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2023 | OriginalPaper | Chapter

A Novel Application of HPSOGWO Trained ANN in Nonlinear Channel Equalization

Authors : Pradyumna Kumar Mohapatra, Ravi Narayan Panda, Saroja Kumar Rout, Rojalin Samantaroy, Pradeep Kumar Jena

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

Publisher: Springer Nature Singapore

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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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Literature
1.
Voulgaris, P.G., & Hadjicostics, C. N. (2004). Optimal processing strategies for perfect reconstruction of binary signals under power-constrained transmission. In Proceedings of IEEE Conference on Decision and Control, Atlantis, Bahamas, Vol. 4, pp. 4040–4045.
2.
Touri, R., Voulgaris, P. G., & Hadjicostis, C. N. (2006) Time varying power limited preprocessing for perfect reconstruction of binary signals. In: Proceedings of the 2006 American control conference, Minneapdis, USA, 2006, pp. 5722–5727.
3.
Patra, J. C., Pal, R. N., Baliarsingh, R., & Panda, G. (1999). Nonlinear channel equalization for QAM signal constellation using artificial neural network. IEEE Transactions on Systems, Man, and Cybernetics Part B Cybetnetics, 29(2).
4.
Patra, J. C., Poh, W. B., Chaudhari, N. S., & Das, A. (2005). Nonlinear channel equalization with QAM signal using Chebyshev artificial neural network. In Proceedings of the International Joint Conference on Neural Networks, Montreal, Canada, 2005, pp. 3214–3219.
5.
Zhao, H., et al. (2011). A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems. Neural Networks, 24, 12–18.CrossRefMATH
6.
Zhao, H., et al. (2011). An adaptive decision feedback equalizer based on the combination of the FIR and FLNN. Digittal Signal Processings, 21, 679–689.CrossRef
7.
Zhao, H., et al. (2011). Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization. Information Sciences, 181, 3677–3692.CrossRef
8.
Zhao, H., et al. (2009). Adaptively combined FIR and functional link neural network equalizer for nonlinear communication channel. IEEE Transactions on Neural Networks, 20(4), 665–674.CrossRef
9.
Zhao, H., et al. (2012). Complex-valued pipelined decision feedback recurrent neural network for nonlinear channel equalization. IET Communications, 6(9), 1082–1096.MathSciNetCrossRef
10.
Zhao, H., et al. (2010). Adaptive reduced feedback FLNN nonlinear filter for active control of nonlinear noise processes. Signal Processings, 90(3), 834–847.CrossRefMATH
11.
Zhao, H., et al. (2010). Nonlinear adaptive equalizer using a pipelined decision feedback recurrent neural network in communication systems. IEEE Transactions on Communications, 58(8), 2193–2198.CrossRef
12.
Abiyev, R. H., et al. (2011). A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization. Applied Soft Computing, 11, 1396–1406.
13.
Panigrahi, S. P., Nayak, S. K., & Padhy, S. K. (2008). A genetic-based neuro-fuzzy controller for blind equalization of time-varying channels. Wiley Inter Science International Journal of Adaptive Control and Signal Processing, 22, 705–716.CrossRefMATH
14.
Yogi, S., Subhashini, K. R., & Satapathy, J. K. (2010). A PSO based functional link artificial neural network training algorithm for equalization of digital communication channels. In International Conference on Industrial and Information Systems (pp. 107–112).
15.
Chau, K. W. (2006). Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology, 329, 363–367.CrossRef
16.
Das, G., Pattnaik, P. K., & Padhy, S. K. (2014). Artificial neural network trained by particle swarm optimization for non-linear channel equalization. Expert Systems with Applications, 41(7), 3491–3496.CrossRef
17.
Lee, C. H., & Lee, Y. C. (2012). Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimisation algorithms. Information Sciences, 186(1), 59–72.CrossRef
