Intelligent reflecting surface (IRS) is an emerging technology for future wireless communications including 5G and especially 6 G. It consists of a large 2D array of (semi-)passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we address the receiver design for an IRS-assisted multiple-input multiple-output (MIMO) communication system via a tensor modeling approach aiming at the channel estimation problem using supervised (pilot-assisted) methods. Considering a structured time-domain pattern of pilots and IRS phase shifts, we present two channel estimation methods that rely on a parallel factor (PARAFAC) tensor modeling of the received signals. The first one has a closed-form solution based on a Khatri-Rao factorization of the cascaded MIMO channel, by solving rank-1 matrix approximation problems, while the second on is an iterative alternating estimation scheme. The common feature of both methods is the decoupling of the estimates of the involved MIMO channel matrices (base station-IRS and IRS-user terminal), which provides performance enhancements in comparison to competing methods that are based on unstructured LS estimates of the cascaded channel. Design recommendations for both methods that guide the choice of the system parameters are discussed. Numerical results show the effectiveness of the proposed receivers, highlight the involved trade-offs, and corroborate their superior performance compared to competing LS-based solutions.

中文翻译：

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智能反射面辅助MIMO系统的信道估计：张量建模方法

智能反射表面（IRS）是用于包括5G尤其是6G在内的未来无线通信的新兴技术。它由（半）无源散射元件的大型2D阵列组成，这些阵列控制射频波的电磁特性，以便反射信号相干地添加到预期的接收器或破坏性地添加以减少同频干扰。IRS辅助通信的预期收益取决于信道状态信息的准确性。在本文中，我们解决了IRS辅助的多输入多输出（MIMO）通信系统的接收机设计通过针对张量建模方法的张量建模方法，采用监督（飞行员辅助）方法。考虑到导频和IRS相移的结构化时域模式，我们提出了两种信道估计方法，这些方法依赖于接收信号的并行因子（PARAFAC）张量建模。第一个解决方案是通过解决Rank-1矩阵逼近问题而基于级联MIMO信道的Khatri-Rao分解的封闭形式解决方案，而第二个解决方案是迭代交替估计方案。两种方法的共同特点是对所涉及的MIMO信道矩阵（基站IRS和IRS用户终端）的估计值进行解耦，与基于级联信道的非结构化LS估计的竞争方法相比，该方法可提供性能增强。讨论了指导系统参数选择的两种方法的设计建议。数值结果表明，与基于LS的竞争解决方案相比，拟议中的接收器是有效的，突出了相关的取舍，并证实了其优越的性能。