In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given $SNR$ and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important ($SNR\ge 16$ dB).

中文翻译：

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基于累积量、二进制测量和内核的通道识别

在本文中，我们使用八种算法讨论了信道识别问题。前三种算法基于高阶累积量，后三种算法基于二进制输出测量，后两种算法基于再生核。本文的主要目的是研究所提出算法在不同情况下的性能，例如不同大小的数据输入或不同的信噪比。所提出的算法被应用于宽带无线电接入网络(BRAN)的信道参数的估计。仿真结果证实了所提出的算法能够以不同的精度估计信道参数，并且每种算法对于给定的情况都有其优点和缺点，例如对于给定的$秒N\mathrm{电阻}$和数据输入。最后，这项研究提供了在给定情况下可以选择哪些算法的想法。本文提出的研究表明，如果数据输入不可用（盲识别），基于累积量的算法更合适，但如果噪声不重要，基于内核和二进制测量的方法更合适（$秒N\mathrm{电阻}\ge 16$ D b）。