Multidimensional frequency estimation using LU decomposition eigenvector–based algorithm

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Many algorithms have been proposed for multidimensional frequency estimation from a single snapshot or multiple snapshots of data mixture. Most of these algorithms fail when one or more identical frequencies are found in certain dimensions. In this paper, a multidimensional frequency estimation technique from a single datum snapshot is proposed. It applies LU decomposition (Gaussian Elimination) on an eigenvector-based algorithm for multidimensional frequency estimation. This proposed technique is simulated using a MATLAB code. The average root mean square error (RMSE) is investigated as a performance measure of the proposed technique. A comparison between original eigenvector-based (traditional) and the proposed techniques is introduced. The simulation results show that the RMSE of the proposed technique is less than the original one, and it has a more efficient solution for an identical frequency case but at the expense of complexity.

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Correspondence to Basel A. Ghreiwati.

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Table 1 List of parameters

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Omar, M.M.M., Eskaf, K.A. & Ghreiwati, B.A. Multidimensional frequency estimation using LU decomposition eigenvector–based algorithm. Ann. Telecommun. 75, 17–25 (2020).

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  • Eigenvalue decomposition
  • Least mean square errors
  • Multidimensional frequency estimation
  • Singular value decomposition