Low-Complexity Likelihood Information Generation for Spatial-Multiplexing MIMO Signal Detection

Signal detection algorithms providing likelihood information for coded spatial-multiplexing multiple-input-multiple-output (MIMO) wireless communication systems pose a critical design challenge due to their prohibitively high computational complexity. In this paper, we present a low-complexity soft-output detection algorithm by adopting four implementation-friendly algorithm-level improvements to the fixed-complexity sphere decoder. More specifically, we introduce a reliability-dependent tree expansion approach and an on-demand list-size reduction scheme for low-cost candidate list generation. In terms of performance improvement, we apply an early bit-flipping strategy and utilize the l¹-norm distance representation. The algorithm is evaluated by computer simulations performed over Rayleigh flat fading channels and computational complexity analysis. Compared with the soft-output K-Best algorithm, the proposed algorithm saves at least 60% of the computations for detecting 4 × 4 64-quadrature amplitude modulation (QAM) MIMO signal and, at the same time, provides better detection performance, making it a promising detection scheme for real-life hardware implementation.