A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming

Amin Mobasher (amincst.uwaterloo.ca)
Mahmoud Taherzadeh (taherzadcst.uwaterloo.ca)
Renata Sotirov (rsotirovms.unimelb.edu.au)
Amir K. Khandani (khandanicst.uwaterloo.ca)

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. The proposed relaxation models are also used for soft output decoding in MIMO systems.

Keywords: semi-definite programming, decoding, multiple antenna, maximum likelihood, binary quadratic programming

Category 1: Applications -- OR and Management Sciences (Telecommunications )

Category 2: Integer Programming (0-1 Programming )

Category 3: Nonlinear Optimization (Quadratic Programming )

Citation: Technical Report UW-E&CE#2005-12 Jul 30, 2005 University of Waterloo, Waterloo, On, Canada, N2L3G1

Download: [Postscript][PDF]

Entry Submitted: 10/03/2005
Entry Accepted: 10/05/2005
Entry Last Modified: 10/05/2005

Modify/Update this entry