| Presentation | 2024-01-18 A Study on Massive MIMO Channel Estimation Based on Sparse Bayesian Learning Using Hierarchical Model Kengo Furuta, Takumi Takahashi, Kenta Ito, Shinsuke Ibi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Massive multi-input multi-output (MIMO) channels are known to have pseudo-sparsity in the angular (beam) domain, and it has been reported that the statistical properties can be used to achieve highly accurate channel estimation.Such a sparse signal recovery (SSR) has been investigated as a signal processing for a wide range of applications, especially in the field of compressed sensing (CS), and this paper focuses on a method based on sparse Bayesian learning (SBL).SBL is a Bayesian SSR algorithm based on least absolute shrinkage and selection operator (LASSO) regression, which improves estimation accuracy by simultaneously estimating (learning) the prior distribution (or its parameters) of the estimated variables.In this paper, we design the SBL algorithm for massive MIMO channel estimation using two- and three-level hierarchical Bayesian models based on complex-domain LASSO modeling, and compare their performance with those of existing SSR algorithms.We also verify the characteristics of the algorithms by evaluating them with different channel models. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Massive MIMO / channel estimation / sparse Bayesian learning / hierarchical Bayesian model / complex LASSO |
| Paper # | IT2023-34,SIP2023-67,RCS2023-209 |
| Date of Issue | 2024-01-11 (IT, SIP, RCS) |
| Conference Information | |
| Committee | SIP / IT / RCS |
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| Conference Date | 2024/1/18(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Takayuki Nakachi(Ryukyu Univ.) / Tetsuya Kojima(Tokyo Kosen) / Kenichi Higuchi(Tokyo Univ. of Science) |
| Vice Chair | Koichi Ichige(Yokohama National Univ.) / Kiyoshi Nishikawa(okyo Metropolitan Univ.) / Yasuyuki Nogami(Okayama Univ.) / Fumihide Kojima(NICT) / Osamu Muta(Kyushu Univ.) / Naoto Ishii(NEC) |
| Secretary | Koichi Ichige(Chiba Univ.) / Kiyoshi Nishikawa(Kogakuin Univ.) / Yasuyuki Nogami(Waseda Univ.) / Fumihide Kojima(Nagaoka Univ. of Tech.) / Osamu Muta(Univ. of Electro-Comm) / Naoto Ishii(Sharp) |
| Assistant | Taichi Yoshida(UEC) / Sayaka Shiota(Tokyo Metropolitan Univ.) / Tetsunao Matsuta(Saitamai Univ.) / Masashi Iwabuchi(NTT) / Issei Kanno(KDDI Research) / Yuyuan Chang(Tokyo Inst. of Tech) / Kazuki Maruta(Tokyo Univ. of Science) / Kiichi Tateishi(NTT Docomo) |
| Paper Information | |
| Registration To | Technical Committee on Signal Processing / Technical Committee on Information Theory / 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 Massive MIMO Channel Estimation Based on Sparse Bayesian Learning Using Hierarchical Model |
| Sub Title (in English) | |
| Keyword(1) | Massive MIMO |
| Keyword(2) | channel estimation |
| Keyword(3) | sparse Bayesian learning |
| Keyword(4) | hierarchical Bayesian model |
| Keyword(5) | complex LASSO |
| 1st Author's Name | Kengo Furuta |
| 1st Author's Affiliation | Osaka University(Osaka Univ.) |
| 2nd Author's Name | Takumi Takahashi |
| 2nd Author's Affiliation | Osaka University(Osaka Univ.) |
| 3rd Author's Name | Kenta Ito |
| 3rd Author's Affiliation | Osaka University(Osaka Univ.) |
| 4th Author's Name | Shinsuke Ibi |
| 4th Author's Affiliation | Doshisha University(Doshisha Uni.) |
| Date | 2024-01-18 |
| Paper # | IT2023-34,SIP2023-67,RCS2023-209 |
| Volume (vol) | vol.123 |
| Number (no) | IT-338,SIP-339,RCS-340 |
| Page | pp.pp.25-30(IT), pp.25-30(SIP), pp.25-30(RCS), |
| #Pages | 6 |
| Date of Issue | 2024-01-11 (IT, SIP, RCS) |