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- W3170257148 abstract "Tensors are higher order generalization of vectors and matrices which can be used to describe signals indexed by more than two indices. This paper introduces a tensor framework for minimum mean square error (MMSE) estimation for multi-domain signals and data using the Einstein Product. The framework addresses both proper and improper complex tensors. The multi-domain nature of tensors has been harnessed to provide an augmented representation of improper complex tensors to account for covariance and pseudo-covariance. The classical notions of linear and widely linear MMSE estimators are extended to tensor case leading to the notion of multi-linear and widely multi-linear MMSE estimation. The Tucker product based n-mode Wiener filtering approach more commonly used in tensor estimation has been shown to be a special case of the proposed multi-linear MMSE estimation. An application of the tensor based estimation in a multiple antenna Orthogonal Frequency Division Multiplexing (MIMO OFDM) system is presented where the tensor formulation allows a convenient treatment of inter-carrier interference. A comparison between the tensor estimation and per sub-carrier estimation used for MIMO OFDM is presented which shows a significant performance advantage of using the tensor framework." @default.
- W3170257148 created "2021-06-22" @default.
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- W3170257148 date "2021-01-01" @default.
- W3170257148 modified "2023-10-16" @default.
- W3170257148 title "A Tensor Framework for Multi-Linear Complex MMSE Estimation" @default.
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- W3170257148 doi "https://doi.org/10.1109/ojsp.2021.3084541" @default.
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