Systems / Technical Committee on Smart Radio / Technical Committee on Reliable Communication and Control |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] Deep Learning Based RSS Prediction Using RGB-D Camera for mmWave Communications |
| Sub Title (in English) | |
| Keyword(1) | machine learning |
| Keyword(2) | deep learning |
| Keyword(3) | wireless communication quality prediction |
| Keyword(4) | millimeter-wave communications |
| Keyword(5) | handover |
| 1st Author's Name | Kota Nakashima |
| 1st Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 2nd Author's Name | Yusuke Koda |
| 2nd Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 3rd Author's Name | Koji Yamamoto |
| 3rd Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 4th Author's Name | Hironao Okamoto |
| 4th Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 5th Author's Name | Takayuki Nishio |
| 5th Author's Affiliation | Kyoto University(Kyoto Univ.) |
| 6th Author's Name | Masahiro Morikura |
| 6th Author's Affiliation | Kyoto University(Kyoto Univ.) |
| Date | 2018-07-12 |
| Paper # | RCC2018-37,NS2018-50,RCS2018-95,SR2018-34,ASN2018-31 |
| Volume (vol) | vol.118 |
| Number (no) | RCC-123,NS-124,RCS-125,SR-126,ASN-127 |
| Page | pp.pp.75-76(RCC), pp.81-82(NS), pp.93-94(RCS), pp.85-86(SR), pp.91-92(ASN), |
| #Pages | 2 |
| Date of Issue | 2018-07-04 (RCC, NS, RCS, SR, ASN) |