COMPLEX SUPPORT VECTOR MACHINE REGRESSION FOR ROBUST CHANNEL ESTIMATION IN LTE DOWNLINK SYSTEM

In this paper, the problem of channel estimation for LTE Downlink system in the environment of highmobility presenting non-Gaussian impulse noise interfering with reference signals is faced. Theestimation of the frequency selective time varying multipath fading channel is performed by using achannel estimator based on a nonlinear complex Support Vector Machine Regression (SVR) which isapplied to Long Term Evolution (LTE) downlink. The estimation algorithm makes use of the pilot signalsto estimate the total frequency response of the highly selective fading multipath channel. Thus, thealgorithm maps trained data into a high dimensional feature space and uses the structural riskminimization principle to carry out the regression estimation for the frequency response function of thefading channel. The obtained results show the effectiveness of the proposed method which has betterperformance than the conventional Least Squares (LS) and Decision Feedback methods to track thevariations of the fading multipath channel.