可重构智能面辅助的低精度量化大规模MIMO系统的信道估计

针对可重构智能面辅助的低精度量化的大规模多输入多输出(multiple input multiple output, MIMO)系统中信道估计问题进行了研究。该系统的信道估计难点在于可重构智能面由近无源反射天线构成, 没有基带信号处理能力。系统观测值通过低精度的模数转换器量化使信道估计问题变得更富挑战性。本文基于基站-可重构智能面-用户的级联信道推导出等效信道, 并证明在虚拟角域上，该有效信道是结构稀疏信号。提出了基于期望最大化的近邻学习广义近似消息传递算法，从低精度量化的观测值中恢复等效信道。仿真结果表明所提出算法比传统算法具有更好的性能表现。

Channel estimation for reconfigurable intelligent surface assisted low-resolution quantized massive MIMO

The problem of channel estimation for reconfigurable intelligent surface assisted massive multiple input multiple output (MIMO) systems in view of low-resolution quantization is focused on. The main challenge for channel estimation of this system lies in that the reconfigurable intelligent surface(RIS) is composed of reconfigurable and nearly passive reflecting antennas which has no signal processing capability. Meanwhile, considering the situation that the observations is quantized by the low-resolution analog-to-digital converters, the channel estimation problem for such system becomes more challenging. By introducing the effective channel model derived from the cascaded channel model among the base station, the RIS and the user equipment, and proving that the effective channel can be considered as a structured sparse signal in the virtual angular domain. An expectation-maximization-based nearest neighbor learning generalized approximate message passing algorithm is proposed to recover the effective channel from the low-resolution quantized observations. Simulation results illustrate that the proposed algorithm can obtain better performance than conventional algorithms.