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Adaptive stream mapping multi antenna systems with low complexity iterative detection

机译:低复杂度迭代检测的自适应流映射多天线系统

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In this paper, we propose an adaptive linear precoding technique combined with low complexity iterative detection which can significantly improve the performance of spatial multiplexing multiple input, multiple output (MIMO) systems. By allowing different data stream mappings to the transmit antennas, linear precoding techniques can provide full diversity and increased capacity gain, assuming maximum likelihood (ML) detection is used. There has been previous work on selection criteria to select the appropriate precoding matrix to perform the stream mapping, based on ML or minimum mean square error (MMSE) detection. However these selection criteria were designed under uncoded conditions. In this paper we present a selection criterion based on the Extrinsic information transfer (EXIT) chart specifically designed for a reduced complexity iteratively detected coded system with interference cancellation detection. The results show that the detector maintains full diversity and has performance within 0.4 dB of the ML detector. Moreover precoding increases performance by 1.2 dB compared to the ML detector with fixed stream mapping. In addition it outperforms the SNR criterion from previous work.
机译:在本文中,我们提出了一种自适应线性预编码技术,该技术与低复杂度迭代检测相结合,可以显着提高空间复用多输入多输出(MIMO)系统的性能。假设使用最大似然(ML)检测,通过允许不同的数据流映射到发射天线,线性预编码技术可以提供完整的分集和增加的容量增益。先前已经有关于选择标准的工作,该选择标准基于ML或最小均方误差(MMSE)检测来选择适当的预编码矩阵以执行流映射。但是,这些选择标准是在未编码条件下设计的。在本文中,我们提出了一种基于外部信息传输(EXIT)图的选择标准,该选择标准是专为具有干扰消除检测功能的降低复杂性的迭代检测编码系统而设计的。结果表明,该检测器保持了全部分集,并且性能比ML检测器低0.4 dB。此外,与具有固定流映射的ML检测器相比,预编码将性能提高了1.2 dB。另外,它优于以前工作中的SNR标准。

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