A Semi Blind Channel Estimation Method Based on Hybrid Neural Networks for Uplink LTE-A

Abstract

The paper describes how to improve channel estimation in Single Carrier Frequency Division Multiple Access (SC-FDMA) system, using a Hybrid Artificial Neural Networks (HANN). The 3 rd Generation Partnership Project (3GPP) standards for uplink Long Term Evolution Advanced (LTE-A) uses pilot based channel estimation technique. This kind of channel estimation method suffers from a considerable loss ofbitrate due to pilot insertion; all data frame sent contains reference signal. The HANN converts data aided channel estimator to semi blind channel estimator. To increase convergence speed, HANN uses some channel propagation Fuzzy Rules to initialize Neural Network parameters before learning instead of a random initialization, so its learning phase ismore rapidly compared to classic ANN.HANN allows more bandwidth efficient and less complexity. Simulation results show that HANN has better computational efficiency than the Minimum Mean Square Error (MMSE) estimator and has faster convergence than classic Neural Networks estimators.