| Presentation | 2022-08-26 Inter-cell Interference Control by Joint Transmit Power and Transmit Beamforming Control based on Machine Learning Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In mobile communications, densely deployed small cell systems using the same frequency band are expected to increase the number of accessible users and to improve the system capacity. However, the gain in the system capacity can be damaged by inter-cell interference (ICI) within overlapping areas close to cell boundaries. Inter-cell interference coordination (ICIC) is one of the promising techniques to solve this problem. As one of ICIC schemes, joint control of transmit power and transmit beamforming (BF) by the base station (BS) has been investigated. Since this ICIC can be considered a non-convex optimization problem, the exhaustive search (ES) method and approximate iterative methods have been applied. However, as the number of BSs and antennas increases, the computational complexity of the two approaches grows exponentially. Thus, machine learning based schemes have been proposed to reduce the computational complexity. In order to improve such schemes furthermore, this report proposes an unsupervised learning-based convolutional neural network (CNN) for the joint control of transmit power and transmit BF. Computer simulations for MIMO channels show that the proposed method can significantly improve the system capacity while reducing the execution time drastically. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | MIMO / inter-cell interference coordination / transmit power control / transmit beamforming control / convolutional neural network / unsupervised learning |
| Paper # | RCS2022-118 |
| Date of Issue | 2022-08-18 (RCS) |
| Conference Information | |
| Committee | SAT / RCS |
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| Conference Date | 2022/8/25(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Tetsushi Ikegami(Meiji Univ.) / Kenichi Higuchi(Tokyo Univ. of Science) |
| Vice Chair | Masashi Kamei(NHK) / Takana Kaho(Shonan Inst. of Tech.) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) / Osamu Muta(Kyushu Univ.) |
| Secretary | Masashi Kamei(NTT) / Takana Kaho(NICT) / Tomoya Tandai(Panasonic) / Fumihide Kojima(Univ. of Electro-Comm) / Osamu Muta(Sharp) |
| Assistant | Riichiro Nagareda(KDDI Research) / Yuuki Koizumi(NHK) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Issei Kanno(KDDI Research) / Yuyuan Chang(Tokyo Inst. of Tech) |
| Paper Information | |
| Registration To | Technical Committee on Satellite Telecommunications / 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) | Inter-cell Interference Control by Joint Transmit Power and Transmit Beamforming Control based on Machine Learning |
| Sub Title (in English) | |
| Keyword(1) | MIMO |
| Keyword(2) | inter-cell interference coordination |
| Keyword(3) | transmit power control |
| Keyword(4) | transmit beamforming control |
| Keyword(5) | convolutional neural network |
| Keyword(6) | unsupervised learning |
| 1st Author's Name | Naoto Tamada |
| 1st Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 2nd Author's Name | Yuyuan Chang |
| 2nd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| 3rd Author's Name | Kazuhiko Fukawa |
| 3rd Author's Affiliation | Tokyo Institute of Technology(Tokyo Tech) |
| Date | 2022-08-26 |
| Paper # | RCS2022-118 |
| Volume (vol) | vol.122 |
| Number (no) | RCS-164 |
| Page | pp.pp.120-125(RCS), |
| #Pages | 6 |
| Date of Issue | 2022-08-18 (RCS) |