Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector
Summary :
In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.12 pp.2671-2679
- Publication Date
- 2017/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.2671
- Type of Manuscript
- Special Section PAPER (Special Section on Information Theory and Its Applications)
- Category
- Communication Theory and Systems
Authors
Ryo HAYAKAWA
Kyoto University
Kazunori HAYASHI
Osaka City University
Keyword
Latest Issue
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Ryo HAYAKAWA, Kazunori HAYASHI, "Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 12, pp. 2671-2679, December 2017, doi: 10.1587/transfun.E100.A.2671.
Abstract: In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.2671/_p
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@ARTICLE{e100-a_12_2671,
author={Ryo HAYAKAWA, Kazunori HAYASHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector},
year={2017},
volume={E100-A},
number={12},
pages={2671-2679},
abstract={In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.},
keywords={},
doi={10.1587/transfun.E100.A.2671},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - Error Recovery for Massive MIMO Signal Detection via Reconstruction of Discrete-Valued Sparse Vector
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2671
EP - 2679
AU - Ryo HAYAKAWA
AU - Kazunori HAYASHI
PY - 2017
DO - 10.1587/transfun.E100.A.2671
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2017
AB - In this paper, we propose a novel error recovery method for massive multiple-input multiple-output (MIMO) signal detection, which improves an estimate of transmitted signals by taking advantage of the sparsity and the discreteness of the error signal. We firstly formulate the error recovery problem as the maximum a posteriori (MAP) estimation and then relax the MAP estimation into a convex optimization problem, which reconstructs a discrete-valued sparse vector from its linear measurements. By using the restricted isometry property (RIP), we also provide a theoretical upper bound of the size of the reconstruction error with the optimization problem. Simulation results show that the proposed error recovery method has better bit error rate (BER) performance than that of the conventional error recovery method.
ER -
English
