| Presentation | 2021-03-04 [Poster Presentation] A Study on Fading Variation Estimation Employing Deep Learning Based on Level Crossing Rate Koshiro Kawachi, Yukiko Shimbo, Hirofumi Suganuma, Fumiaki Maehara, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Fifth-generation (5G) mobile communication systems entail various scenarios such as enhanced mobile broadband (eMBB), ultra-reliable and low-latency communications (URLLC), and massive machine type communications (mMTC). In order to realize various requirements of 5G, it is expected to easily estimate channel conditions such as time selectivity which affects transmission performance. So far, we have proposed a time selectivity estimation method using level crossing rate, which estimates Doppler frequency just only by counting level cross of channel variation. As an extension of this work, we propose a deep-learning-based fading variation estimation method using level crossing rate. The estimation performance of the proposed method is demonstrated in comparison with the traditional estimation method on the assumption of Rayleigh fading as a starting point for evaluation under different types of fading channels. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | level crossing rate / time selective fading / doppler frequency / deep learning |
| Paper # | RCS2020-233,SR2020-72,SRW2020-62 |
| Date of Issue | 2021-02-24 (RCS, SR, SRW) |
| Conference Information | |
| Committee | RCS / SR / SRW |
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| Conference Date | 2021/3/3(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Chair | Eiji Okamoto(Nagoya Inst. of Tech.) / Masayuki Ariyoshi(NEC) / Satoshi Denno(Okayama Univ.) |
| Vice Chair | Fumiaki Maehara(Waseda Univ.) / Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) / Suguru Kameda(Tohoku Univ.) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Keiichi Mizutani(Kyoto Univ.) / Kentaro Saito(Tokyo Inst. of Tech.) / Hanako Noda(Anritsu) |
| Secretary | Fumiaki Maehara(Kyushu Univ.) / Toshihiko Nishimura(NEC) / Tomoya Tandai(ATR) / Suguru Kameda(Univ. of Electro-Comm.) / Osamu Takyu(Mie Univ.) / Kentaro Ishidu(NTT) / Keiichi Mizutani(NIigata Univ.) / Kentaro Saito / Hanako Noda |
| Assistant | Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) / Mai Ohta(Fukuoka Univ.) / Teppei Oyama(Fujitsu Lab.) / Kentaro Kobayashi(Nagoya Univ.) / Masaaki Fuse(Anritsu) / Akihito Noda(Nanzan Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Radio Communication Systems / Technical Committee on Smart Radio / Technical Committee on Short Range Wireless Communications |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Poster Presentation] A Study on Fading Variation Estimation Employing Deep Learning Based on Level Crossing Rate |
| Sub Title (in English) | |
| Keyword(1) | level crossing rate |
| Keyword(2) | time selective fading |
| Keyword(3) | doppler frequency |
| Keyword(4) | deep learning |
| 1st Author's Name | Koshiro Kawachi |
| 1st Author's Affiliation | Waseda University(Waseda Univ.) |
| 2nd Author's Name | Yukiko Shimbo |
| 2nd Author's Affiliation | Waseda University(Waseda Univ.) |
| 3rd Author's Name | Hirofumi Suganuma |
| 3rd Author's Affiliation | Waseda University(Waseda Univ.) |
| 4th Author's Name | Fumiaki Maehara |
| 4th Author's Affiliation | Waseda University(Waseda Univ.) |
| Date | 2021-03-04 |
| Paper # | RCS2020-233,SR2020-72,SRW2020-62 |
| Volume (vol) | vol.120 |
| Number (no) | RCS-404,SR-405,SRW-406 |
| Page | pp.pp.155-156(RCS), pp.48-49(SR), pp.43-44(SRW), |
| #Pages | 2 |
| Date of Issue | 2021-02-24 (RCS, SR, SRW) |