Channel Estimation Method for Massive MIMO Based on Image Super-resolution Generative Adversarial Network
In millimeter-wave massive multiple-input multiple-output systems,for the traditional channel estimation the accuracy can be improved by using the statistical characteristics of the channel and noise.In this paper a channel estimation algorithm based on image super-resolution generative adversarial network(SRGAN)is proposed,which models channel estimation as the problem of image super-resolution recovery.Firstly,the least square algorithm is used to obtain the channel information at the pilot position,and then the channel matrix information with low resolution is obtained by two-dimensional linear interpolation,and finally it is used as the input of the SRGAN network proposed in this paper to recover the true channel frequency response of the channel through train-ing.The simulation results show that the performance of the proposed channel estimation algorithm is greatly improved compared with the traditional channel estimation algorithm,and the recovered channel is more consistent with the real channel.
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