论文标题
MMWave Massive Mimo Systems的可训练近端梯度下降频道估计
Trainable Proximal Gradient Descent Based Channel Estimation for mmWave Massive MIMO Systems
论文作者
论文摘要
在这封信中，我们解决了大型MIMO通信系统中毫米波频道估计的问题。利用Beamspace中MMWave通道的稀疏性，我们将估计问题作为稀疏信号恢复问题提出。为此，我们提出了一个基于深度学习的近端梯度下降网络（TPGD-NET）。 TPGD-NET将迭代近端梯度下降（PGD）算法展开为层网络，其中梯度下降步长集作为可训练的参数。此外，我们用神经网络替换PGD算法中的近端操作员，该神经网络利用数据驱动的先验通道信息隐含地执行近端操作。为了进一步增强特征信息跨层的传输，我们将跨层特征注意融合模块引入TPGD-NET。我们在Saleh-Valenzuela频道模型和DeepMimo数据集上进行的模拟结果表明，与最新的MMWave频道估计器相比，TPGD-NET的性能出色。
In this letter, we address the problem of millimeter-Wave channel estimation in massive MIMO communication systems. Leveraging the sparsity of the mmWave channel in the beamspace, we formulate the estimation problem as a sparse signal recovery problem. To this end, we propose a deep learning based trainable proximal gradient descent network (TPGD-Net). The TPGD-Net unfolds the iterative proximal gradient descent (PGD) algorithm into a layer-wise network, with the gradient descent step size set as a trainable parameter. Additionally, we replace the proximal operator in the PGD algorithm with a neural network that exploits data-driven prior channel information to perform the proximal operation implicitly. To further enhance the transfer of feature information across layers, we introduce the cross-layer feature attention fusion module into the TPGD-Net. Our simulation results on the Saleh-Valenzuela channel model and the DeepMIMO dataset demonstrate the superior performance of TPGD-Net compared to state-of-the-art mmWave channel estimators.
