Radio Techniques Incorporating Sparse Modeling

Toshihiko NISHIMURA; Yasutaka OGAWA; Takeo OHGANE; Junichiro HAGIWARA

doi:10.1587/transfun.2020EAI0001

IEICE TRANSACTIONS on Fundamentals

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Radio Techniques Incorporating Sparse Modeling

Toshihiko NISHIMURA, Yasutaka OGAWA, Takeo OHGANE, Junichiro HAGIWARA

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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 In time-varying environments, CSI estimated with pilot symbols may be outdated at the actual transmission time.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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.3 pp.591-603

Publication Date: 2021/03/01

Publicized: 2020/09/01

Online ISSN: 1745-1337

DOI: 10.1587/transfun.2020EAI0001

Type of Manuscript: INVITED SURVEY PAPER

Category: Digital Signal Processing

Authors

Toshihiko NISHIMURA
  Hokkaido University
Yasutaka OGAWA
  Hokkaido University
Takeo OHGANE
  Hokkaido University
Junichiro HAGIWARA
  Hokkaido University

Keyword

sparse modeling, compressed sensing, sparse Bayesian learning, DOA estimation, channel estimation

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Toshihiko NISHIMURA, Yasutaka OGAWA, Takeo OHGANE, Junichiro HAGIWARA, "Radio Techniques Incorporating Sparse Modeling" in IEICE TRANSACTIONS on Fundamentals, vol. E104-A, no. 3, pp. 591-603, March 2021, doi: 10.1587/transfun.2020EAI0001.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAI0001/_p

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@ARTICLE{e104-a_3_591,
author={Toshihiko NISHIMURA, Yasutaka OGAWA, Takeo OHGANE, Junichiro HAGIWARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Radio Techniques Incorporating Sparse Modeling},
year={2021},
volume={E104-A},
number={3},
pages={591-603},
abstract={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.},
keywords={},
doi={10.1587/transfun.2020EAI0001},
ISSN={1745-1337},
month={March},}

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TY - JOUR
TI - Radio Techniques Incorporating Sparse Modeling
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 591
EP - 603
AU - Toshihiko NISHIMURA
AU - Yasutaka OGAWA
AU - Takeo OHGANE
AU - Junichiro HAGIWARA
PY - 2021
DO - 10.1587/transfun.2020EAI0001
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2021
AB - 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.
ER -

IEICE TRANSACTIONS on Fundamentals