To meet the ever-increasing demand for higher data rates, 5G and 6G technologies are shifting transceivers to higher carrier frequencies, to support wider bandwidths and more antenna elements. Nevertheless, this solution poses several key challenges: i) increasing the carrier frequency and bandwidth leads to greater channel frequency selectivity in time and frequency domains, and ii) the greater the number of antennas the greater the the pilot overhead for channel estimation and the more prohibitively complex it becomes to determine the optimal precoding matrix. This paper presents two deep-learning frameworks to solve these issues. Firstly, we propose a 3D convolutional neural network (CNN) that is based on image super-resolution and captures the correlations between the transmitting and receiving antennas and the frequency domains to combat frequency selectivity. Secondly, we devise a deep learning-based framework to combat the time selectivity of the channel that treats channel aging as a distortion that can be mitigated through deep learning-based image restoration techniques. Simulation results show that combining both frameworks leads to a significant improvement in performance compared to existing techniques with little increase in complexity.

翻译：为满足日益增长的高数据速率需求，5G和6G技术正将收发器转向更高载波频率，以支持更宽带宽和更多天线单元。然而，这种解决方案带来了若干关键挑战：其一，增加载波频率和带宽会导致时频域信道频率选择性增强；其二，天线数量越多，信道估计所需的导频开销越大，确定最优预编码矩阵的计算复杂度也越高。本文提出两种深度学习框架以解决这些问题。首先，我们提出一种基于图像超分辨率的3D卷积神经网络（CNN），该网络通过捕捉发射与接收天线及频域间的相关性来对抗频率选择性。其次，我们设计了一种基于深度学习的框架来应对信道的时间选择性，该框架将信道老化视为可通过基于深度学习的图像复原技术缓解的失真。仿真结果表明，相较于现有技术，结合两种框架能在复杂度仅小幅增加的情况下实现显著的性能提升。