An Intelligent Approach for OFDM Channel Estimation Using Gravitational Search Algorithm

The abundant benefits of Orthogonal Frequency-Division Multiplexing (OFDM) and its high flexibility have resulted in its widespread applications in many telecommunication standards. One important parameter for improving wireless system’s efficiency is the accurate estimation of channel state information (CSI). In the literatures many techniques have been studied in order to estimate the CSI. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have attracted attention of researchers. With a very low pilot overhead, these techniques are able to estimate the channel frequency response (CFR) properly only using the received signals. Unfortunately each of these techniques suffers a common weakness: they have a slow convergence rate. In this paper, a new intelligent and different method has been presented for channel estimation using gravitational search algorithm (GSA). This method can achieve accurate channel estimation with a moderate computational complexity in comparison with GA and PSO estimators. Furthermore, with higher convergence rate our proposed method is capable of providing the same performance as GA and PSO. For a two-path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate (BER) and the mean square error (MSE).