在智能超表面(Reconfigurable Intelligent Surface,RIS)辅助的无线通信系统中,信道状态信息(Channel Situation Information,CSI)是RIS反射相位矩阵优化的前提和基础.为了获得精准的CSI,针对多RIS辅助的多输入单输出(Multiple Input Single Output,MISO)通信系统,提出了一种训练序列优化和线性最小均方误差(Linear Minimum Mean Squared Error,LMMSE)方法相结合的信道估计方法.该方法使用LMMSE方法估计级联信道的CSI;在所得CSI的基础上,对RIS的反射矩阵和基站(Base Station,BS)发射的训练序列进行了联合优化,最小化原本信道估计的均方误差(Mean Squared Error,MSE),进一步提高了信道估计的精度.仿真结果验证了所提信道估计方法和双RIS联合训练反射矩阵与训练序列优化设计的有效性.与各种基准方法进行比较,具有更高的CSI估计精度.
Abstract
In a wireless communication system assisted by the Reconfigurable Intelligent Surface(RIS),Channel State Information(CSI)is the premise and foundation for optimizing the RIS reflection phase matrix.In order to obtain accurate CSI,a channel estimation method combining training sequence optimization and Linear Minimum Mean Squared Error(LMMSE)method is proposed for multi-RIS assisted Multiple Input Single Output(MISO)communication systems.This method uses LMMSE method to estimate the CSI of the cascaded channel;based on the obtained CSI,the reflection matrix of the RIS and the training sequence transmitted by the Base Station(BS)are jointly optimized to minimize the Mean Squared Error(MSE)of the original channel estimation,further improving the accuracy of channel estimation.Simulation results verify the effectiveness of the proposed channel estimation method and the joint training reflection matrix and training sequence optimization design of dual RIS.Compared with various benchmark methods,it has higher CSI estimation accuracy.
维普
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