期刊名称：International Journal of Soft Computing and Software Engineering
电子版ISSN：2251-7545
出版年度：2013
卷号：3
期号：3
页码：733-738
DOI：10.7321/jscse.v3.n3.111
出版社：Advance Academic Publisher
摘要：Paper propose a robust channel estimator for downlink Long Term Evolution-Advanced (LTE-A) system using Artificial Neural Network (ANN) trained by backpropa- gation algorithm (BPA) and ANN trained by genetic algorithm (GA). The new methods use the information provided by the received reference symbols 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 methods is confirmed by simula- tions in Vienna LTE-A Link Level Simulator. Performances of the proposed channel estimator, ANN trained by GA and ANN trained by BPA is compared with traditional Least Square (LS) algorithm for Closed Loop Spatial Multiplexing-Single User Multi-input Multi-output (2X2) (CLSM-SUMIMO) case.
