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Dergiler / Balkan Journal of Electrical and Computer Engineering / 2026 / Cilt: 8 Sayı: 1

Performance of Cellular Neural Network Based Channel Equalizers

Atilla Özmen(KADIR HAS UNIVERSITY)0000-0002-3868-1927
Baran Tander(KADIR HAS UNIVERSITY)0000-0002-9037-4668
Habib Şenol(KADIR HAS UNIVERSITY)0000-0001-5724-0839
Sayfa
1–6
Yayın Tarihi
2020-01-31
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Simulations are carried out to compare the proposed structures with minimum mean square error (MMSE) and multilayer perceptron (MLP) based equalizers.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Abstract—In this paper, a popular dynamic neural network structure called Cellular Neural Network (CNN) is employed as a channel equalizer in digital communications.
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Temel Bulgular & Veri:The architecture is a small-scaled, simple neural network containing only 25 neurons (cells) with a neighborhood of r = 2 , thus including only 51 weight coefficients.
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Sonuç & Katkı:Simulations are carried out to compare the proposed structures with minimum mean square error (MMSE) and multilayer perceptron (MLP) based equalizers.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Cellular Neural Networks · channel equalization · MLP equalizer

Abstract

Abstract—In this paper, a popular dynamic neural network structure called Cellular Neural Network (CNN) is employed as a channel equalizer in digital communications. It is shown that, this nonlinear system is capable of suppressing the effect of intersymbol interference (ISI) and the noise at the channel. The architecture is a small-scaled, simple neural network containing only 25 neurons (cells) with a neighborhood of r = 2 , thus including only 51 weight coefficients. Furthermore, a special technique called repetitive codes in equalization process is also applied to the mentioned CNN based system to show that the two-dimensional structure of CNN is capable of processing such signals, where performance improvement is observed. Simulations are carried out to compare the proposed structures with minimum mean square error (MMSE) and multilayer perceptron (MLP) based equalizers.

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Bu Makaleye Atıf Yap

Özmen, A., Tander, B., Şenol, H. (2020). Performance of Cellular Neural Network Based Channel Equalizers. Balkan Journal of Electrical and Computer Engineering, Cilt: 8 Sayı: 1, 1–6. https://doi.org/10.17694/bajece.519464

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Kaynakça (14)

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