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A Novel Application of HPSOGWO Trained ANN in Nonlinear Channel Equalization

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Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 428))

Abstract

In a communication channel, there is a possibility of distortions such as ISI, CCI, and another source of noise that interfere with useful signals, and the signal becomes corrupted. Therefore, equalizers are needed to counter such types of distortions. In this paper, we presented a nature-inspired hybrid algorithm which is an amalgamation of PSO and GWO. The proposed algorithm is called HPSOGWO. During this work, we pertain to ANN trained with the proposed HPSOGWO in the channel equalization. The foremost initiative is to boost the flexibility of the variants of the proposed algorithm and the utilization of proper weight, topology, and transfer function of ANN in the channel equalization. The performance of the proposed equalizer can be evaluated by estimating MSE and BER by considering popular nonlinear channels and added with nonlinearities. Extensive simulations show the performance of our proposed equalizer, better than existing NN-based equalizers also as neuro-fuzzy equalizers.

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Author information

Authors and Affiliations

  1. Vedang Institute of Technology, Bhubaneswar, Odisha, India

    Pradyumna Kumar Mohapatra & Rojalin Samantaroy

  2. Vardhaman College of Engineering, Hyderabad, India

    Ravi Narayan Panda & Saroja Kumar Rout

  3. GITA, Bhubaneswar, Odisha, India

    Pradeep Kumar Jena

Authors
  1. Pradyumna Kumar Mohapatra
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  2. Ravi Narayan Panda
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  3. Saroja Kumar Rout
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  4. Rojalin Samantaroy
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  5. Pradeep Kumar Jena
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Corresponding author

Correspondence to Pradyumna Kumar Mohapatra .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India

    Bibudhendu Pati

  2. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, India

    Chhabi Rani Panigrahi

  3. University of California, Davis, CA, USA

    Prasant Mohapatra

  4. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Kumar Mohapatra, P., Narayan Panda, R., Kumar Rout, S., Samantaroy, R., Kumar Jena, P. (2023). A Novel Application of HPSOGWO Trained ANN in Nonlinear Channel Equalization. In: Pati, B., Panigrahi, C.R., Mohapatra, P., Li, KC. (eds) Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering. Lecture Notes in Networks and Systems, vol 428. Springer, Singapore. https://doi.org/10.1007/978-981-19-2225-1_15

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  • DOI: https://doi.org/10.1007/978-981-19-2225-1_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2224-4

  • Online ISBN: 978-981-19-2225-1

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