融合残差SENet的毫米波大规模MIMO信道估计OA北大核心CSTPCD

In the outdoor ray tracing scene,to solve the problem of low estimation accuracy of channel estimation caused by outdoor ambient noise interference in mmWave massive multiple-input multiple-output(MIMO)systems,a channel estimation method based on the Conditional Generative Adversarial Network(CGAN)of fused residual Squeeze-and-Excitation Network(SENet)is proposed.The method uses the CGAN to reconstruct the low resolution received signal into the original signal with high resolution to complete channel estimation.At the same time,SENet is introduced into the generator network to suppress the significant noise interference in the outdoor scene and improve the estimation accuracy.Finally,the residual block in the residual network is added to the scaling operation of the SENet to improve the convergence speed of the CGAN.The simulation results show that compared with that of Orthogonal Matching Pursuit(OMP)algorithm,Convolutional Neural Network(CNN)algorithm,Denoising Convolutional Neural Network(DnCNN)algorithm and CGAN algorithm,the estimation accuracy of the proposed method in outdoor noise environment is improved by about 2.2 dB on average,and the improvement of estimation accuracy is more significant under high noise intensity.