论文标题
展开的深度神经网络（UDNN）用于高移动通道估计
Unfolded Deep Neural Network (UDNN) for High Mobility Channel Estimation
论文作者
论文摘要
高移动通道估计对于超出5G（B5G）或6G无线通信网络至关重要。本文与DM通信系统的高迁移率有关。首先，通过将通道作为稀疏载体的过度词典的乘积近似线性化来提出二维压缩感测问题，其中考虑了高移动率通道引起的多普勒效应。为了解决传统压缩传感算法的迭代太多并且耗时的问题，我们提出了一个展开的深度神经网络（UDNN）作为快速求解器，该快速求解器的灵感来自迭代式收缩率收缩率替代算法的结构（ISTA）。 UDNN中的所有参数（例如非线性变换，收缩阈值，测量矩阵等）都是端到端学习的，而不是被手工制作的。实验表明，对于OFDM高移动通道估计，所提出的UDNN的性能优于ISTA，同时保持非常快速的计算速度。
High mobility channel estimation is crucial for beyond 5G (B5G) or 6G wireless communication networks. This paper is concerned with channel estimation of high mobility OFDM communication systems. First, a two-dimensional compressed sensing problem is formulated by approximately linearizing the channel as a product of an overcomplete dictionary with a sparse vector, in which the Doppler effect caused by the high mobility channel is considered. To solve the problem that the traditional compressed sensing algorithms have too many iterations and are time consuming, we propose an unfolded deep neural network (UDNN) as the fast solver, which is inspired by the structure of iterative shrinkage-thresholding algorithm (ISTA). All the parameters in UDNN (e.g. nonlinear transforms, shrinkage thresholds, measurement matrices, etc.) are learned end-to-end, rather than being hand-crafted. Experiments demonstrate that the proposed UDNN performs better than ISTA for OFDM high mobility channel estimation, while maintaining extremely fast computational speed.
