A near maximum likelihood decoding algorithm for MIMO systems based on semi-definite programming

Author

Mobasher, Amin ; Taherzadeh, Mahmoud ; Sotirov, Renata ; Khandani, Amir K.

Author_Institution

Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont.

fYear

2005

fDate

4-9 Sept. 2005

Firstpage

1686

Lastpage

1690

Abstract

In multi-input multi-output (MIMO) systems, maximum-likelihood (ML) decoding is equivalent to finding the closest lattice point in an N-dimensional complex space. In general, this problem is known to be NP hard. In this paper, we propose a quasi-maximum likelihood algorithm based on semi-definite programming (SDP). We introduce several SDP relaxation models for MIMO systems, with increasing complexity. We use interior-point methods for solving the models and obtain a near-ML performance with polynomial computational complexity. Lattice basis reduction is applied to further reduce the computational complexity of solving these models

Keywords

MIMO systems; computational complexity; matrix algebra; maximum likelihood decoding; optimisation; radio networks; MIMO systems; NP hard problem; interior-point methods; lattice basis reduction; maximum likelihood decoding algorithm; multiinput multioutput systems; polynomial computational complexity; quasimaximum likelihood algorithm; semidefinite programming; Binary phase shift keying; Bit error rate; Computational complexity; Lattices; MIMO; Maximum likelihood decoding; Polynomials; Receiving antennas; Signal to noise ratio; Transmitting antennas;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory, 2005. ISIT 2005. Proceedings. International Symposium on

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-9151-9

Type

conf

DOI

10.1109/ISIT.2005.1523632

Filename

1523632