论文标题
RIS协助通信的基于监督学习的稀疏渠道估计
Supervised Learning based Sparse Channel Estimation for RIS aided Communications
论文作者
论文摘要
当直接路径受到损害时，可使用可重构的智能表面（RIS）可用于建立视线（LOS）通信，这在毫米波（MMWave）网络中是常见的发生。在本文中，我们专注于此类网络的上行链路通道估计。我们通过离散基地（BS）的到达角度（AOAS）来将其作为稀疏信号恢复问题。单网格和离网AOA被分别考虑。在网格情况下，我们提出了一种算法来估计直接和RIS通道。基于监督学习训练的神经网络用于估计离网病中的残留角度以及两种情况下的AOA。数值结果表明，在这两种情况下，所提出的算法的性能提高。
An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.
