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International Conference on Contemporary Computing

IC3 2011: Contemporary Computing pp 403–414Cite as

Nonlinear Channel Equalization for Digital Communications Using DE-Trained Functional Link Artificial Neural Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 168))

Abstract

A major hindrance in the way of reliable and lossless communication is the inter symbol interference (ISI). To counter the effects of ISI and to have proper & reliable communication an adaptive equalizer can be employed at the receiver end. This paper considers the applications of artificial neural network structures (ANN) to the channel equalization problem. The problems related with channel nonlinearities and can be effectively subdued by application of ANNs. This paper contains a new approach to channel equalization using functional link artificial neural network (FLANN). In this paper we have incorporated the novel idea of utilizing an evolutionary technique called Differential Evolution (DE) for the training of FLANN we have compared the results with back propagation (BP) and Genetic Algorithm (GA) trained FLANNs. The comparison has been drawn based upon the minimum Mean Square Error (MSE) and Bit Error Rate (BER) performances. From this study it is evident that the DE trained FLANN performs better than the other types of equalizers.

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Author information

Authors and Affiliations

  1. Dept of ECE, ITER, S‘o’A University, Bhubaneswar, India

    Gyana Ranjan Patra

  2. Dept of ETCE, Jadavpur University, Kolkata, India

    Sayan Maity, Soumen Sardar & Swagatam Das

Authors
  1. Gyana Ranjan Patra
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  2. Sayan Maity
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  3. Soumen Sardar
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  4. Swagatam Das
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Editor information

Editors and Affiliations

  1. Iowa State University and IIT Bombay, India, 329 Durham, Ames, IA 5001, Iowa, USA

    Srinivas Aluru

  2. Indian Statistical Institute, 203 B.T. Road, 700 108, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. The Ohio State University, 3190 Graves Hall, 333 W 10th Ave, 43210, Columbus, OH, USA

    Umit V. Catalyurek

  4. Department of Computing Science, Chalmers University, Rännvagen 6B, 412 96, Göteborg, Sweden

    Devdatt P. Dubhashi

  5. Dept. of Electrical and Computer Engineering, Iowa State University, 329 Durham, IA 50011, Ames, USA

    Phillip H. Jones

  6. TASSL, Dept. of Electrical & Computer Engineering, Rutgers, The State University of New Jersey, Brett Road, NJ 08854-8058, Piscataway, USA

    Manish Parashar

  7. School of Computer Engineering, Nanyang Technological University, N4-02a-32 Nanyang Ave, 639798, Singapore

    Bertil Schmidt

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© 2011 Springer-Verlag Berlin Heidelberg

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Patra, G.R., Maity, S., Sardar, S., Das, S. (2011). Nonlinear Channel Equalization for Digital Communications Using DE-Trained Functional Link Artificial Neural Networks. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_41

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  • DOI: https://doi.org/10.1007/978-3-642-22606-9_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22605-2

  • Online ISBN: 978-3-642-22606-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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