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The first example simulates the SINR performance affected by norm bound of steering vector error.
The norm bound of steering vector error is set as ε IWCB =0.1.
This is because elements of steering vector of virtual array correspond to elements of virtual correlation matrix.
The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty.
The actual norm bound of steering vector error corresponding to 2° pointing error is about ε 0=1.96.
This paper only concerns about the error of steering vector, so we use actual covariance matrix R in all of the following formulas.
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projection of steering vectors on π n (cf. Equation 16). .
projection of steering vectors on R ̂ − 1 (cf. Equation 14). .
R ̂ estimation by smoothing; EVD; projection of steering vectors on π n (cf. Equation 16).
Furthermore, A is a N × M matrix of steering vectors, which is named manifold matrix.
R ̂ estimation by smoothing; R ̂ inversion; projection of steering vectors on R ̂ − 1 (cf. Equation 14).
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