Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization

Authors:
Haiquan Zhao;Xiangping Zeng;Jiashu Zhang;Tianrui Li;Yangguang Liu;Da Ruan
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China and School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China and Center of Electronic Lab, Chengdu University of Information Technology, Chengdu 610255, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;Belgian Nuclear Research Centre (SCKCEN), Boeretang 200, 2400 Mol, Belgium
Venue:
Information Sciences: an International Journal
Year:
2011

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Abstract

This paper presents a computationally efficient nonlinear adaptive filter by a pipelined functional link artificial decision feedback recurrent neural network (PFLADFRNN) for the design of a nonlinear channel equalizer. It aims to reduce computational burden and improve nonlinear processing capabilities of the functional link artificial recurrent neural network (FLANN). The proposed equalizer consists of several simple small-scale functional link artificial decision feedback recurrent neural network (FLADFRNN) modules with less computational complexity. Since it is a module nesting architecture comprising a number of modules that are interconnected in a chained form, its performance can be further improved. Moreover, the equalizer with a decision feedback recurrent structure overcomes the unstableness thanks to its nature of infinite impulse response structure. Finally, the performance of the PFLADFRNN modules is evaluated by a modified real-time recurrent learning algorithm via extensive simulations for different linear and nonlinear channel models in digital communication systems. The comparisons of multilayer perceptron, FLANN and reduced decision feedback FLANN equalizers have clearly indicated the convergence rate, bit error rate, steady-state error and computational complexity, respectively, for nonlinear channel equalization.