OFDM Channel Estimation Based on Intelligent Systems


This work is dedicated to the study of reducing Bit Error Rate (BER) when transferring data in the system Orthogonal Frequency Division Multiplexing (OFDM) by estimating the carrier channel in different ways. The proposal design for Artificial Neural Network (ANN) is considered as a tool to improve performance BER and compared with the traditional method based on the use of the Least Square estimation algorithm (LS) to estimate the impulse response of frequency selective Rayleigh fading channel. A MATLAB 7.14 program is used in simulation.The proposed method which integrates algorithm LS with ANN includes the following:1. Training the neural network by Back-Propagation (BP) and using the trained neural network with algorithm (LS) to estimate the channel in different paths.2. Using Resilient Back propagation algorithm (RProp)in the training of the neural network.3. UsingLevenberg-Marquardt algorithm (LM) in the training of the neural network.4.The comparison of results between the traditional method and the proposed method when taking BER = 0.001 at various tracks (one path, two path and three path) and showed that there profit of (1.5dB, 2dB, 2dB) between using the traditional method and the proposed method using RProp algorithm and a profit of (2dB,3dB, 2dB) using an algorithm LM. There is also comparison between the performance ofRProp algorithm and LMalgorithm and the results showed that the LM algorithm better thanRProp algorithm.