基于ML准则的时空线性预编码器的特征模信噪比增加方法

S. Narieda, K. Yamashita
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引用次数: 0

摘要

本文提出了一种基于极大似然准则的时空线性预编码器(ML- stlp)的特征模信噪比增加方法。该方法在一帧中使用多个ML-STLP,而传统方法只使用一个ML-STLP。当使用两个以上的ml - stlp时,相同的ml - stlp(前面的符号长度较短)被并行排列。此外,该方法与传统方法在一帧的前置符号长度和数据速率上是相同的。我们研究了前导符号长度对结果特征模信噪比的影响,并表明根据信道条件选择具有较高特征模信噪比的前导符号长度可以实现特征模信噪比的增加。最后,通过计算机仿真验证了该方法的适用性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Eigenmode SNR increasing method for ML criterion based space-time linear precoder
In this paper, we propose an eigenmode SNR increasing method for a maximum likelihood (ML) criterion based space-time linear precoder (ML-STLP). In the proposed method, several ML-STLPs are employed for one frame whereas only one ML-STLP is employed in the conventional method. When over two ML-STLPs are employed, the same ML-STLPs, which have short preceded symbol length, are arranged in parallel. Also, a length of preceded symbol for one frame and a data rate are same between the proposed method and the conventional method. We investigate the effect of the preceded symbol length on the resulting eigenmode SNR and show that the eigenmode SNR increasing can be achieved by selecting the preceded symbol length with higher eigenmode SNR depending on the channel condition. Also, the applicability of the proposed method is demonstrated by a computer simulation
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