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Independent component analysis (ICA) for blind equalization of frequency selective channels

机译：独立分量分析（ICA）用于频率选择信道的盲均衡

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In this paper we address the problem of blind source separation (BSS) in frequency selective multiple-input multiple-output (MIMO) channels, when the only available prior knowledge about the transmitted signals is their mutual statistical independence. The novelty of the paper is two-fold. Firstly, we analytically show that when orthogonal frequency division multiplexing (OFDM) is employed, the original BSS problem is transformed into a set of standard ICA problems with complex mixing matrices. Each ICA problem is associated with one of the orthogonal subcarriers. Secondly, we show that the statistical correlation between the different frequency bins (at each orthogonal subcarrier) can be exploited to avoid the frequency-bin dependent permutation and scaling problems, which are intrinsic to the ICA solution. Our approach is also tested on a realistic channel model.
机译：在本文中，当关于传输信号的唯一可用先验知识是它们的相互统计独立性时，我们解决了频率选择性多输入多输出（MIMO）信道中的盲源分离（BSS）问题。论文的新颖性是双重的。首先，我们分析表明，当采用正交频分复用（OFDM）时，原始的BSS问题被转换为具有复杂混合矩阵的一组标准ICA问题。每个ICA问题与正交子载波之一相关联。其次，我们表明可以利用不同频率点之间（在每个正交子载波处）之间的统计相关性来避免ICA解决方案固有的频率点相关的排列和缩放问题。我们的方法也在真实的渠道模型上进行了测试。

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