Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for $5$G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.

翻译：深层学习表明,在改善系统性能和降低5G美元和超过5G美元的网络的计算复杂性方面具有重要作用。在本文中,我们提议在深层学习的协助下采用新的频道估计方法,以支持最小的平方估算,这是一种低成本方法,但频道估计误差相对较高。实现这一目标的途径是利用MIMO(多投入多产出)系统,该系统在多普勒效应的严重影响下,在5G网络进行模拟时使用多路路剖面剖面图。数字结果显示,提议的深层学习辅助频道估计方法在以往工作中优于其他频道估计方法,以中值平方误表示。