Channel Estimation for One-Bit Massive MIMO Based on Improved CGAN

In the one-bit massive multiple-input multiple-output (MIMO) channel scenario, the accurate channel estimation becomes more difficult because the signals received by the low-resolution analog-to-digital converters (ADC) are quantized and affected by channel noise. Therefore, a one-bit massive MIMO channel estimation method is proposed in this paper. The channel matrix is regarded as a two-dimensional image. In order to enhance the significance of noise features in the image and remove them, the channel attention mechanism is introduced into the conditional generative adversarial network (CGAN) to generate channel images, and improve the loss function. The simulation results show that the improved network can use a smaller number of pilots to obtain better channel estimation results. Under the same number of pilots and signal-to-noise ratio (SNR), the channel estimation accuracy can be improved by about 7.5 dB, and can adapt to the scenarios with more antennas.