Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions

• Published in: arXiv.org
• Main Authors: Charrada, Anis, Samet, Abdelaziz
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 31.08.2011
• Subjects: Algorithms; Computer Science - Learning; Computer simulation; Frequency response functions; Reference signals; Statistics - Machine Learning; Support vector machines
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1109.0895

A nonlinear channel estimator using complex Least Square Support Vector Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink under high mobility conditions. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.