Reconfigurable intelligent surface (RIS) is envisioned to be a promising green technology to reduce the energy consumption and improve the coverage and spectral efficiency of multiple-input multiple-output (MIMO) wireless networks. In a RIS-aided MIMO system, the acquisition of channel state information (CSI) is important for achieving passive beamforming gains of the RIS, but is also challenging due to the cascaded property of the transmitter-RIS-receiver channel and the lack of signal processing capability of the large number of passive RIS elements. The state-of-the-art approach for CSI acquisition in such a system is a pure training-based strategy that hinges on a long sequence of pilot symbols. In this paper, we investigate semi-blind cascaded channel estimation for RIS-aided massive MIMO systems, in which the receiver jointly estimates the channels and the partially unknown transmit signals with a small number of pilot sequences. Specifically, we formulate the semi-blind channel estimation as a trilinear matrix factorization task. Under the Bayesian inference framework, we develop a computationally efficient iterative algorithm using the sum-product approximate message passing principle to resolve the trilinear inference problem. Meanwhile, we present an analytical framework to characterize the theoretical performance bound of the proposed algorithm in the large-system limit. Extensive simulation results demonstrate the effectiveness of the proposed semi-blind channel estimation algorithm.
翻译:可以重新配置的智能表面(RIS)被认为是一个大有希望的绿色技术,可以减少能源消耗,提高多投入多输出无线网络的覆盖面和光谱效率。在RIS辅助的MSIMO系统中,获取频道国家信息(CSI)对于实现RIS的被动波束增益十分重要,但也具有挑战性,因为发报机-接收器频道的级联特性以及大量被动的RIS元素缺乏信号处理能力。在这样一个系统中获取 CSI的最先进的方法是一种纯粹的基于培训的战略,取决于一系列长的试点符号。在本文中,我们调查了对TRIS辅助的大型MIMO系统的半盲级级级级级频道估计,在该系统中,接收器共同估计频道和部分未知的信号,以少量的试点序列来传递信号。具体地说,我们将半盲频道估算作为三线矩阵化的指数化任务。在Bayeservic 框架下,我们开发了一种基于计算效率的高效的迭代方算算算法计算方法,在分析中,我们提出了将分析系统模拟模型分析结果的精度模型分析模型分析结果,从而解决了我们提出的双轨分析结果的系统。