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In this paper we propose estimator for Long Term Evolution (LTE) selective using neural network. This method us provided by the reference signals to estimate t response of the channel in two phases. In th proposed method learns to adapt to the channe the second phase it predicts the channel paramThe performance of the estimation met complexity and quality is confirmed by theor simulations in an LTE/OFDMA transmis performance of the proposed channel estima with those of least square (LS), decision feedb Wiener methods. The simulation results show estimator performs better than the above es more robust at high speed mobility.

In this paper we propose a robust channel estimator for the downlink of a Long Term Evolution (LTE) system using a highly selective neural network. This method uses the information provided by the reference signals to estimate the total frequency response of the channel in two phases. In the first phase, the proposed method learns to adapt to the channel variations, and in the second phase it predicts the channel parameters.The performance of the estimation method in terms of complexity and quality is confirmed by theoretical analysis and simulations in an LTE/OFDMA (Orthogonal Frequency-Division Multiple Access) transmission system. The performance of the proposed channel estimator is compared with those of least-square decision feedback and modified Wiener methods. The simulation results show that the proposed estimator performs better than the above estimators and it is more robust at high-speed mobility.