Javier E. Kolodziej

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This paper proposes an improved stochastic model for the normalized least-mean-square (NLMS) algorithm considering correlated input signals obtained from a spherically invariant random process (SIRP). A SIRP describes both Gaussian and a wide class of non-Gaussian processes, including the ones with Laplacian, K 0 , and Gamma marginal density functions.(More)
This paper presents an improved statistical model for the transform-domain LMS algorithm operating in non-stationary environment from a time-varying plant. The stationary case is also considered as a particular case of the non-stationary one. The derived model takes into account a fixed-length sliding window for estimating the transformed input signal(More)
This paper presents an analytical model for the constrained stochastic gradient (CSG) algorithm. This algorithm is used to obtain the weights of antenna arrays for mobile communications, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of such systems. For the algorithm performance evaluation, several operating conditions are considered(More)
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