Channel Estimation for LTE Uplink System by Perceptron Neural Network
In this paper, a channel estimator using neural network is presented for Long Term Evolution (LTE) uplink. This paper considers multiuser SC-FDMA uplink transmissions with doubly selective channels. This channel estimation method uses knowledge of pilot channel properties to estimate the unknown channel response at non-pilot sub-carriers. First, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance, in terms of complexity and quality, compared to the conventional methods Least Square (LS), MMSE and decision feedback and it is more robust at high speed mobility.