Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems

Chao SUN; Ling YANG; Juan DU; Fenggang SUN; Li CHEN; Haipeng XI; Shenglei DU

doi:10.1587/transcom.2016EBP3473

Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems

Chao SUN, Ling YANG, Juan DU, Fenggang SUN, Li CHEN, Haipeng XI, Shenglei DU

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In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

Publication: IEICE TRANSACTIONS on Communications Vol.E101-B No.3 pp.698-708

Publication Date: 2018/03/01

Publicized: 2017/08/28

Online ISSN: 1745-1345

DOI: 10.1587/transcom.2016EBP3473

Type of Manuscript: PAPER

Category: Fundamental Theories for Communications

Authors

Chao SUN
  Lanzhou University
Ling YANG
  Lanzhou University
Juan DU
  Lanzhou University
Fenggang SUN
  Shandong Agricultural University
Li CHEN
  Lanzhou University
Haipeng XI
  Lanzhou University
Shenglei DU
  Lanzhou University

Keyword

MIMO systems, blind source separation, blind equalization, support vector regression, constant modulus algorithm, radius directed algorithm

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Chao SUN, Ling YANG, Juan DU, Fenggang SUN, Li CHEN, Haipeng XI, Shenglei DU, "Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems" in IEICE TRANSACTIONS on Communications, vol. E101-B, no. 3, pp. 698-708, March 2018, doi: 10.1587/transcom.2016EBP3473.
Abstract: In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2016EBP3473/_p

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@ARTICLE{e101-b_3_698,
author={Chao SUN, Ling YANG, Juan DU, Fenggang SUN, Li CHEN, Haipeng XI, Shenglei DU, },
journal={IEICE TRANSACTIONS on Communications},
title={Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems},
year={2018},
volume={E101-B},
number={3},
pages={698-708},
abstract={In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.},
keywords={},
doi={10.1587/transcom.2016EBP3473},
ISSN={1745-1345},
month={March},}

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TY - JOUR
TI - Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 698
EP - 708
AU - Chao SUN
AU - Ling YANG
AU - Juan DU
AU - Fenggang SUN
AU - Li CHEN
AU - Haipeng XI
AU - Shenglei DU
PY - 2018
DO - 10.1587/transcom.2016EBP3473
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E101-B
IS - 3
JA - IEICE TRANSACTIONS on Communications
Y1 - March 2018
AB - In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.
ER -