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.
Original language | English |
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Pages (from-to) | 3677-3692 |
Number of pages | 16 |
Journal | Information Sciences |
Volume | 181 |
Issue number | 17 |
DOIs | |
State | Published - 1 Sep 2011 |
Funding
This work was partially Supported by National Science Foundation of China (Grant: 61071183 , 60572027 and 60971104 ), by the program for new century excellent talents in university of China (Grant:NCET-05-0794) and by the doctoral innovation fund of Southwest Jiaotong University, Chengdu, China.
Funders | Funder number |
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Southwest Jiaotong University | |
NSFC - National Natural Science Foundation of China | 60971104, 60572027, 61071183, NCET-05-0794 |
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence