A computationally efficient soft-output Lattice reduction-aided selective spanning sphere decoder for wireless MIMO systems

In recent years, the algorithmic optimizations and implementations of near-optimal Multiple-Input Multiple-Output (MIMO) detectors have been an area of active research. Lattice Reduction (LR) has shown to be a promising technique to improve the performance of linear MIMO detectors. However, LR-aided linear hard-output MIMO detection is still far from optimal. Practical systems use soft-output information to exploit gains from coded systems in order to yield near-optimal performance. In this paper, the LR-aided Selective Spanning Sphere Detection algorithm is proposed as a reduced-complexity candidate list generation method for soft-output MIMO detection, specifically optimized for practical MIMO-OFDM systems. This algorithm uses efficient and scalable heuristics based on simple processor-friendly operations that significantly contribute to lowering the computational complexity of the MIMO detection problem. Results from Monte Carlo simulations reveal that LR-aided SSSD is a promising algorithm that is capable of providing near-optimal performance whilst being especially computationally efficient, in comparison to other algorithms.