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Blind channel identification algorithms based on the Parafac decomposition of cumulant tensors: The single and multiuser cases

机译:基于累积量张量的Parafac分解的盲信道识别算法:单用户和多用户情况

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In this paper, we exploit the symmetry properties of 4th-order cumulants to develop new blind channel identification algorithms that utilize the parallel factor (Parafac) decomposition of cumulant tensors by solving a single-step (SS) least squares (LS) problem. We first consider the case of single-input single-output (SISO) finite impulse response (FIR) channels and then we extend the results to multiple-input multiple-output (MIMO) instantaneous mixtures. Our approach is based on 4th-order output cumulants only and it is shown to hold for certain underdetermined mixtures, i.e. systems with more sources than sensors. A simplified approach using a reduced-order tensor is also discussed. Computer simulations are provided to assess the performance of the proposed algorithms in both SISO and MIMO cases, comparing them to other existing solutions. Initialization and convergence issues are also addressed.
机译:在本文中,我们利用四阶累积量的对称性来开发新的盲通道识别算法,该算法通过解决单步(SS)最小二乘(LS)问题来利用累积量张量的并行因子(Parafac)分解。我们首先考虑单输入单输出(SISO)有限脉冲响应(FIR)通道的情况,然后将结果扩展到多输入多输出(MIMO)即时混合。我们的方法仅基于四阶输出累积量,并且已证明适用于某些不确定的混合物,即源比传感器多的系统。还讨论了使用降阶张量的简化方法。提供计算机仿真以评估所提出算法在SISO和MIMO情况下的性能,并将其与其他现有解决方案进行比较。还解决了初始化和收敛问题。

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