A new training scheme for neural networks and application in non-linear channel equalization

A Recently proposed DSO trained ANN used for the problem of channel equalization.This paper introduced a novel strategy for equalization of nonlinear channels using this DSO trained neural network.Proposed method of channel equalization performs better than contemporary equalization methods used in the literature. This paper deals with the problem of equalization of channels in a digital communication system. In the literature, artificial neural network (ANN) has been increasingly used for the said problem. However, traditional methods of ANN training fall short of desired performance in the problem of equalization. In this paper, we propose a recently proposed training method for ANN for the problem. This training uses directed search optimization (DSO) as a trainer to neural networks. Then, we apply the same to the problem of nonlinear channel equalization and in that way, this paper introduces a novel strategy for equalization of nonlinear channels. Proposed method of channel equalization performs better than contemporary equalization methods used in the literature, as evident from extensive simulation results presented in this paper.

[1]  Sasmita Kumari Padhy,et al.  Dynamic task scheduling using a directed neural network , 2015, J. Parallel Distributed Comput..

[2]  Andries Petrus Engelbrecht,et al.  Training feedforward neural networks with dynamic particle swarm optimisation , 2012, Swarm Intelligence.

[3]  N. Burum,et al.  Using particle swarm optimization in training neural network for indoor field strength prediction , 2009, 2009 International Symposium ELMAR.

[4]  Armando Blanco,et al.  A real-coded genetic algorithm for training recurrent neural networks , 2001, Neural Networks.

[5]  Ahmet Rizaner.  Radial basis function network assisted single-user channel estimation by using a linear minimum mean square error detector under impulsive noise , 2013, Comput. Electr. Eng..

[6]  Dexuan Zou,et al.  Directed searching optimization algorithm for constrained optimization problems , 2011, Expert Syst. Appl..

[7]  Prasant Kumar Pattnaik,et al.  Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization , 2014, Expert Syst. Appl..

[8]  Pradipta Kishore Dash,et al.  NARX model based nonlinear dynamic system identification using low complexity neural networks and robust H∞ filter , 2013, Appl. Soft Comput..

[9]  Dong-Sun Kim,et al.  A Modified Genetic Algorithm for Fast Training Neural Networks , 2005, ISNN.

[10]  Da Ruan,et al.  Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization , 2011, Inf. Sci..

[11]  Swagatam Das,et al.  Nonlinear Channel Equalization for Digital Communications Using DE-Trained Functional Link Artificial Neural Networks , 2011, IC3.

[12]  Kaibo Zhou,et al.  A New Ridgelet Neural Network Training Algorithm Based on Improved Particle Swarm Optimization , 2007, Third International Conference on Natural Computation (ICNC 2007).

[13]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[14]  Necmi Taspinar,et al.  Channel estimation based on neural network in space time block coded MIMO-OFDM system , 2013, Digit. Signal Process..

[15]  Ravi Kumar Jatoth,et al.  An intelligent functional link artificial neural network for channel equalization , 2009 .

[16]  Nurhan Karaboga,et al.  Digital IIR Filter Design Using Differential Evolution Algorithm , 2005, EURASIP J. Adv. Signal Process..

[17]  Xiukai Ruan,et al.  Blind sequence estimation of MPSK signals using dynamically driven recurrent neural networks , 2014, Neurocomputing.