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

被引：2
|
作者：
Tan, Benying [1 ]
Li, Xiang [1 ]
Ding, Shuxue [2 ]
Li, Yujie [3 ]
Akaho, Shotaro [3 ]
Asoh, Hideki [3 ]
机构：
[1] Univ Aizu, Sch Comp Sci & Engn, Aizu Wakamatsu, Fukushima, Japan
[2] Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin, Peoples R China
[3] Natl Inst Adv Ind Sci & Technol, Tsukuba, Ibaraki, Japan
来源：
2019 IEEE 10TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST 2019) | 2019年
关键词：
MIMO system; ML detection; binary quadratic programming (BQP); difference of convex functions (DC) programming; DC algorithm (DCA); COMPLEXITY; ALGORITHM;
D O I：
10.1109/icawst.2019.8923139
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：122 / 127
页数：6
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