| Presentation | 2021-11-11 A Study on Receive Beamforming for Multi-User Detection with Trainable Gaussian Belief Propagation Takanobu Doi, Jun Shikida, Kazushi Muraoka, Naoto Ishii, Daichi Shirase, Takumi Takahashi, Shinsuke Ibi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We propose two digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via Gaussian belief propagation (GaBP) at a base station (BS) comprising radio unit (RU) and distributed unit (DU) for massive multi-input multi-output. In such scenarios, it is vital to reduce the cost of the BSs by reducing the bandwidth of fronthaul (FH) links between RU and DU, and the dimensionality reduction of the received signal using receive BF at RU is a well-known strategy to reduce the amount of data transported via the FH links. We clarify appropriate criteria to design a BF weight considering the subsequent GaBP signal detection, and propose two BF methods: singular-value-decomposition-based BF and QR-decomposition-based BF with the aid of discrete-Fourier-transformation-based spreading. Both methods enable to reduce the dimension of the received signal without compromising the desired signal power by taking advantage of a null space of the channels. The BF reduces correlations between the beamformed received signals, which improves the robustness of GaBP against spatial fading correlation. Simulation results show that the proposed methods improve detection performance while reducing computation. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Massive MIMO / Receive Beamforming / Belief Propagation / Deep Unfolding |
| Paper # | RCS2021-159 |
| Date of Issue | 2021-11-03 (RCS) |
| Conference Information | |
| Committee | AP / RCS |
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| Conference Date | 2021/11/10(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | NBC-Bekkan (Nagasaki) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. |
| Chair | Hiroshi Yamada(Niigata Univ.) / Eiji Okamoto(Nagoya Inst. of Tech.) |
| Vice Chair | Mitoshi Fujimoto(Fukui Univ) / Toshihiko Nishimura(Hokkaido Univ.) / Tomoya Tandai(Toshiba) / Fumihide Kojima(NICT) |
| Secretary | Mitoshi Fujimoto(NTT DOCOMO) / Toshihiko Nishimura(National Defense Academy) / Tomoya Tandai(NEC) / Fumihide Kojima(Panasonic) |
| Assistant | Dr. Kim(Niigata Univ.) / Koichi Adachi(Univ. of Electro-Comm.) / Osamu Nakamura(Sharp) / Manabu Sakai(Mitsubishi Electric) / Masashi Iwabuchi(NTT) / Tatsuki Okuyama(NTT DOCOMO) |
| 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) | A Study on Receive Beamforming for Multi-User Detection with Trainable Gaussian Belief Propagation |
| Sub Title (in English) | |
| Keyword(1) | Massive MIMO |
| Keyword(2) | Receive Beamforming |
| Keyword(3) | Belief Propagation |
| Keyword(4) | Deep Unfolding |
| 1st Author's Name | Takanobu Doi |
| 1st Author's Affiliation | NEC Corporation(NEC) |
| 2nd Author's Name | Jun Shikida |
| 2nd Author's Affiliation | NEC Corporation(NEC) |
| 3rd Author's Name | Kazushi Muraoka |
| 3rd Author's Affiliation | NEC Corporation(NEC) |
| 4th Author's Name | Naoto Ishii |
| 4th Author's Affiliation | NEC Corporation(NEC) |
| 5th Author's Name | Daichi Shirase |
| 5th Author's Affiliation | Osaka University(Osaka Univ.) |
| 6th Author's Name | Takumi Takahashi |
| 6th Author's Affiliation | Osaka University(Osaka Univ.) |
| 7th Author's Name | Shinsuke Ibi |
| 7th Author's Affiliation | Doshisha University(Doshisha Univ.) |
| Date | 2021-11-11 |
| Paper # | RCS2021-159 |
| Volume (vol) | vol.121 |
| Number (no) | RCS-234 |
| Page | pp.pp.86-91(RCS), |
| #Pages | 6 |
| Date of Issue | 2021-11-03 (RCS) |