Complex support vector machines regression for robust channel estimation in LTE downlink system
This is an incremental improvement for LTE communication systems facing noisy, high-mobility environments.
The paper tackled channel estimation in LTE downlink systems under high mobility and non-Gaussian noise by using a complex Support Vector Machine Regression method, achieving better performance than conventional Least Squares and Decision Feedback methods.
In this paper, the problem of channel estimation for LTE Downlink system in the environment of high mobility presenting non-Gaussian impulse noise interfering with reference signals is faced. The estimation of the frequency selective time varying multipath fading channel is performed by using a channel estimator based on a nonlinear complex Support Vector Machine Regression (SVR) which is applied to Long Term Evolution (LTE) downlink. The estimation algorithm makes use of the pilot signals to estimate the total frequency response of the highly selective fading multipath channel. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization principle to carry out the regression estimation for the frequency response function of the fading channel. The obtained results show the effectiveness of the proposed method which has better performance than the conventional Least Squares (LS) and Decision Feedback methods to track the variations of the fading multipath channel.