Intelligent reflecting surface (IRS) is a promising technology for beyond of the wireless communications. In fully passive IRS-assisted systems, channel estimation is challenging and should be carried out only at the base station or at the terminals since the elements of the IRS are incapable of processing signals. In this letter, we formulate a tensor-based semi-blind receiver that solves the joint channel and symbol estimation problem in an IRS-assisted multi-user multiple-input multiple-output system. The proposed approach relies on a generalized PARATUCK tensor model of the signals reflected by the IRS, based on a two-stage closed-form semi-blind receiver using Khatri-Rao and Kronecker factorizations. Simulation results demonstrate the superior performance of the proposed semi-blind receiver, in terms of the normalized mean squared error and symbol error rate, as well as a lower computational complexity, compared to recently proposed parallel factor analysis-based receivers.

中文翻译：

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IRS 辅助多用户 MIMO 网络中的半盲联合信道和符号估计


智能反射表面（IRS）是一项有前途的无线通信技术。在完全无源 IRS 辅助系统中，信道估计具有挑战性，并且只能在基站或终端处执行，因为 IRS 的元件无法处理信号。在这封信中，我们制定了一种基于张量的半盲接收器，它解决了 IRS 辅助的多用户多输入多输出系统中的联合信道和符号估计问题。所提出的方法依赖于 IRS 反射信号的广义 PARATUCK 张量模型，该模型基于使用 Khatri-Rao 和 Kronecker 分解的两级封闭式半盲接收器。仿真结果表明，与最近提出的基于并行因子分析的接收机相比，所提出的半盲接收机在归一化均方误差和符号错误率方面具有优越的性能，并且计算复杂度较低。