Radio Techniques Incorporating Sparse Modeling Article Swipe
Toshihiko Nishimura
,
Yasutaka Ogawa
,
Takeo Ohgane
,
Junichiro Hagiwara
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1587/transfun.2020eai0001
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1587/transfun.2020eai0001
Sparse modeling is one of the most active research areas in engineering and science. The technique provides solutions from far fewer samples exploiting sparsity, that is, the majority of the data are zero. This paper reviews sparse modeling in radio techniques. The first half of this paper introduces direction-of-arrival (DOA) estimation from signals received by multiple antennas. The estimation is carried out using compressed sensing, an effective tool for the sparse modeling, which produces solutions to an underdetermined linear system with a sparse regularization term. The DOA estimation performance is compared among three compressed sensing algorithms. The second half reviews channel state information (CSI) acquisitions in multiple-input multiple-output (MIMO) systems. In time-varying environments, CSI estimated with pilot symbols may be outdated at the actual transmission time. We describe CSI prediction based on sparse DOA estimation, and show excellent precoding performance when using the CSI prediction. The other topic in the second half is sparse Bayesian learning (SBL)-based channel estimation. A base station (BS) has many antennas in a massive MIMO system. A major obstacle for using the massive MIMO system in frequency-division duplex mode is an overhead for downlink CSI acquisition because we need to send many pilot symbols from the BS and to get the feedback from user equipment. An SBL-based channel estimation method can mitigate this issue. In this paper, we describe the outline of the method, and show that the technique can reduce the downlink pilot symbols.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1587/transfun.2020eai0001
- https://www.jstage.jst.go.jp/article/transfun/E104.A/3/E104.A_2020EAI0001/_pdf
- OA Status
- bronze
- Cited By
- 2
- References
- 93
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3082549911
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3082549911Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1587/transfun.2020eai0001Digital Object Identifier
- Title
-
Radio Techniques Incorporating Sparse ModelingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-31Full publication date if available
- Authors
-
Toshihiko Nishimura, Yasutaka Ogawa, Takeo Ohgane, Junichiro HagiwaraList of authors in order
- Landing page
-
https://doi.org/10.1587/transfun.2020eai0001Publisher landing page
- PDF URL
-
https://www.jstage.jst.go.jp/article/transfun/E104.A/3/E104.A_2020EAI0001/_pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://www.jstage.jst.go.jp/article/transfun/E104.A/3/E104.A_2020EAI0001/_pdfDirect OA link when available
- Concepts
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Computer science, MIMO, Channel state information, Compressed sensing, Telecommunications link, Precoding, Base station, Overhead (engineering), Algorithm, Underdetermined system, Computer engineering, Channel (broadcasting), Real-time computing, Wireless, Telecommunications, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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93Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.raw_source_name | IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences |
| primary_location.landing_page_url | https://doi.org/10.1587/transfun.2020eai0001 |
| publication_date | 2020-08-31 |
| publication_year | 2020 |
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