A new multiple-input multiple-output (MIMO) detection scheme combining minimum-mean-square-error (MMSE) detection and the K-best detection algorithm is proposed. The proposed scheme leverages the MMSE detection results to ease the demand of a large $K$ in the conventional K-best algorithm to achieve satisfactory performance. The post-detection SNR obtained after MMSE detection is consulted to determine the symbols upon which a reduced-dimension K-best algorithm (h-best algorithm) is performed to obtain final detection results. Parameters associated with the proposed scheme are empirically chosen to make a fair comparison with the conventional K-best algorithm. Extensive Monte Carlo simulation demonstrates that the hybrid approach exhibits significant performance gain over both MMSE and K-best detection schemes.
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机译：提出了一种结合最小均方误差（MMSE）检测和K最佳检测算法的多输入多输出（MIMO）检测方案。提出的方案利用MMSE检测结果来缓解传统K-best算法对大$ K $的需求，以实现令人满意的性能。查阅MMSE检测之后获得的检测后SNR，以确定执行降维K最佳算法（h最佳算法）以获得最终检测结果的符号。根据经验选择与所提出的方案相关的参数，以便与传统的K-最佳算法进行公平的比较。广泛的蒙特卡洛仿真表明，与MMSE和K最佳检测方案相比，混合方法具有显着的性能提升。
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