基于不动点深度学习的IRS辅助毫米波移动通信系统全信道估计

Full Channel Estimation for IRS-assisted Millimeter-wave Mobile Communication Systems Based on Fixed Point Deep Learning

将智能反射面(IRS)与大规模MIMO结合能够保证和提高毫米波通信系统性能。针对基站(BS)-用户直连信道与用户-IRS-BS反射信道混叠场景,该文提出一种自适应的全信道估计方法。首先,引入辅助变量,采用原子范数将直连信道与反射信道的稀疏角度域子空间进行关联;然后,利用原子范数最小化将全信道估计问题建模为连续角度域稀疏矩阵重建规划;最后,基于不动点深度学习网络设计低复杂度的问题求解算法。该算法不仅能够克服传统基于模型解法中非线性估计算子对先验知识的依赖还可根据移动场景变化自适应调节算法复杂度。仿真结果表明,所提算法能够避免传统时分估计策略引起的差错传播效应,具有更高的估计精度和更低的复杂度。

Combining Intelligent Reflective Surface(IRS)with massive MIMO can guarantee and improve the performance of millimeter-wave communication systems.An adaptive full-channel estimation method is proposed for the Base Station(BS)-user direct-connect channel and user-IRS-BS reflective channel mixing scenario.First,auxiliary variables are introduced and atomic paradigms are used to correlate the sparse angledomain subspaces of the direct-connect and reflective channels;then,the full-channel estimation problem is modeled as a continuous angle-domain sparse matrix reconstruction planning by using atomic paradigm minimization;and finally,a low-complexity problem solving algorithm based on the immovable-point deep learning network is designed.The algorithm can not only overcome the dependence of the nonlinear estimation operator on a priori knowledge in the traditional model-based solution method but also adaptively adjust the complexity of the algorithm according to the changes of the mobile scene.Simulation results show that the proposed algorithm can avoid the error propagation effect caused by the traditional time-division estimation strategy,and has higher estimation accuracy and lower complexity.