Name
Title | ||
|---|---|---|
Pipelined functional link artificial recurrent neural network with the decision feedback structure for nonlinear channel equalization |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ins.2011.04.033 | Inf. Sci. |
Keywords | Field | DocType |
decision feedback structure,computationally efficient nonlinear adaptive,artificial decision feedback recurrent,neural network,nonlinear channel equalization,modified real-time recurrent,decision feedback recurrent structure,pipelined functional link,artificial recurrent neural network,nonlinear channel equalizer,computational complexity,nonlinear channel model,multilayer perceptron,convergence rate,channel equalization,infinite impulse response,adaptive filter,process capability,recurrent neural network,bit error rate,steady state,artificial neural network | Nonlinear system,Control theory,Computer science,Infinite impulse response,Recurrent neural network,Communications system,Multilayer perceptron,Artificial intelligence,Rate of convergence,Machine learning,Computational complexity theory,Bit error rate | Journal |
Volume | Issue | ISSN |
181 | 17 | 0020-0255 |
Citations | PageRank | References |
9 | 0.64 | 32 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiquan Zhao | 1 | 886 | 64.79 |
| Xiangping Zeng | 2 | 151 | 11.35 |
| Jiashu Zhang | 3 | 1122 | 75.03 |
| Tianrui Li | 4 | 3176 | 191.76 |
| Yangguang Liu | 5 | 37 | 5.25 |
| Da Ruan | 6 | 2008 | 112.05 |