A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell's reflection law. However, the optimal control of the RIS requires perfect channel state information (CSI) of the individual channels that link the base station (BS) and the mobile station (MS) to each other via the RIS. Thereby super-resolution channel (parameter) estimation needs to be efficiently conducted at the BS or MS with CSI feedback to the RIS controller. In this paper, we adopt a two-stage channel estimation scheme for RIS-aided millimeter wave (mmWave) MIMO systems without a direct BS-MS channel, using atomic norm minimization to sequentially estimate the channel parameters, i.e., angular parameters, angle differences, and products of propagation path gains. We evaluate the mean square error of the parameter estimates, the RIS gains, the average effective spectrum efficiency bound, and average squared distance between the designed beamforming and combining vectors and the optimal ones. The results demonstrate that the proposed scheme achieves super-resolution estimation compared to the existing benchmark schemes, thus offering promising performance in the subsequent data transmission phase.
翻译:重新整合的智能表面(RIS)可以通过改变射入电磁波向任何预期方向的阻断电磁波,从而打破Snell一般反射法,影响无线电传播环境,然而,对RIS的最佳控制要求各频道之间通过RIS连接基站(BS)和移动站(MS)的完美频道状态信息。通过超分辨率频道(参数)估计需要在BS或MS有效进行,CSI向IRS控制员反馈CSI的反馈。在本文中,我们采用了两阶段的RIS辅助毫米毫米波(mmWave)MIMO系统频道估计计划,没有直接的BS-MS频道,但利用原子规范最小化来按顺序估计频道参数,即角参数参数参数、角度差异和传播路径增益的产品。我们评估参数估计的中方错误、RIS收益、平均有效频谱效率捆绑和设计成的矢量与最佳矢量之间的平均平方距离。结果显示,拟议的系统将随后的性能计划实现超分辨率估计。