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智能反射面(IRS)是未来无线通信中的一项新兴技术。IRS由大量低成本的无源反射元件组成，每个元件可以通过控制其幅度和相位来独立反射信号。因此，当视线(LOS)链路被障碍物阻挡时，IRS可以提供替代的反射链路以维持通信。IRS辅助通信的预期效果取决于信道状态信息(CSI)的准确性。针对IRS辅助的多输入多输出(MIMO)通信系统，提出了一种基于PARAFAC分解的正则化交替最小二乘法(P-RALS)。该算法能够通过少量导频估计信道矩阵，从而提高频谱利用率。后续的仿真结果表明，与现有的基于导频的信道估计算法相比，该算法显著提高了CSI估计的准确性。
Abstract：Intelligent Reflecting Surface( IRS) is an emerging technology for future wireless communications. Composed of a large number of low-cost passive reflecting elements,an IRS enables each element to independently reflect signals by controlling both amplitude and phase. Consequently,when the line-of-sight( LOS) link is obstructed,the IRS can provide an alternative reflective link to maintain communication. The expected performance of IRS-assisted communication depends on the accuracy of channel state information( CSI). This paper proposes a PARAFAC decomposition-based regularized alternating least squares( P-RALS) algorithm for IRS-assisted multiple-input multiple-output( MIMO) systems. The algorithm estimates the channel matrix using a small number of pilots,thereby enhancing spectral efficiency. Simulation results demonstrate that the proposed method significantly improves CSI estimation accuracy compared to existing pilot-based channel estimation algorithms.
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基本信息:
中图分类号:TN911.23
引用信息:
[1]朱文杰,冷清.基于张量分解的智能反射面信道估计算法[J].湖北师范大学学报(自然科学版),2025,45(03):19-25.
基金信息:
国家自然科学基金(62172144); 湖北省教育厅中青年人才科技项目(Q20192501); 湖北师范大学人才引进项目(1000002)