Researcher:
Kalantarova, Nargiz

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PhD Student

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Nargiz

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Kalantarova

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Kalantarova, Nargiz

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2011 - 20134

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Researcher Of Publications: search.filters.isAuthorOfPublication.bd37f612-5f1e-415e-84a3-ce5d2cfa912c

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    Robust turbo equalization under channel uncertainties
    (IEEE, 2011) N/A; Department of Electrical and Electronics Engineering; N/A; Department of Electrical and Electronics Engineering; Kozat, Süleyman Serdar; Kalantarova, Nargiz; Erdoğan, Alper Tunga; Faculty Member; PhD Student; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; College of Engineering; 177972; N/A; 41624
    Robust turbo equalization over discrete time channels with inter-symbol interference (ISI) in the presence of channel uncertainties is investigated. The turbo equalization framework proposed in this paper contains a linear equalizer (LE) and a trellis based decoder. In this framework, a minimax scheme and a competitive scheme are studied, which incorporate the uncertainty in channel information into equalizer design in order to improve robustness. The validation of the performance improvement gained by the proposed algorithms are demonstrated through simulations.
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    Competitive least squares problem with bounded data uncertainties
    (IEEE, 2012) N/A; Department of Electrical and Electronics Engineering; N/A; N/A; Kozat, Süleyman Serdar; Dönmez, Mehmet Ali; Kalantarova, Nargiz; Faculty Member; Master Student; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 177972; N/A; N/A
    We study robust least squares problem with bounded data uncertainties in a competitive algorithm framework. We propose a competitive least squares (LS) approach that minimizes the worst case “regret” which is the difference between the squared data error and the smallest attainable squared data error of an LS estimator. We illustrate that the robust least squares problem can be put in an SDP form for both structured and unstructured data matrices and uncertainties. Through numerical examples we demonstrate the potential merit of the proposed approaches.
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    Linear MMSE-optimal turbo equalization using context trees
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Kim, Kyeongyeon; Kozat, Suleyman Serdar; Singer, Andrew C.; N/A; Kalantarova, Nargiz; PhD Student; Graduate School of Sciences and Engineering; N/A
    Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean-square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer, and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.
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    Nonlinear turbo equalization using context trees
    (IEEE, 2011) Kim, Kyeongyeon; Singer, Andrew C.; Department of Electrical and Electronics Engineering; N/A; Kozat, Süleyman Serdar; Kalantarova, Nargiz; Faculty Member; PhD Student; Department of Electrical and Electronics Engineering; College of Engineering; Graduate School of Sciences and Engineering; 177972; N/A
    In this paper, we study adaptive nonlinear turbo equalization to model the nonlinear dependency of a linear minimum mean square error (MMSE) equalizer on soft information from the decoder. To accomplish this, we introduce piecewise linear models based on context trees that can adaptively choose both the partition regions as well as the equalizer coefficients in each region independently, with the computational complexity of a single adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer that can choose both its regions as well as its filter parameters based on observing the whole data sequence in advance.