Millimeter wave (mmWave) massive multiple-input multiple-output (massive MIMO) is one of the most promising technologies for the fifth generation and beyond wireless communication system. However, a large number of antennas incur high power consumption and hardware costs, and high-frequency communications place a heavy burden on the analog-to-digital converters (ADCs) at the base station (BS). Furthermore, it is too costly to equipping each antenna with a high-precision ADC in a large antenna array system. It is promising to adopt low-resolution ADCs to address this problem. In this paper, we investigate the cascaded channel estimation for a mmWave massive MIMO system aided by a reconfigurable intelligent surface (RIS) with the BS equipped with few-bit ADCs. Due to the low-rank property of the cascaded channel, the estimation of the cascaded channel can be formulated as a low-rank matrix completion problem. We introduce a Bayesian optimal estimation framework for estimating the user-RIS-BS cascaded channel to tackle with the information loss caused by quantization. To implement the estimator and achieve the matrix completion, we use efficient bilinear generalized approximate message passing (BiG-AMP) algorithm. Extensive simulation results verify that our proposed method can accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO system with low-resolution ADCs.

翻译：毫米波大规模多输入多输出（massive MIMO）是第五代及未来无线通信系统中最具潜力的技术之一。然而，大量天线会带来高功耗与硬件成本，且高频通信对基站端的模数转换器（ADC）造成了沉重负担。此外，在大型天线阵列系统中为每根天线配备高精度ADC的成本过高。采用低分辨率ADC是解决该问题的可行方案。本文针对基站配备少比特ADC的可重构智能表面（RIS）辅助毫米波大规模MIMO系统，研究其级联信道估计问题。利用级联信道的低秩特性，可将级联信道估计转化为低秩矩阵补全问题。我们引入一种贝叶斯最优估计框架来估计用户-RIS-基站级联信道，以应对量化引起的信息损失。为实现估计器并完成矩阵补全，采用高效的双线性广义近似消息传递（BiG-AMP）算法。大量仿真结果验证，所提方法能够准确估计采用低分辨率ADC的RIS辅助毫米波大规模MIMO系统的级联信道。