A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming
<|reference_start|>A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on Semi-Definite Programming: 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.<|reference_end|>
@article{mobasher2005a,
    title={A Near Maximum Likelihood Decoding Algorithm for MIMO Systems Based on
  Semi-Definite Programming},
    author={Amin Mobasher, Mahmoud Taherzadeh, Renata Sotirov, and Amir K.
  Khandani},
    journal={arXiv preprint arXiv:cs/0510030},
    year={2005},
    number={UW-E&CE#2005-12},
    archivePrefix={arXiv},
    eprint={cs/0510030},
    primaryClass={cs.IT math.IT}
}