In this paper, we propose a novel low-complexity, near-optimal soft-input soft-output detector for N × M multiple-input multiple-output (MIMO) systems. Our algorithm is based on the combination of minimum mean square error decision feedback equalization (MMSE-DFE) and conditional optimization. In a round-robin fashion, one symbol is detected using exhaustive search in such a way that all N × (M - 1) submatrices of the baseband channel matrix are considered and the one with the best metric is chosen. This search over all columns of the channel matrix, which can be performed in parallel, has the advantages of improving the performance of the hard-output version of the detector, and refining the list of candidates for efficient implementation of the soft-output detector for MIMO systems with error correcting codes. In particular, it is shown that the error performance of the soft-output system is comparable to that of the list sphere decoder (LSD) but with much smaller list size, and hence smaller complexity than the latter. We also analyze the performance and complexity of the proposed algorithm and discuss different techniques to further reduce its complexity without affecting the performance. Finally, the obtained theoretical results are validated via simulations.

In this paper, we propose a novel low-complexity, near-optimal detector for multiple-input multiple-output (MIMO) systems. We consider the combination of minimum mean square error generalized decision feedback equalization (MMSE-GDFE) and conditional optimization. All submatrices of QR decomposition of regularized baseband channel matrix are considered in a round-robin fashion for detecting one symbol using exhaustive search over the constellation points. Among all possible candidates, the one with the best metric is chosen. This search over all columns of the channel matrix, which can be performed in parallel, improves the error performance of the hard-output version of the detector. Additionally, the list of candidates can be utilized for efficient implementation of the soft-output detector for MIMO systems with error correcting codes. We also discuss different techniques to further reduce the complexity of the proposed algorithm without affecting the performance. Finally, the performance of the proposed detector is analyzed and the obtained theoretical results are validated via simulations.

In this paper, we propose an efficient detector for large-scale multi-input multi-output (MIMO) systems. Our proposed detector is mainly based on conditional (or subspace) detection and partial lattice reduction (PLR) algorithm tailored to an appropriate channel ordering. The conditional detection for finding one symbol through an exhaustive search over the constellation points is considered. Then, part of the channel is reduced by the PLR technique in order to compensate for the degradation of diversity due to limiting the exhaustive search levels to one. In order to improve the error performance, all other remaining non-reduced columns are also considered for conditional detection. In the proposed detector, part of the detection complexity is transferred to the preprocessing stage where the PLR algorithm is implemented. Consequently, for quasi-static channels where the channel is constant over a long block, the cost of PLR can be negligible. The improvement over the error performance of the state-of-the-art detection schemes is investigated for some large-scale MIMO systems.