Channel Estimation for mmWave mMIMO Systems With Low-Resolution ADCs Based on Adaptive Quantization Improved CGAN

Millimeter wave massive multiple-input multiple-output systems offer higher information capacity and can mitigate severe propagation losses. However, the rapid increase in the number of antennas results in higher hardware costs and power consumption. Although these issues can be addressed by reducing the resolution of analog-to-digital converters (ADCs) in the radio frequency chain, low-resolution ADCs introduce significant nonlinear errors when quantizing received signals, making accurate channel estimation very challenging. To mitigate the impact of quantization noise from low-resolution ADCs on channel estimation performance, we propose a novel channel estimation method based on adaptive quantization conditional generative adversarial network (AQ-CGAN). Instead of using traditional quantization methods, we convert the quantized received signals and pilot sequences as well as the quantization thresholds into two-dimensional images, randomly generate and iteratively optimize the thresholds under the CGAN framework. At the same time, we use the neural network and CGAN to establish a mapping relationship between quantization thresholds and channel matrices to achieve adaptive quantization, and introduce a new loss function to improve the channel estimation accuracy in massive MIMO systems. Simulation results demonstrate that the proposed algorithm outperforms traditional quantized CGAN channel estimation under the same signal-to-noise ratio conditions.