An efficient soft MIMO detection based on differential METRICS

The multiple-input multiple-output (MIMO) technology can make full use of spectrum and increase the communication throughput. In the coded MIMO system, the main challenge of soft detection is to efficiently generate the log-likelihood ratios (LLR) values for channel decoder. The exact maximum a posteriori (MAP) probability detection can guarantee the optimal performance, but its realization is difficult due to its enormous complexity. In this paper, we propose the efficient soft detection algorithms based on differential metrics. We apply the differential metrics for the list sphere decoding, and propose the list gradient algorithm. We further propose a novel algorithm that can generate the values of LLR and provide a trade-off between performance and complexity. The proposed algorithms do not need the QR decomposition and matrix inversion. The proposed algorithms have fixed complexity, and are appropriate for pipelined hardware implementation. The numerical results verify the efficiency of our algorithms.