Abstract
With the increase in demand of high-speed data, the communication channels are suffering from inter-symbol interference, nonlinearity and noise. In this paper, digital channel equalizers are developed for finite impulse response (FIR) and infinite impulse response (IIR) channels. Supervised learning based on minimum mean square error is opted for FIR channels affected by noise and nonlinearity. For IIR channel, blind equalization based on constant modulus technique is employed. The weights of both type of equalizers are determined by a recently developed nature-inspired algorithm moth-flame optimization (MFO). The performance is compared with the same structure of equalizer trained with standard algorithms like: artificial immune system, adaptive particle swarm optimization, binary genetic algorithm and least mean square algorithm. Simulation studies are demonstrated for equalizers designed to compensate the response of two FIR channels, and both are affected by two nonlinearities with noise and three IIR channels. Superior performance by the proposed MFO equalizer is reported based on frequency response analysis, mean square error during training and BER plots during testing.
Similar content being viewed by others
References
K. Burse, R. Yadav, S. Shrivastava, Channel equalization using neural networks: a review. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 40(3), 352–357 (2010)
B. Widrow, S.D. Stearns, Adaptive Signal Processing (Prentice Hall, Englewood Cliffs, 1985)
A.H. Sayed, Fundamentals of Adaptive Filtering (Wiley, New York, 2003)
S. Chen, Y. Wu, Maximum likelihood joint channel and data estimation using genetic algorithms. IEEE Trans. Signal Process. 46(5), 1469–1473 (1998)
F.H.F. Leung, H.K. Lam, S.H. Ling, P.K.S. Tam, Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. Neural Netw. 14(1), 79–88 (2003)
G. Das, P.K. Pattnaik, S.K. Padhy, Artificial neural network trained by particle swarm optimization for non-linear channel equalization. Expert Syst. Appl. 41(7), 3491–3496 (2014)
A.T. Al-Awami, A. Zerguine, L. Cheded, A. Zidouri, W. Saif, A new modified particle swarm optimization algorithm for adaptive equalization. Digit. Signal Proc. 21(2), 195–207 (2011)
N. Iqbal, A. Zerguine, N. Al-Dhahir, Decision feedback equalization using particle swarm optimization. Sig. Process. 108, 112 (2015)
S. Han, W. Pedrycz, C. Han, Nonlinear channel blind equalization using hybrid genetic algorithm with simulated annealing. Math. Comput. Model. 41(67), 697–709 (2005)
B. Majhi, G. Panda, A. Choubey, On the development of a new adaptive channel equalizer using bacterial foraging optimization technique, in Annual IEEE India Conference, pp. 16, 2006
T.-J. Su, J.-C. Cheng, C.-J. Yu, An adaptive channel equalizer using self-adaptation bacterial foraging optimization. Opt. Commun. 283(20), 3911–3916 (2010)
S.J. Nanda, G. Panda, B. Majhi, Development of novel digital equalizers for noisy nonlinear channel using artificial immune system, in Proceedings of IEEE Region 10 and Third IEEE International Conference on Industrial and Information Systems, pp. 1–6, 2008
S.J. Nanda, Artificial immune systems: principle, algorithms and applications, in M. Tech Research Thesis, Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, 2009
T.S.D. Singh, A. Chatterjee, Mmse design of nonlinear volterra equalizers using artificial bee colony algorithm. Measurement 46(1), 210–219 (2013)
S. Panda, A. Sarangi, S.P. Panigrahi, A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization. AEU Int. J. Electron. Commun. 68(11), 1031–1036 (2014)
S. Pandey, R. Patidar, N.V. George, Design of a krill herd algorithm based adaptive channel equalizer, in IEEE International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), pp. 257–260, 2014
S.J. Nanda, N. Jonwal, Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm. Appl. Soft Comput. 57, 197–209 (2017)
S. Mirjalili, Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228–249 (2015)
W. Yamany, M. Fawzy, A. Tharwat, A.E. Hassanien, Moth-flame optimization for training multi-layer perceptrons, in Proceedings of 11th IEEE International Computer Engineering Confrence (ICENCO), pp. 267–272, 2015
H. Zhao, H. Zhao, S. Guo, Using GM(1 1) Optimized by MFO with rolling mechanism to forecast the electricity consumption of inner mongolia. Appl. Sci. 6(1), 20 (2016)
S.J. Nanda, Multi-objective moth flame optimization, in IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2470–2476, 2016
F. Chen, S. Kwong, C.W. Kok, Blind MMSE equalization of FIR/IIR channels using oversampling and multichannel linear prediction. ETRI J. 31(2), 162–172 (2009)
Y. Li, Z. Ding, Blind channel identification based on second order cyclostationary statistics, in Proceedings of IEEE International Conference of Acoustics, Speech, and Signal Processing, ICASSP-93, vol. 4, pp. 81–84, 1993
D. Godard, Self-recovering equalization and carrier tracking in two-dimensional data communication systems. IEEE Trans. Commun. 28(11), 1867–1875 (1980)
S. Haykin, Matched exponents for the representation of signals, adaptive filter theory (Prentice Hall, Upper Saddle River, 1996)
K.S. Tang, K.F. Man, S. Kwong, Z.F. Liu, Design and optimization of IIR filter structure using hierarchical genetic algorithms. IEEE Trans. Ind. Electron. 45(3), 481–487 (1998)
T. Arslan, D.H. Horrocks, A genetic algorithm for the design of nite word length arbitrary response cascaded IIR digital filters. Proceedings of 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA-1995) (1995), pp. 276–281
R.D.F. Attux, L.N. de Castro, F.J. Von Zuben, J.M.T. Romano, A paradigm for blind IIR equalization using the constant modulus criterion and an artificial immune network, in 13th IEEE Workshop on Neural Net-works for Signal Processing, NNSP’03, pp. 839–848, 2003
R. Johnson, P. Schniter, T.J. Endres, J.D. Behm, D.R. Brown, R.A. Casas, Blind equalization using the constant modulus criterion: a review. Proc. IEEE 86(10), 1927–1950 (1998)
H.H. Zeng, L. Tong, C.R. Johnson, An analysis of constant modulus receivers. IEEE Trans. Signal Process. 47(11), 2990–2999 (1999)
U.P. Shukla, S.J. Nanda, Parallel social spider clustering algorithm for high dimensional datasets. Eng. Appl. Artif. Intell. 56, 75–90 (2016)
A. Panda, S. Pani, An orthogonal parallel symbiotic organism search algorithm embodied with augmented Lagrange multiplier for solving constrained optimization problems. Soft Comput. 22(8), 2429–2447 (2018)
S.J. Nanda, G. Panda, Automatic clustering algorithm based on multi-objective immunized PSO to classify actions of 3D human models. Eng. Appl. Artif. Intell. 26, 1429–1441 (2013)
G. Panda, P.M. Pradhan, B. Majhi, IIR system identification using cat swarm optimization. Expert Syst. Appl. 38(10), 12671–12683 (2011)
E. Bai, M. Fu, Blind system identification and channel equalization of IIR systems without statistical information. IEEE Trans. Signal Process. 47(7), 1910–1921 (1999)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nanda, S.J., Garg, S. Design of Supervised and Blind Channel Equalizer Based on Moth-Flame Optimization. J. Inst. Eng. India Ser. B 100, 105–115 (2019). https://doi.org/10.1007/s40031-018-0361-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40031-018-0361-5