| Presentation | 2020-03-05 Deep reinforcement learning based access control scheme for radio access networks Hang Zhou, Xiaoyan Wang, Masahiro Umehira, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | After a disaster occurred, it is extremely important to reconstruct the network and provide the communication services to the victims immediately. Deploying MDRU (movable and deployable resource unit) in the disaster area, along with multiple access points to extend the service area of MDRU is a very promising solution. In the disaster area, since the communication environment changes frequently, it is difficult for the user terminal to perform the optimal radio access control. In this paper, we propose a deep reinforcement learning based radio access control mechanism, and evaluate its performance by simulations. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Deep reinforcement learning / Radio access network |
| Paper # | SR2019-123 |
| Date of Issue | 2020-02-26 (SR) |
| Conference Information | |
| Committee | RCS / SR / SRW |
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| Conference Date | 2020/3/4(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Chair | Tomoaki Otsuki(Keio Univ.) / Masayuki Ariyoshi(NEC) / Satoshi Denno(Okayama Univ.) |
| Vice Chair | Satoshi Suyama(NTT DoCoMo) / Fumiaki Maehara(Waseda Univ.) / Toshihiko Nishimura(Hokkaido Univ.) / Suguru Kameda(Tohoku Univ.) / Osamu Takyu(Shinshu Univ.) / Kentaro Ishidu(NICT) / Keiichi Mizutani(Kyoto Univ.) |
| Secretary | Satoshi Suyama(NTT) / Fumiaki Maehara(Kyushu Univ.) / Toshihiko Nishimura(ATR) / Suguru Kameda(Univ. of Electro-Comm.) / Osamu Takyu(Mie Univ.) / Kentaro Ishidu(Tokyo Inst. of Tech.) / Keiichi Mizutani(Anritsu) |
| Assistant | Kazushi Muraoka(NEC) / Shinsuke Ibi(Doshisha Univ.) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Mai Ohta(Fukuoka Univ.) / Teppei Oyama(Fujitsu Lab.) / Kentaro Kobayashi(Nagoya Univ.) / Masaaki Fuse(Anritsu) / Tomoki Murakami(NTT) |
| 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) | Deep reinforcement learning based access control scheme for radio access networks |
| Sub Title (in English) | |
| Keyword(1) | Deep reinforcement learning |
| Keyword(2) | Radio access network |
| 1st Author's Name | Hang Zhou |
| 1st Author's Affiliation | Graduate School of Ibaraki University(Ibaraki Univ.) |
| 2nd Author's Name | Xiaoyan Wang |
| 2nd Author's Affiliation | Graduate School of Ibaraki University(Ibaraki Univ.) |
| 3rd Author's Name | Masahiro Umehira |
| 3rd Author's Affiliation | Graduate School of Ibaraki University(Ibaraki Univ.) |
| Date | 2020-03-05 |
| Paper # | SR2019-123 |
| Volume (vol) | vol.119 |
| Number (no) | SR-449 |
| Page | pp.pp.59-64(SR), |
| #Pages | 6 |
| Date of Issue | 2020-02-26 (SR) |