In this paper, we investigate a cascaded channel estimation method for a millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with low-resolution analog-to-digital converters (ADCs), where the BS and the RIS are both equipped with a uniform planar array (UPA). Due to the sparse property of mmWave channel, the channel estimation can be solved as a compressed sensing (CS) problem. However, the low-resolution quantization cause severe information loss of signals, and traditional CS algorithms are unable to work well. To recovery the signal and the sparse angular domain channel from quantization, we introduce Bayesian inference and efficient vector approximate message passing (VAMP) algorithm to solve the quantize output CS problem. To further improve the efficiency of the VAMP algorithm, a Fast Fourier Transform (FFT) based fast computation method is derived. Simulation results demonstrate the effectiveness and the accuracy of the proposed cascaded channel estimation method for the RIS-aided mmWave massive MIMO system with few-bit ADCs. Furthermore, the proposed channel estimation method can reach an acceptable performance gap between the low-resolution ADCs and the infinite ADCs for the low signal-to-noise ratio (SNR), which implies the applicability of few-bit ADCs in practice.

翻译：本文研究了一种针对毫米波大规模多输入多输出系统的级联信道估计方法，该系统由可重构智能表面辅助，且基站配备低分辨率模数转换器，其中基站和RIS均采用均匀平面阵列。由于毫米波信道的稀疏特性，信道估计可建模为压缩感知问题。然而，低分辨率量化会导致严重的信号信息损失，传统CS算法难以有效工作。为从量化信号中恢复信号及稀疏角度域信道，本文引入贝叶斯推理和高效向量近似消息传递算法来求解量化输出CS问题。为进一步提升VAMP算法的效率，本文推导了一种基于快速傅里叶变换的快速计算方法。仿真结果表明，所提出的级联信道估计方法对于采用少比特ADC的RIS辅助毫米波大规模MIMO系统具有有效性和准确性。此外，在低信噪比下，该方法可使低分辨率ADC与无限分辨率ADC之间的性能差距达到可接受水平，从而验证了少比特ADC在实际应用中的可行性。