Optimization of the loading factor of regularized estimated spatial-temporal wiener filters in large system case

Abstract : In this paper, it is established that the signal to interference plus noise ratio (SINR) produced by a trained regularized Wiener spatio-temporal filter can be estimated consistently in the asymptotic regime where the number of receivers and the number of snapshots converge to infinity at the same rate. The optimal regularization parameter is estimated as the argument of the maximum of the estimated SINR. Numerical simulations show that the proposed optimum regularized Wiener filter outperforms the existing regularized spatio-temporal Wiener filters
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Communication dans un congrès
IEEE Workshop on Statistical Signal Processing, Jun 2016, Palma de Majorque, Spain. 2016 IEEE Statistical Signal Processing Workshop (SSP), 〈10.1109/SSP.2016.7551725〉
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G Pham, P Loubaton. Optimization of the loading factor of regularized estimated spatial-temporal wiener filters in large system case. IEEE Workshop on Statistical Signal Processing, Jun 2016, Palma de Majorque, Spain. 2016 IEEE Statistical Signal Processing Workshop (SSP), 〈10.1109/SSP.2016.7551725〉. 〈hal-01616389〉

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