Channel Estimation for One-Bit Massive MIMO Base On UNet++
Aiming at the problem that channel estimation based on U-Net network will lead to information loss caused by neural network up-sampling and downsampling,this paper proposes a channel estimation algorithm under a one-bit massive MIMO system based on UNet++.Firstly,the algorithm connects the deep features with the shallow features through dense convolutional blocks to greatly reduce the information loss caused by the upper and lower sampling of the neural network,and secondly,it uses the conditional generative adversarial network(cGAN)to train it,introduces an adaptive loss function to train the network correctly,and completes the estimation of the channel matrix according to the quantized received signal of the pilot.The simulation results show that under the same conditions,the channel estimation method performs better than other existing deep learning(DL)methods,and has strong robustness under short pilot sequences or low signal-to-noise ratio(SNR).
万方数据
维普
