DocumentCode :
1623078
Title :
Channel estimation based on neural network in OFDM system for powerline communication
Author :
Taspinar, Necmi ; Sulev, Anil
Author_Institution :
Dept. of Electr. & Electron. Eng, Erciyes Univ., Kayseri, Turkey
fYear :
2013
Firstpage :
1
Lastpage :
4
Abstract :
In this paper, channel estimation based on neural network in powerline communication is proposed and its performance is compared with LS and MMSE methods by computer simulations using mean square error (MSE) and bit error rate (BER) criterias.The MSE and BER performances of neural network to estimate channel are between the LS and MMSE algorithms. MMSE algorithm yields the best performance but its complexity is high. The advantage of the use of the neural network is that the neural network yields better performance than the LS algorithm and it is less complex than the MMSE algorithm1.
Keywords :
OFDM modulation; carrier transmission on power lines; channel estimation; error statistics; least mean squares methods; neural nets; telecommunication computing; BER criteria; LS algorithm; LS method; MMSE algorithm; MMSE method; MSE criteria; OFDM system; bit error rate criteria; channel estimation; computer simulations; mean square error criteria; neural network; powerline communication; Biological neural networks; Bit error rate; Channel estimation; Mean square error methods; OFDM; Signal processing algorithms; OFDM; channel estimation; neural networks; powerline channel;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics, Computers and Artificial Intelligence (ECAI), 2013 International Conference on
Conference_Location :
Pitesti
Print_ISBN :
978-1-4673-4935-2
Type :
conf
DOI :
10.1109/ECAI.2013.6636209
Filename :
6636209
Link To Document :
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