Paper Abstract and Keywords |
Presentation |
2017-08-18 13:50
Performance Evaluation of User Selection Algorithm for High-SHF-Band Massive MIMO by Using Real Propagation Channel Data Hiroyuki Miyazaki, Satoshi Suyama, Tatsuki Okuyama, Jun Mashino, Yukihiko Okumura (NTT DOCOMO), Kenji Nakagawa, Kiyoshige Nakamura, Akihiro Okazaki (Mitsubishi Electric) RCS2017-166 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The 5th generation mobile communication system (5G) will introduce the use of higher frequency band with wider bandwidth and Massive MIMO technology for realizing super high bit rate and higher system capacity of more than 10 Gbps. In high-SHF band Massive MIMO, from the point of view of realization, as a novel method for hybrid beamforming (BF) that combines analog BF with digital precoding, joint processing of fixed analog BF and channel state information (CSI)-based precoding (FBCP) has been proposed. Moreover, transmission performance in multiuser conditions mainly depends on inter-user interference, and thus a low-complexity user selection algorithm that selects users from multiple users on maximum throughput criterion has been also proposed for FBCP. On the other hand, it is important to evaluate the proposed algorithms in the real radio channel, because the Massive MIMO performance highly depends on propagation channel characteristics. In this report, we measure propagation channels by using horn antennas that simulate the fixed analog BF in 28 GHz band indoor propagation experiments, and verify the effectiveness of the proposed user selection algorithm by computer simulations using real propagation channel data. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
5G / high SHF bands / Massive MIMO / hybrid beamforming / propagation channel data / user selection / / |
Reference Info. |
IEICE Tech. Rep., vol. 117, no. 175, RCS2017-166, pp. 123-128, Aug. 2017. |
Paper # |
RCS2017-166 |
Date of Issue |
2017-08-10 (RCS) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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RCS2017-166 |
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