Abstract
This work proposes a modified particle swarm optimization (PSO) as an adaptive algorithm to search for optimum equalizer weights of transversal and decision feedback equalizers. Inertia weight is one of the PSO’s critical parameters which manage the search abilities of PSO. Higher values of inertia weight improve the global search, whereas smaller values improve the local search with faster convergence. Different approaches are reported in literature to improve PSO by modifying the inertia weight. This work analyzes the performance of the existing modified PSO algorithms with different time-varying inertia weight strategies and proposes two new strategies. Detailed simulations present the enhanced performance characteristics of the proposed algorithms in transversal and decision feedback models. Also the simulation work analyzes the performance in linear and nonlinear channel conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qureshi S (1985) Adaptive equalization. Proc IEEE 73:1349–1387
Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice-Hall, Inc
Shin HC, Saved AH (2004) Mean-square performance of a family of affine projection algorithms. IEEE Trans Signal Process 52:90–101
Karaboga N, Cetinkaya B (2011) A novel and efficient algorithm for adaptive filtering: artificial bee colony algorithm. Turk J Electr Eng Comput Sci 19(1):175–190
Krusienski DJ (2004) Enhanced structured stochastic global optimization algorithms for IIR and nonlinear adaptive filtering. Ph.D. thesis, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA
Al-Awami AT, Zerguine A, Cheded L, Zidouri A, Saif W (2011) A new modified particle swarm optimization algorithm for adaptive equalization. J Digital Signal Process 21(2):195–207
Das G, Pattnaik PK, Padhy SK (2014) Artificial neural network trained by particle swarm optimization for non-linear channel equalization. J Exp Syst Appl 41(7):3491–3496
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol 4, pp 1942–1948
Nickabadi A, Ebadzadeh MM, Safabakhsh R (2011) A novel particle swarm optimization algorithm with adaptive inertia weight. J Appl Soft Comput 11(4):3658–3670
Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the international conference on evolutionary computation, IEEE world congress on computational intelligence, pp 69–73
Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation, vol 1, pp 84–88
Chatterjee A, Siarry P (2006) Nonlinear inertia weight variation for dynamic adaption in particle swarm optimization. Comput Oper Res 33(3):859–871
Feng Y, Teng G, Wang A, Yao YM (2007) Chaotic inertia weight in particle swarm optimization. In: Second international conference on innovative computing, information and control (ICICIC 07), pp 475–1475
Feng Y, Yao YM, Wang A (2007) Comparing with chaotic inertia weights in particle swarm optimization. In: International conference on machine learning and cybernetics, pp 329–333
Lei K, Qiu Y, He Y (2006) A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization. In: ISSCAA
Fan S, Chiu Y (2007) A decreasing inertia weight particle swarm optimizer. Eng Optim 39(2):203–228
Zheng Y, Ma L, Zhang L, Qian J (2003) Empirical study of particle swarm optimizer with an increasing inertia weight. In: IEEE congress on evolutionary computation
Jiao B, Lian Z, Gu X (2008) A dynamic inertia weight particle swarm optimization algorithm. Chaos Solitons Fractals 37(3):698–705
Patra JC, Pal RN, Baliarsingh R, Panda G (1999) Nonlinear channel equalization for QAM signal constellation using artificial neural networks. IEEE Trans Syst Man Cybern 29(2):262–271
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Diana, D.C., Joy Vasantha Rani, S.P. (2017). Modified PSO-Based Equalizers for Channel Equalization. In: Nath, V. (eds) Proceedings of the International Conference on Nano-electronics, Circuits & Communication Systems. Lecture Notes in Electrical Engineering, vol 403. Springer, Singapore. https://doi.org/10.1007/978-981-10-2999-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-10-2999-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2998-1
Online ISBN: 978-981-10-2999-8
eBook Packages: EngineeringEngineering (R0)