%0 Journal Article %1 anischarrada2012estimation %A Anis charrada, Abdelaziz samet %D 2012 %E AEU, %J AEU %K LS-SVM, LTE. Mercer’s OFDM, SVM, channel complex estimation, kernel, myown %N 8 %P 687-692 %T Estimation of Highly Selective Channels for OFDM System by complex Least Squares Support Vector Machines %U http://www.sciencedirect.com/science/article/pii/S1434841111003189 %V 66 %X A channel estimator using complex Least Squares Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink. This channel estimation algorithm use knowledge of the pilot signals to estimate the total frequency response of the channel. Thus, the algorithm maps trained data into a high di- mensional feature space and uses the structural risk minimiza- tion (SRM) principle, which minimizes an upper bound on the generalization error, to carry out the regression estimation for the frequency response function of the highly selective chan- nel. Simulation results show that the proposed method has bet- ter performance compared to the conventional LS and Decision Feedback methods and it is more robust at high speed mobility.

@article{anischarrada2012estimation, abstract = {A channel estimator using complex Least Squares Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink. This channel estimation algorithm use knowledge of the pilot signals to estimate the total frequency response of the channel. Thus, the algorithm maps trained data into a high di- mensional feature space and uses the structural risk minimiza- tion (SRM) principle, which minimizes an upper bound on the generalization error, to carry out the regression estimation for the frequency response function of the highly selective chan- nel. Simulation results show that the proposed method has bet- ter performance compared to the conventional LS and Decision Feedback methods and it is more robust at high speed mobility.}, added-at = {2014-12-18T17:11:00.000+0100}, author = {Anis charrada, Abdelaziz samet}, biburl = {https://www.bibsonomy.org/bibtex/2da3c41fefc888c471189445d2c83e525/achs}, editor = {AEU}, interhash = {b1378c52931b3046ebeaf48e72e82665}, intrahash = {da3c41fefc888c471189445d2c83e525}, journal = {AEU}, keywords = {LS-SVM, LTE. Mercer’s OFDM, SVM, channel complex estimation, kernel, myown}, month = {Mai}, number = 8, pages = {687-692}, timestamp = {2014-12-18T17:23:35.000+0100}, title = {Estimation of Highly Selective Channels for OFDM System by complex Least Squares Support Vector Machines}, url = {http://www.sciencedirect.com/science/article/pii/S1434841111003189}, volume = 66, year = 2012 }