Tensor-Based Blind Channel Identification

We propose a blind FIR channel identification method based on the parallel factor (Parafac) analysis of a 3rd-order tensor composed of the 4-th order output cumulants. Our algorithm is based on a single-step least squares (LS) minimization procedure instead of using classical three-step alternating...

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Bibliographic Details
Main Authors: Fernandes, C.E.R., Favier, G., Mota, J.C.M.
Format: Conference Proceeding
Language:English
Subjects:
Communications Society
Computer simulation
Equations
Finite impulse response filter
Higher order statistics
Laboratories
Least squares methods
Signal processing
Signal processing algorithms
Tensile stress
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Summary:We propose a blind FIR channel identification method based on the parallel factor (Parafac) analysis of a 3rd-order tensor composed of the 4-th order output cumulants. Our algorithm is based on a single-step least squares (LS) minimization procedure instead of using classical three-step alternating least squares (ALS) methods. Using a Parafac-based decomposition, we avoid any kind of pre-processing such as the prewhitening operation, which is mandatory in most methods using higher-order statistics. Our method retrieves the channel vector without any permutation or scaling ambiguities. In addition, we establish a link between the cumulant tensor decomposition and the joint-diagonalization approach. Computer simulations illustrate the performance gains that our method provides with respect to other classical solutions. Initialization and convergence issues are also addressed.
ISSN:1550-3607
1938-1883
DOI:10.1109/ICC.2007.453