Channel estimation based on learning automata for OFDM systems

Summary The abundant benefits of Orthogonal Frequency‐Division Multiplexing 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. In the literatures, many techniques have been studied in order to estimate the channel state information. Nowadays, the techniques based on intelligent algorithms such as genetic algorithm 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 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 learning automata, entitled LA estimator, where the learning automata are search agents, and each pair is responsible for searching 1 complex coefficient of the channel frequency response. This method can achieve an 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 2‐path fast fading channel, simulation results demonstrate the robustness of our proposed scheme according to the bit error rate and the mean square error. Accurate channel state information is required to increase the efficiency of the Orthogonal Frequency‐Division Multiplexing systems. In this paper, an intelligent channel estimator based on learning automata has been proposed. The learning automata channel estimator can estimate the channel state information in a more proper way compared with conventional intelligent channel estimators like GA and particle swarm optimization estimators.