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Blind equalization in the presence of jammers and unknown noise: solutions based on second-order cyclostationary statistics

机译:存在干扰和未知噪声的情况下的盲均衡:基于二阶循环平稳统计的解决方案

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Addresses the blind identification of a linear time-invariant channel using some second-order cyclostationary statistics. In contrast to other contributions, the case where the second-order statistics of the noise and of the jammers are totally unknown is considered. It is shown that the channel can be identified consistently by adapting the so-called subspace method of Moulines et al. (1995). This adaptation is valid for fractionally spaced systems and, more interestingly, for the general systems exhibiting transmitter induced cyclostationarity introduced by Tsatsanis and Glannakis (1995). The new subspace method is based in both cases on a common tool, i.e., a general spectral factorization algorithm. The identifiability conditions are specified and some simulation examples are given.
机译:使用一些二阶循环平稳统计量解决线性时不变信道的盲目识别。与其他贡献相反,考虑了完全未知噪声和干扰的二阶统计的情况。结果表明,通过改编Moulines等人的所谓子空间方法,可以一致地识别信道。 (1995)。这种改编对于间隔较小的系统是有效的,更有趣的是,对于由Tsatsanis和Glannakis(1995)引入的具有发射机引起的循环平稳性的一般系统是有效的。在这两种情况下,新的子空间方法均基于通用工具,即通用频谱分解算法。指定了可识别性条件,并给出了一些仿真示例。

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