원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.8 No.10
2015.10
pp.81-90
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
A new channel estimation method of neural network based on complex radial basis function (CRBF) is proposed to enhance the anti-interference ability of traditional pilot frequency estimation algorithm in power line communication (PLC). This method builds up a new channel model of complex field signals in PLC. The complete response model was established by using transmitting terminal’s pilot signal as input sample data, pilot signal’s frequency response as output sample data, and pre-setting mean square error (MSE) and diffusion constant. Computer simulations show that compared with the traditional algorithm the channel estimation was more accurate and had lower MSE and bit error rate (BER).
목차
Abstract
1. Introduction
2. OFDM System Model
3. Traditional Pilot Frequency Estimation Algorithm
4. Channel Estimation based on Complex RBF Neural Network
4.1 The Data Processing of Plural RBF Neural Network
4.2 CRBF Neural Network Training
5. The Simulation Experiments and Analysis
6. Conclusion
Ackownledgement
References
1. Introduction
2. OFDM System Model
3. Traditional Pilot Frequency Estimation Algorithm
4. Channel Estimation based on Complex RBF Neural Network
4.1 The Data Processing of Plural RBF Neural Network
4.2 CRBF Neural Network Training
5. The Simulation Experiments and Analysis
6. Conclusion
Ackownledgement
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보