{"title":"On Complexity-Reduced Implementation of Multi-Dimensional Wiener Interpolation Filtering","authors":"Huijun Li, A. Ibing","doi":"10.1109/VETECF.2010.5594450","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":417714,"journal":{"name":"2010 IEEE 72nd Vehicular Technology Conference - Fall","volume":"82 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE 72nd Vehicular Technology Conference - Fall","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VETECF.2010.5594450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.