Soft-Heuristic Detectors for Large MIMO Systems

We propose low-complexity detectors for large MIMO systems with BPSK or QAM constellations. These detectors work at the bit level and consist of three stages. In the first stage, maximum likelihood decisions on certain bits are made in an efficient way. In the second stage, soft values for the remaining bits are calculated. In the third stage, these remaining bits are detected by means of a heuristic programming method for high-dimensional optimization that uses the soft values (“soft-heuristic” algorithm). We propose two soft-heuristic algorithms with different performance and complexity. We also consider a feedback of the results of the third stage for computing improved soft values in the second stage. Simulation results demonstrate that, for large MIMO systems, our detectors can outperform state-of-the-art detectors based on nulling and canceling, semidefinite relaxation, and likelihood ascent search.