Channel Estimation for mmWave mMIMO Systems With Mixed-ADC: Conditional Generative Adversarial Network and Deep Transfer Learning

Millimeter wave (mmWave) massive multiple input multiple output (mMIMO) systems have the advantages of abundant spectrum resources, high transmission rate, and high multiplexing gain. However, the large number of antennas leads to high power consumption and difficult channel estimation. Although the problem of high power consumption can be addressed by reducing the analog-to-digital converter (ADC) resolution of radio frequency (RF) links, considering the accuracy of channel state information (CSI), high-resolution antennas are still needed to be deployed on a small number of RF terminals. Therefore, a mixed-resolution ADC should be used for signal transmission to consider channel estimation and power consumption tradeoff. To reduce the influence of quantization noise generated by the low- resolution ADC on channel estimation performance, we propose two deep learning-based channel estimation methods for mmWave mMIMO systems with mixed-resolution ADCs. Based on the conditional generative adversarial network, the first method has the advantage of generating more useful CSI with less available high-resolution CSI, which fits the characteristics of the mixed ADC system. Moreover, the second proposed method exploits deep transfer learning to train low-resolution antenna data using network parameters that have been trained at high-resolution antennas. Both methods can fully utilize the information from high-resolution antennas while mining the effective information from low-resolution antennas, thereby achieving high-resolution channel estimation while reducing mMIMO system overhead. Simulation results demonstrate that both methods can perform better than deep neural networks in mixed-resolution ADC scenarios.