Robust ISI equalization for uncertain channels via minimax optimal filtering

Ugrinovskii, Valery; Petersen, Ian R.

doi:10.1002/acs.887

Cited by 5 publications

(1 citation statement)

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“…In [25], norm-based perturbations on the matrices of a FIR MIMO channel model are considered and min-max MSE problems to find robust FIR equalizers are converted into SDLPs. In [26], a minimax formulation is considered with filtering performance assessed by the power of the estimation error and uncertainty classes of probability measures pertaining to signal and noise processes defined by a (time-indexed) family of KL-divergence constraints.…”

Section: Introductionmentioning

confidence: 99%

Robust MIMO Equalization for Non-Parametric Channel Model Uncertainty

Correa¹

2014

IEEE Trans. Signal Process.

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In this paper, three MIMO robust equalization problems are considered for non-parametric classes of channel models defined by weighted or balls (of frequency-responses) and performance criteria based on (variance) or norms of error signals. The approach pursued here centers on characterizing the worst-case performance of candidate equalizers, or upper bounds on it, by means of dual Lagrangian functionals. Then, for linearly parametrized, finite-dimensional classes of candidate equalizers, the corresponding robust equalization problems are converted into semi-definite linear programming problems for which approximate solutions can be effectively computed. A simple numerical example is presented, involving model uncertainty and error-variance performance, to illustrate, for various levels of uncertainty, the changes in the worst-case performances of the nominally optimal equalizer and of the one, in a specific linear class, which minimizes the worst-case error variance.

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Section: Introductionmentioning

confidence: 99%