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On complexity-reduced implementation of multi-dimensional Wiener Interpolation Filtering

: Li, H.; Ibing, A.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Vehicular Technology Society -VTS-:
IEEE 72nd Vehicular Technology Conference - Fall, VTC 2010. Proceedings. Vol.2 : Ottawa, Ontario, Canada, 6 - 9 September 2010
New York, NY: IEEE, 2010
ISBN: 978-1-4244-3573-9
ISBN: 978-1-4244-3574-6
Vehicular Technology Conference (VTC Fall) <72, 2010, Ottawa>
Conference Paper
Fraunhofer HHI ()

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.