Researcher:
Kızılkale, Can

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PhD Student

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Can

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Kızılkale

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Kızılkale, Can

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2004 - 20053

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Researcher Of Publications: search.filters.isAuthorOfPublication.8b2c4b1c-889c-4b1a-8275-b633b19fb419

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Now showing 1 - 3 of 3
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    A fast blind equalization method based on subgradient projections
    (IEEE, 2004) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Kızılkale, Can; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    A novel blind equalization method based on a subgradient search over a convex cost surface is proposed. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA) which mostly suffer from the convergence problems caused by their non-convex cost functions. The proposed method is an iterative algorithm, for both real and complex constellations, with a very simple update rule that minimizes the l(infinity) norm of the equalizer output under a linear constraint on the equalizer coefficients. The algorithm has a nice convergence behavior attributed to the convex l(infinity) cost surface. Examples are provided to illustrate the algorithm's performance.
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    Fast and low complexity blind equalization via subgradient projections
    (Ieee-Inst Electrical Electronics Engineers Inc, 2005) Department of Electrical and Electronics Engineering; N/A; Department of Electrical and Electronics Engineering; Erdoğan, Alper Tunga; Kızılkale, Can; Faculty Member; PhD Student; College of Engineering; Graduate School of Sciences and Engineering; 41624; N/A
    We propose a novel blind equalization method based on subgradient search over a convex cost surface. This is an alternative to the existing iterative blind equalization approaches such as the Constant Modulus Algorithm (CMA), which often suffer from the convergence problems caused by their nonconvex cost functions. The proposed method is an iterative algorithm called SubGradient based Blind Algorithm (SGBA) for both real and complex constellations, with a very simple update rule. It is based on the minimization of the l(infinity) norm of the equalizer output under a linear constraint on the equalizer coefficients using subgradient iterations. The algorithm has a nice convergence behavior attributed to the convex l(infinity) cost surface as well as the step size selection rules associated with the subgradient search. We illustrate the performance of the algorithm using examples with both complex and real constellations, where we show that the proposed algorithm's convergence is less sensitive to initial point selection, and a fast convergence behavior can be achieved with a judicious selection of step sizes. Furthermore, the amount of data required for the training of the equalizer is significantly lower than most of the existing schemes.
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    A moving window approach for blind equalization using subgradient projections
    (IEEE, 2004) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Kızılkale, Can; Erdoğan, Alper Tunga; PhD Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 41624
    A novel blind equalization method based on a subgradient search over a convex cost surface is examined under a noisy channel and a modification is proposed. This is an alternative to the existing iterative blind equalization approaches such as Constant Modulus Algorithm (CMA) which mostly suffer from the convergence problems caused by their non-convex cost functions. The proposed method is an iterative algorithm, for both real and complex constellations, with a very simple update rule that minimizes the l(infinity) norm of the equalizer output under a linear constraint on the equalizer coefficients. The subgradient based algorithm has a fast convergence behavior attributed to the convex l(infinity) cost surface. A moving window based approach is used in this algorithm to both decrease algorithm's complexity and increase its immunity to noise. / Bu makalede alt-bayır izdüşümleri kullanılarak yapılan kör eşitleme metodunun gürültülü bir kanal için performansı incelenmiş ve bu performansın arttırılması için bir öneride bulunulmuştur. Bu algoritma daha önce önerilen sabit genlik algoritmasi(CMA) gibi özyineli yöntemlere bir alternatif olarak sunulmaktadır. Bilindiği gibi daha once sunulan algoritmalar dışbükey olmayan maliyet işlevlerinden dolayı yakınsallık problemi yaşamaktadırlar. Önerilen yöntem, hem gerçek hem de karmaşık burçlar (constellation) için, denkleştirici katsayıları üzerindeki doğrusal bir kısıt altında denkleştiricinin çıktısını l(infinity), normunu enküçültme esasına dayalı, basit bi güncelleme yapısına sahip özyinelemeli bir algoritmadır. Bu algoritma l(infinity) maliyet yüzeyinin karakterinden dolayı hızlı yakınsama davranışına sahiptir. Algoritmanin hem karmaşıklığını azaltacak hem de gürültüye karşı bağışıklığını yükseltecek hareketli pencereye dayalı bir yapı kullanılmıştır.