In this correspondence, an approach to enhance the quality of superimposed training (ST) based channel estimation procedures is proposed. The approach is based on postprocessing the estimated channel. This postprocessing is performed with the projection of the estimated channel onto a set of orthogonal functions known as the Universal Basis (UB), that were defined in A. G. Orozco-Lugo, R. Parra-Michel, D. McLernon, and V. Kontorovitch, "Enhancing the Performance of the CR Blind Channel Estimation Algorithm Using the Karhunen-Loeve Expansion," Proceedings of the ICASSP, May 2002, pp. III-2653-III-2656. The projection leads to improved channel estimation when compared to raw ST methods. We demonstrate the enhanced performance of the proposed technique by means of both theoretical formulas and simulation results, focusing on data dependent ST.
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机译：在这篇文章中，提出了一种提高基于叠加训练（ST）的信道估计程序质量的方法。该方法基于对估计信道的后处理。这种后处理是通过将估计的信道投影到一组称为通用基 （UB） 的正交函数上来执行的，这些函数在 [A. G. Orozco-Lugo， R. Parra-Michel， D. McLernon， and V. Kontorovitch， “Enhancing the Performance of the CR Blind Channel Estimation Algorithm Using the Karhunen-Loeve Expansion，” Proceedings of the ICASSP， May 2002， 第III-2653-III-2656页]。与原始ST方法相比，该预测可改进通道估计。我们通过理论公式和仿真结果证明了所提技术的增强性能，重点关注数据依赖性ST。
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