Computer Science – Information Theory
Scientific paper
2005-10-12
Computer Science
Information Theory
Submitted to IEEE Trans. on Info. Theory, Revised
Scientific paper
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.
Khandani Amir K.
Mobasher Amin
Sotirov Renata
Taherzadeh Mahmoud
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