A Novel Maximum-Likelihood Detection for the Binary MIMO System Using DC Programming

The multiple-input multiple-output (MIMO) system is widely used in wireless communications. For the problem of the discrete maximum-likelihood (ML) detection for the MIMO system, one can formulate it as binary quadratic programming (BQP). The general BQP problem is an NP-hard problem, which is a cha...

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Published in:International Conference on Awareness Science and Technology pp. 1 - 6
Main Authors: Tan, Benying, Li, Xiang, Ding, Shuxue, Li, Yujie, Akaho, Shotaro, Asoh, Hideki
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Antenna measurements
binary quadratic programming (BQP)
Convex functions
DC algorithm (DCA)
Detectors
difference of convex functions (DC) programming
MIMO communication
MIMO system
ML detection
Programming
Receiving antennas
Transmitting antennas
ISSN:2325-5994
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Abstract The multiple-input multiple-output (MIMO) system is widely used in wireless communications. For the problem of the discrete maximum-likelihood (ML) detection for the MIMO system, one can formulate it as binary quadratic programming (BQP). The general BQP problem is an NP-hard problem, which is a challenge for finding promising solutions. The variable complexity is a special considered issue. In this paper, inspired by the optimization of sparse constrains, we employ a regularization approach to deal with the binary constraints in the proposed formulation and then introduce the difference of convex functions (DC) programming to solve the formulated nonconvex cost function. A novel and robust DC algorithm is proposed. Numerical experiments show that the proposed algorithm, which is based on DC programming, can achieve accurate results with a higher convergence rate.
AbstractList The multiple-input multiple-output (MIMO) system is widely used in wireless communications. For the problem of the discrete maximum-likelihood (ML) detection for the MIMO system, one can formulate it as binary quadratic programming (BQP). The general BQP problem is an NP-hard problem, which is a challenge for finding promising solutions. The variable complexity is a special considered issue. In this paper, inspired by the optimization of sparse constrains, we employ a regularization approach to deal with the binary constraints in the proposed formulation and then introduce the difference of convex functions (DC) programming to solve the formulated nonconvex cost function. A novel and robust DC algorithm is proposed. Numerical experiments show that the proposed algorithm, which is based on DC programming, can achieve accurate results with a higher convergence rate.
Author Ding, Shuxue
Li, Xiang
Tan, Benying
Asoh, Hideki
Akaho, Shotaro
Li, Yujie
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  surname: Li
  fullname: Li, Xiang
  organization: The University of Aizu,School of Computer Science and Engineering,Aizuwakamatsu,Japan
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  givenname: Shuxue
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  organization: Guilin University of Electronic Technology,School of Artificial Intelligence,Guilin,China
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  fullname: Li, Yujie
  organization: National Institute of Advanced Industrial Science and Technology,Tsukuba,Japan
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  organization: National Institute of Advanced Industrial Science and Technology,Tsukuba,Japan
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  givenname: Hideki
  surname: Asoh
  fullname: Asoh, Hideki
  organization: National Institute of Advanced Industrial Science and Technology,Tsukuba,Japan
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Snippet The multiple-input multiple-output (MIMO) system is widely used in wireless communications. For the problem of the discrete maximum-likelihood (ML) detection...
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SubjectTerms Antenna measurements
binary quadratic programming (BQP)
Convex functions
DC algorithm (DCA)
Detectors
difference of convex functions (DC) programming
MIMO communication
MIMO system
ML detection
Programming
Receiving antennas
Transmitting antennas
Title A Novel Maximum-Likelihood Detection for the Binary MIMO System Using DC Programming
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