| Presentation | 2020-07-09 [Invited Lecture] Blind Adaptive Array Interference Suppression Performance with Deep Learning based SIR Estimation Kazuki Maruta, Shun Kojima, Daisuke Hisano, Yu Nakayama, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | This paper proposes a blind interference estimation via deep learning approach exploiting the visualized wireless signal information, that is, IQ constellation. Co-channel interference becomes more extensive due to frequency resource exhaustion and small cell deployment which had been triggered by mobile traffic explosion. Multi-antenna signal processing known to blind adaptive array (BAA) is an effective means to suppress co-channel interference without any a priori information such as channel state information. Unfortunately, blind algorithms have their applicable regions depending on the signal-to-interference (SIR) at array input. These algorithms should be optimally selected according to interference level. Here we investigates the possibility of the SIR classification by the multi-layered deep convolutional neural network (CNN). Constellation images where includes the desired and interference signals are used for model training. Further, we clarify the interference suppression performance of BAAs using estimated SIR values. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Adaptive array / Interference suppression / Constant modulus algorithm / Power inversion / Interference estimation / Deep learning / Constellation |
| Paper # | RCC2020-8,NS2020-37,RCS2020-71,SR2020-16,SeMI2020-8 |
| Date of Issue | 2020-07-01 (RCC, NS, RCS, SR, SeMI) |
| Conference Information | |
| Committee | SR / NS / SeMI / RCC / RCS |
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| Conference Date | 2020/7/8(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication and Network Technology of the AI Age, M2M (Machine-to-Machine)，D2D (Device-to-Device)，IoT（Internet of Things）, etc |
| Chair | Masayuki Ariyoshi(NEC) / Akihiro Nakao(Univ. of Tokyo) / Susumu Ishihara(Shizuoka Univ.) / HUAN-BANG LI(NICT) / Eiji Okamoto(Nagoya Inst. of Tech.) |
| Vice Chair | Suguru Kameda(Tohoku Univ.) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Tetsuya Oishi(NTT) / Kazuya Monden(Hitachi) / Koji Yamamoto(Kyoto Univ.) / Shunichi Azuma(Nagoya Univ.) / Koji Ishii(Kagawa Univ.) / Fumiaki Maehara(Waseda Univ.) / Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) |
| Secretary | Suguru Kameda(ATR) / Osamu Takyu(Univ. of Electro-Comm.) / Kentaro Ishidu(Mie Univ.) / Tetsuya Oishi(NTT) / Kazuya Monden(Chuo Univ.) / Koji Yamamoto(Kyoto Univ.) / Shunichi Azuma(Osaka Univ.) / Koji Ishii(Hitachi) / Fumiaki Maehara(Waseda Univ.) / Toshihiko Nishimura(CRIEPI) / Tomoya Tandai(Osaka Univ.) |
| Assistant | Mai Ohta(Fukuoka Univ.) / Teppei Oyama(Fujitsu Lab.) / Kentaro Kobayashi(Nagoya Univ.) / Shinya Kawano(NTT) / Yuki Katsumata(NTT DOCOMO) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) / Akira Uchiyama(Osaka Univ.) / SHAN LIN(NICT) / Masaki Ogura(Osaka Univ.) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) |
| Paper Information | |
| Registration To | Technical Committee on Smart Radio / Technical Committee on Network Systems / Technical Committee on Sensor Network and Mobile Intelligence / Technical Committee on Reliable Communication and Control / Technical Committee on Radio Communication Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Lecture] Blind Adaptive Array Interference Suppression Performance with Deep Learning based SIR Estimation |
| Sub Title (in English) | |
| Keyword(1) | Adaptive array |
| Keyword(2) | Interference suppression |
| Keyword(3) | Constant modulus algorithm |
| Keyword(4) | Power inversion |
| Keyword(5) | Interference estimation |
| Keyword(6) | Deep learning |
| Keyword(7) | Constellation |
| 1st Author's Name | Kazuki Maruta |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 2nd Author's Name | Shun Kojima |
| 2nd Author's Affiliation | Chiba University(Chiba Univ.) |
| 3rd Author's Name | Daisuke Hisano |
| 3rd Author's Affiliation | Osaka University(Osaka Univ.) |
| 4th Author's Name | Yu Nakayama |
| 4th Author's Affiliation | Tokyo University of Agriculture and Technology(TUAT) |
| Date | 2020-07-09 |
| Paper # | RCC2020-8,NS2020-37,RCS2020-71,SR2020-16,SeMI2020-8 |
| Volume (vol) | vol.120 |
| Number (no) | RCC-87,NS-88,RCS-89,SR-90,SeMI-91 |
| Page | pp.pp.37-41(RCC), pp.37-41(NS), pp.79-83(RCS), pp.43-47(SR), pp.31-35(SeMI), |
| #Pages | 5 |
| Date of Issue | 2020-07-01 (RCC, NS, RCS, SR, SeMI) |