| Presentation | 2017-11-08 Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction Rei Nagashima, Tomoaki Ohtsuki, Wenjie Jiang, Yasushi Takatori, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). However, it is necessary to feed back the compression matrix to compress a channel once every updating interval $T_s$ where the compression matrix accounts for a large portion of the amount of feedback. Therefore, there exists a problem that the amount of feedback increases when $T_s$ is small. In this report, to solve this problem, we propose a method to compress the channel matrix based on multi-dimensional scaling (MDS) with channel prediction. In the proposed method, we decrease the size of the CSI by mapping the channels on the multi-dimensional space and reducing the number of dimensions using MDS, thus our method does not need generating and feeding back the compression matrix. By computer simulation, we show that the proposed method achieves the same system capacity with the smaller amount of feedback compared to the conventional one based on PCA when the channel changes fast. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Massive MIMO / 5G / Channel Compression / Multi-Dimensional Scaling / Channel Prediction / Feedback Reduction |
| Paper # | RCS2017-221 |
| Date of Issue | 2017-11-01 (RCS) |
| Conference Information | |
| Committee | AP / RCS |
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| Conference Date | 2017/11/8(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Fukuoka University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. |
| Chair | Jiro Hirokawa(Tokyo Tech.) / Hidekazu Murata(Kyoto Univ.) |
| Vice Chair | Ryo Yamaguchi(SoftBank) / Yukitoshi Sanada(Keio Univ.) / Eisuke Fukuda(Fujitsu Labs.) / Satoshi Suyama(NTT DoCoMo) |
| Secretary | Ryo Yamaguchi(NTT DoCoMo) / Yukitoshi Sanada(Saitama Univ.) / Eisuke Fukuda(Toshiba) / Satoshi Suyama(Hokkaido Univ.) |
| Assistant | Nobuyasu Takemura(Nippon Inst. of Tech.) / Satoshi Yamaguchi(Mitsubishi Electric) / Tetsuya Yamamoto(Panasonic) / Koichi Ishihara(NTT) / Kazushi Muraoka(NEC) / Shinsuke Ibi(Osaka Univ.) / Hiroshi Nishimoto(Mitsubishi Electric) |
| Paper Information | |
| Registration To | Technical Committee on Antennas and Propagation / 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) | Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction |
| Sub Title (in English) | |
| Keyword(1) | Massive MIMO |
| Keyword(2) | 5G |
| Keyword(3) | Channel Compression |
| Keyword(4) | Multi-Dimensional Scaling |
| Keyword(5) | Channel Prediction |
| Keyword(6) | Feedback Reduction |
| 1st Author's Name | Rei Nagashima |
| 1st Author's Affiliation | Keio University(Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University(Keio Univ.) |
| 3rd Author's Name | Wenjie Jiang |
| 3rd Author's Affiliation | NTT Network Innovation Laboratories(NTT) |
| 4th Author's Name | Yasushi Takatori |
| 4th Author's Affiliation | NTT Network Innovation Laboratories(NTT) |
| Date | 2017-11-08 |
| Paper # | RCS2017-221 |
| Volume (vol) | vol.117 |
| Number (no) | RCS-284 |
| Page | pp.pp.93-98(RCS), |
| #Pages | 6 |
| Date of Issue | 2017-11-01 (RCS) |