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An improved channel estimation algorithm based on compressed sensing for LTE-A

The performance of wireless communication system is not only determined by the transmitter and the receiver, but also by the wireless channel. In practical communication systems, wireless channel is extremely complex and uncertain, which will lead to the distortion of the received signal. In order to recover the transmitted signal more accurately, the receiver needs the channel estimation operation. At the same time, the channel estimation is a basic and crucial technology in the LTE-Advanced system, because the channel state information is needed in many key technologies, such as equilibrium detection, beam-forming, interference alignment etc.. Many researches indicated that the wireless channel has sparse characteristics, but existing channel estimation algorithms do not take advantage of this characteristic. The compressed sensing theory can be used to recover the original data with fewer observation data. In this paper, the compressed sensing theory is used to match the signal model by analyzing the model of received signals. Besides, it is used to estimate the wireless channel for the LTE-A uplink channel by taking advantage of sparse characteristics. Considering that the channel sparsity and the channel length of the practical communication radio channel, this paper proposes an improved channel estimation algorithm based on the orthogonal matching pursuit reconstruction algorithm which can full use of the sparse characteristics of the channel. Simulation results indicate that the proposed algorithm can improve the effectiveness of the proposed algorithm in improving the spectrum utilization, because no separate pilot is used for channel estimation. Whats more, the proposed algorithm can reconstruct the channel information effectively.