Intelligent reflecting surface (IRS) is a promising technology for beyond 5th Generation 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.
翻译:在完全被动的IRS辅助系统中,频道估计具有挑战性,应仅在基站或终端进行,因为IRS的元素无法处理信号。在本信中,我们开发一个基于 ARS 的半盲人接收器,在IRS 协助的多用户多输入多产出系统中解决联合频道和符号估计问题。拟议方法依赖IRS 反映的信号的通用PARATATAK Xamor模型,该模型以使用Khatri-Rao和Kronecker因子化的两阶段封闭式半盲人接收器为基础,使用Khatri-Rao和Kronecker因子化器。模拟结果表明,拟议的半盲人接收器在正方位常态平均错误率和符号错误率方面表现优异,而且与最近提议的平行要素分析接收器相比,计算复杂性较低。