论文标题
IRS辅助毫米浪潮DM系统的压缩通道估计：一种基于低量张量分解的方法
Compressed Channel Estimation for IRS-Assisted Millimeter Wave OFDM Systems: A Low-Rank Tensor Decomposition-Based Approach
论文作者
论文摘要
我们考虑智能反射表面（IRS）辅助毫米波（MMWAVE）正交频施加多路复用（OFDM）系统的下行链路通道估计的问题。通过探索MMWave通道的固有稀疏散射特性，我们表明接收的信号可以表示为低级别的三阶张量，该张量可以接收张量秩分解，也称为典型的多层分解（CPD）。然后开发基于结构化的基于CPD的方法来估计通道参数。我们的分析表明，我们提出的方法所需的训练开销与O（u^2）一样低，在该训练中，u表示级联通道的稀疏性。提供仿真结果以说明所提出的方法的效率。
We consider the problem of downlink channel estimation for intelligent reflecting surface (IRS)-assisted millimeter Wave (mmWave) orthogonal frequency division multiplexing (OFDM) systems. By exploring the inherent sparse scattering characteristics of mmWave channels, we show that the received signals can be expressed as a low-rank third-order tensor that admits a tensor rank decomposition, also known as canonical polyadic decomposition (CPD). A structured CPD-based method is then developed to estimate the channel parameters. Our analysis reveals that the training overhead required by our proposed method is as low as O(U^2), where U denotes the sparsity of the cascade channel. Simulation results are provided to illustrate the efficiency of the proposed method.
