Channel estimation for OFDM systems using radial basis function networks | ||
| The International Conference on Electrical Engineering | ||
| Article 17, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Pages 1-9 PDF (116.63 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iceeng.2008.34203 | ||
| Authors | ||
| M. Nuri Seyman1; Necmi Taşpinar2 | ||
| 1Department of Electronic Communication, Kirikkale University, Kirikkale, Turkey. | ||
| 2Department of Electrical and Electronic Engineering, Erciyes University, Kayseri, Turkey. | ||
| Abstract | ||
| Abstract: In this paper, in order to estimate channel impulse responses in orthogonal frequency division multiplexing (OFDM), we use radial basis function network that is a kind of neural network, because of this structure is applicable of this kind of problem for its strong approximation and learning ability. We compare the performance of channel estimator based on radial basis function neural network with LS and MMSE algorithm with bit error rate (BER) and mean square error (MSE) criterias. Also Cramer Rao bound is given to evaluate the performances of estimators. Our proposal channel estimator has better performance than LS algorithm and closer performance to MMSE algorithm. However there is unnecessity of knowledge of channel statics and noise information of channel when neural structures are used as a channel estimator. Moreover after neural structures are trained, there is no need of sending pilot tones that are used to get channel impulse responses by LS and MMSE algorithm. As a result, system spendings are reduced. | ||
| Keywords | ||
| Orthogonal frequency division multiplexing (OFDM); Channel estimation; radial basis function network | ||
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