多维维纳插值滤波的降低复杂度实现

Huijun Li, A. Ibing
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引用次数: 0

摘要

维纳插值滤波器通常用于从噪声样本中重构随机过程。重点讨论了一个多维随机过程的情况,并给出了该滤波器在无线接收机移动无线电传播信道估计中的应用实例。我们表明,通过利用两个特性可以大大降低实现的计算复杂性:首先,多维维纳滤波通常是不可分的,而如果样本结构是晶格,则插值的上采样是可分的-因此将这两个步骤分开是有益的。其次,利用部分重叠的多维块的频谱整形(快速卷积、重叠添加或重叠保存方法)实现维纳滤波。我们讨论了在OFDM (2D信道相关)和MIMO-OFDM (3D信道相关)传输中估计时变信道传递函数的应用性能和复杂性,用于改变信道自相关值(WSSUS模型)和滤波器核大小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Complexity-Reduced Implementation of Multi-Dimensional Wiener Interpolation Filtering
The Wiener Interpolation Filter is commonly used to reconstruct a stochastic process from noisy samples. We focus on the case of a multi-dimensional stochastic process and the practical example of application of the filter for estimation of mobile radio propagation channels at a wireless receiver. We show that computational complexity of the implementation can be considerably reduced by exploiting two properties: first, multidimensional Wiener filtering is in general non-separable, while upsampling for interpolation is separable if the sample structure is a lattice - so it is beneficial to separate the two steps. Second, Wiener filtering can be implemented using spectral shaping of partially overlapping multidimensional blocks (fast convolution, overlap-add or overlap-save method). We discuss performance and complexity of the application to estimate the time-variant channel transfer function in OFDM (2D channel correlation) and MIMO-OFDM (3D channel correlation) transmission, for varying channel autocorrelation values (WSSUS model) and filter kernel sizes.
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