DocumentCode :
2429333
Title :
Artificial neural network modeling approach to power-line communication multi-path channel
Author :
Ma, Yong-tao ; Liu, Kai-hua ; Guo, Yi-na
Author_Institution :
Sch. of Electron. Inf. Eng., Tianjin Univ., Tianjin
fYear :
2008
fDate :
7-11 June 2008
Firstpage :
229
Lastpage :
232
Abstract :
A trained neural network can be used for high-level design, providing fast and accurate answers to the task it has learned. Neural networks are effective alternatives to conventional methods such as statistical and stochastic modeling methods, which could be computationally expensive, or analytical methods which could be difficult to obtain for new environments, or empirical modeling solutions whose range and accuracy may be limited. Power-line communication (PLC) is a useful way to transmit data and exchange information based on power-line channel. Due to the multi-path propagation inherently in the power line channel, the characteristic of power line channel is analyzed in this paper. The modeling of multi-path propagation is completed base on conventional way and ANN. Results of different modeling methods are analyzed. It is proved that ANN-based modeling of communication channel is an efficient method. This makes stable foundation for future power-line communication simulation.
Keywords :
carrier transmission on power lines; multipath channels; neural nets; telecommunication computing; artificial neural network; data transmission; high-level design; information exchange; multipath channel; multipath propagation; power-line channel; power-line communication; Artificial neural networks; Attenuation; Communication channels; Delay effects; Impedance; Mathematical model; Power system modeling; Predictive models; Programmable control; Stochastic processes; Artificial Neural Network; Channel Modeling; Multi-path; Power-line Communication;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks and Signal Processing, 2008 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-2310-1
Electronic_ISBN :
978-1-4244-2311-8
Type :
conf
DOI :
10.1109/ICNNSP.2008.4590345
Filename :
4590345
Link To Document :
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