Using Back Propagation Neural Network for Channel Estimation and Compensation in OFDM Systems

In orthogonal frequency division multiplexing (OFDM) communication systems, due to the environmental impact generated the multipath effect caucused signals distortion and attenuation in transmitted process, and due to relative motion between transmitter and receiver caused the Doppler Effect that makes the signal carrier offset. Therefore, the knowledge of the channel characteristics is very important. To remove the effect from received signal, the receiver needs to have knowledge of channel impulse response (CIR) by channel estimation, and then compensates signals. In this paper, a back propagation neural network (BPNN) is used to estimate channel and compensate signals. Our proposed BPNN channel estimation would compare bit error rate (BER) and mean square error (MSE) with least square (LS) and minimum mean square error (MMSE) algorithms in an existing OFDM channel environment. From the results, our proposed algorithm has better performance than LS algorithm and closes to MMSE algorithm.