A Low Complexity Algorithm for Intelligent Reflective Surface-assisted Tera Hertz Channel Estimation
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摘要: 信道估计是智能反射表面(IRS)辅助太赫兹(THz)通信中的一大挑战。为了减少由收发端天线和IRS反射元件数增加引起的信道估计导频开销过大的问题，该文提出一种基于正则平行因子(CP)分解的信道估计算法。首先在分析信道特点的基础上对IRS阵元进行分组设计，然后将IRS辅助的无线通信信道表示为统一数学表达式，接着利用多天线THz信道固有的低秩结构将信号接收矩阵构建成一个3维张量，并且利用基于正则平行因子分解算法对张量进行分解，最后利用相关性对信道参数进行估计。通过蒙特卡罗仿真表明，该算法在信道传输条件相同的情况下相对于基准算法有4.28 dB和7.12 dB左右的性能提升，并且具有更低的计算复杂度。Abstract: Channel estimation is a major challenge in Tera Hertz (THz) communication assisted by Intelligent Reflecting Surfaces (IRS). To reduce the problem of large channel estimation pilot overhead caused by an increase in the number of receiving/transmitting antennas and IRS reflecting elements, a channel estimation algorithm based on Canonical decomposition/Parallel factors (CP) decomposition is proposed. Firstly, based on the analysis of the channel characteristics, the IRS elements are designed by grouping. Then, the wireless communication channel assisted by IRS is expressed in a unified mathematical expression. Subsequently, the received signal matrix is constructed into a three-dimensional tensor by utilizing the inherent low-rank structure of the THz channel with multiple antennas. The tensor is decomposed using the CP decomposition algorithm, and the channel parameters are estimated using correlation. Monte Carlo simulations show that the proposed algorithm has a performance improvement of around 4.28 dB and 7.12 dB compared to the benchmark algorithm under the same channel transmission conditions, and has a lower computational complexity.
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