18.
Lin, C.-J., & Liu, Y.-C. (2009). Image backlight compensation using neuro-fuzzy networks with immune particle swarm optimization. Expert Systems with Applications, 36(3, Part 1), 5212–5220.
19.
Lin, C. J., & Chen, C. H. (2011). Nonlinear system control using self-evolving neural fuzzy inference networks with reinforcement evolutionary learning. Applied Soft Computing, 11(8), 5463–5476.CrossRef
20.
Hong, W.-C. (2008). Rainfall forecasting by technological machine learning models. Applied Mathematics Computation, 200(1), 41–57.MathSciNetCrossRefMATH
21.
Potter, C., Venayagamoorthy, G. K., & Kosbar, K. (2010). RNN based MIMO channel prediction. Signal Processing, 90(2), 440–450.CrossRefMATH
22.
Ingle, K. K., & Jatoth, R. K. (2021). A new training scheme for neural network based non-linear channel equalizers in wireless communication system using Cuckoo Search Algorithm. AEU International Journal of Electronics and Communications, 138, 153371.
23.
Ingle, K. K., & Jatoth, R. K. (2020). An efficient JAYA algorithm with lévy flight for non-linear channel equalization. Expert Systems with Applications, 145, 112970.
24.
Panigrahi, S. P., Nayak, S. K., & Padhy, S. K. (2008). Hybrid ANN reducing training time requirements and decision delay for equalization in presence of co-channel interference. Applied Soft Computing, 8, 1536–1538.
25.
Mohapatra, P., Samantara, T., Panigrahi, S. P., & Nayak, S. K. (2018). Equalization of communication channels using GA-trained RBF networks. In Progress in Advanced Computing and Intelligent Engineering (pp. 491–499). Springer, Singapore.
26.
Panda, S., Mohapatra, P. K., & Panigrahi, S. P. (2015). A new training scheme for neural networks and application in non-linear channel equalization. Applied Soft Computing, 27, 47–52.CrossRef
27.
Mohapatra, P., Panda, S., & Panigrahi, S. P. (2018). Equalizer modeling using FFA trained neural networks. In Soft computing: Theories and applications (pp. 569–577). Springer, Singapore.
28.
Nanda, S. J., & Jonwal, N. (2017). Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm. Applied Soft Computing, 57, 197–209.CrossRef
29.
Rumelhart, D. E., Geoffey, E. H., & Ronald, J. W. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.CrossRefMATH
30.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, (p. 3943).
31.
del Valle, Y., Venayagamoorthy, G. K., Mohagheghi, S., Hernandez, J. C., & Harley, R. G. (2008). Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Transactions on Evolutionary Computation, 12, 171–195.CrossRef
32.
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61.CrossRef
33.
Talbi, E.-G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8(5), 541–564.CrossRef
34.
Zhao, H., Zeng, X., Zhang, J., Li, T., Liu, Y., & Ruan, D. (2011). Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization. Information Sciences, 181, 3677–3692.CrossRef
35.
Liang, J., & Ding, Z. (2004). FIR channel estimation through generalized cumulant slice weighting. IEEE Transactions on Signal Processing, 52(3), 657–667.MathSciNetCrossRefMATH
36.
Panigrahi, S. P., Santanu, K. N., & Sasmita, K. P. (2008a). A genetic-based neuro-fuzzy controller for blind equalization of time-varying channels. International Journal of Adaptive Control and Signal Processing, 22, 705–716. WileyInterscience.
Metadata
Title
A Novel Application of HPSOGWO Trained ANN in Nonlinear Channel Equalization
Authors
Pradyumna Kumar Mohapatra
Ravi Narayan Panda
Saroja Kumar Rout
Rojalin Samantaroy
Pradeep Kumar Jena
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of the 6th International Conference on Advance Computing and Intelligent Engineering

Print ISBN: 978-981-19-2224-4

Electronic ISBN: 978-981-19-2225-1

Copyright Year: 2023

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