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A class of multi-modulus blind deconvolution algorithms using hyperbolic and Givens rotations for MIMO systems

机译:一类使用双曲线和Givens旋转的多模盲解卷积算法,用于MIMO系统

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This paper targets the blind deconvolution problem for multiple-input multiple-output communication systems, using small and moderate constellation's size signals, i.e. PSK and QAM. We introduce four different blind deconvolution algorithms based on four different techniques. These algorithms come as a natural extension of the successful work done by Shah et al in 2018 for blind source separation (BSS). The first two methods are considered as two-step based methods, where the first one performs the BSS for the spatio-temporal system followed by a pairing and sorting phase. While the second is accomplished by performing a cascaded linear equalization, using one of the existing subspace-methods, followed by the BSS routine. The third method is based on the minimization of a hybrid cost function, and the last one is a deflation-based method. These solutions summarize the main possible paths that can be followed to extend any of the existing instantaneous de-mixing algorithms. Experimental results are provided to compare and highlight the unique characteristics of each of the four different methods.
机译:本文针对多输入多输出通信系统的盲解卷积问题,使用小型和中等星座的尺寸信号,即PSK和QAM。我们介绍了基于四种不同技术的四种不同的盲解卷积算法。这些算法作为2018年Shah等人的成功工作的自然延伸,用于盲源分离(BSS)。前两种方法被认为是基于两步的方法,其中第一个方法对于时空系统执行BSS,然后是配对和分拣阶段。虽然通过执行级联线性均衡,但是使用现有子空间方法之一,然后是BSS例程来实现第二种。第三种方法基于混合成本函数的最小化,最后一个是基于通货紧缩的方法。这些解决方案总结了可以遵循的主要可能的路径,以扩展任何现有的瞬时解混算法。提供实验结果以比较和突出四种不同方法中的每种的独特特征。

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