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In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E101-B No.12 pp.2462-2470

- Publication Date
- 2018/12/01

- Publicized
- 2018/06/22

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.2017EBP3417

- Type of Manuscript
- PAPER

- Category
- Wireless Communication Technologies

Yanqing LIU

Jiangxi University of Finance and Economics,Baylor University

Liyun DAI

Jiangxi University of Finance and Economics

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Yanqing LIU, Liyun DAI, "Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding" in IEICE TRANSACTIONS on Communications,
vol. E101-B, no. 12, pp. 2462-2470, December 2018, doi: 10.1587/transcom.2017EBP3417.

Abstract: In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3417/_p

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@ARTICLE{e101-b_12_2462,

author={Yanqing LIU, Liyun DAI, },

journal={IEICE TRANSACTIONS on Communications},

title={Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding},

year={2018},

volume={E101-B},

number={12},

pages={2462-2470},

abstract={In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.},

keywords={},

doi={10.1587/transcom.2017EBP3417},

ISSN={1745-1345},

month={December},}

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TY - JOUR

TI - Multiuser Multiantenna Downlink Transmission Using Extended Regularized Channel Inversion Precoding

T2 - IEICE TRANSACTIONS on Communications

SP - 2462

EP - 2470

AU - Yanqing LIU

AU - Liyun DAI

PY - 2018

DO - 10.1587/transcom.2017EBP3417

JO - IEICE TRANSACTIONS on Communications

SN - 1745-1345

VL - E101-B

IS - 12

JA - IEICE TRANSACTIONS on Communications

Y1 - December 2018

AB - In this paper, we apply extended regularized channel inversion precoding to address the multiuser multiantenna downlink transmission problem. Different from conventional regularized channel inversion precoding, extended RCI precoding considers non-homogeneous channels, adjusts more regularization parameters, and exploits the information gained by inverting the covariance matrix of the channel. Two ways of determining the regularization parameters are investigated. First, the parameters can be determined by solving a max-min SINR problem. The constraints of the problem can be transformed to the second-order cone (SOC) constraints. The optimal solution of the problem can be obtained by iteratively solving a second-order cone programming (SOCP) problem. In order to reduce the computational complexity, a one-shot algorithm is proposed. Second, the sum-rate maximization problem is discussed. The simple gradient-based method is used to solve the problem and get the regularization parameters. The simulation results indicate that the proposed algorithms exhibit improved max-min SINR performance and sum-rate performance over RCI precoding.

ER -