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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Vydáno v:International Conference on Awareness Science and Technology s. 1 - 6
Hlavní autoři: Tan, Benying, Li, Xiang, Ding, Shuxue, Li, Yujie, Akaho, Shotaro, Asoh, Hideki
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2019
Témata:
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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Shrnutí: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.
ISSN:2325-5994
DOI:10.1109/ICAwST.2019.8923139