基于超分辨率网络的级联信道估计方法

Cascaded Channel Estimation Based on a Super-Resolution Network

为了提高无线通信系统中级联信道估计的精度和稳定性,提出一种新颖的级联信道估计方法,融合了压缩感知模型和深度学习技术。具体地,将信道估计问题转化为角度域上稀疏信号恢复问题,进一步设计了基于超分辨率网络的角度估计模块,结合多尺度注意力机制,提高相关矩阵的分辨率,获得精细的角度估计结果。最后逐径估计幅度,完成信道重建。仿真表明,所提算法在估计精度方面优于基准算法,导频数目较少时依旧表现出良好性能。

To improve the accuracy and stability of cascaded channel estimation in wireless communication systems,this paper proposes a novel method that combines compressed sensing models with deep learning techniques.Specifically,we transform the channel estimation problem into a sparse signal recovery problem in the angular domain.Furthermore,an angle estimation module based on a super-resolution network is designed combined with a multi-scale attention mechanism,which enhances the resolution of the correlation matrix and achieves accurate angle estimation results.Finally,the amplitudes are calculated path-by-path to reconstruct the cascaded channel.Simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of the estimation accuracy and works well even with a small number of pilot symbols.