Design of phase codes for radar performance optimization with a similarity constraint
IEEE Transactions on Signal Processing
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
Code design for radar STAP via optimization theory
IEEE Transactions on Signal Processing
PIC-based iterative SDR detector for OFDM systems in doubly-selective fading channels
IEEE Transactions on Wireless Communications
Probabilistic analysis of the semidefinite relaxation detector in digital communications
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Matrix-lifting semi-definite programming for detection in multiple antenna systems
IEEE Transactions on Signal Processing
Probabilistic Analysis of Semidefinite Relaxation for Binary Quadratic Minimization
SIAM Journal on Optimization
| Hi-index | 754.85 |
In multiple-input multiple-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, a quasi-ML algorithm based on semi-definite programming (SDP) is proposed. 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.