论文标题
IRS辅助多用户MIMO网络中的半盲频道和符号估计
Semi-Blind Joint Channel and Symbol Estimation in IRS-Assisted Multi-User MIMO Networks
论文作者
论文摘要
智能反射表面（IRS）是超越第五代无线通信的有前途的技术。在完全被动的IRS辅助系统中，渠道估计是具有挑战性的，并且仅在基站或终端进行，因为IRS的元素无法处理信号。在这封信中，我们制定了一个基于张量的半盲接收器，该接收器解决了IRS辅助的多用户多输入多输出系统中的联合通道和符号估计问题。所提出的方法依赖于IRS反射的信号的广义paratuck张量模型，该模型基于使用Khatri-Rao和Kronecker Facerization的两阶段封闭形式的半盲型接收器。模拟结果证明了与最近提出的基于平行因子分析的接收器相比，就标准化的平方误差和符号误差率以及较低的计算复杂性而言，提出的半盲接收器的出色性能以及较低的计算复杂性。
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.
