The real-time quantification of the effect of a wireless channel on the
transmitting signal is crucial for the analysis and the intelligent design of
wireless communication systems for various services. Recent mechanisms to model
channel characteristics independent of coding, modulation, signal processing,
etc., using deep learning neural networks are promising solutions. However, the
current approaches are neither statistically accurate nor able to adapt to the
changing environment. In this paper, we propose a new approach that combines a
deep learning neural network with a mixture density network model to derive the
conditional probability density function (PDF) of receiving power given a
communication distance in general wireless communication systems. Furthermore,
a deep transfer learning scheme is designed and implemented to allow the
channel model to dynamically adapt to changes in communication environments.
Extensive experiments on Nakagami fading channel model and Log-normal shadowing
channel model with path loss and noise show that the new approach is more
statistically accurate, faster, and more robust than the previous deep
learning-based channel models.

在这篇论文中，我们提出了一种将深度学习神经网络与混合密度网络模型相结合的新方法，以得到普通无线通信系统中给定通信距离的接收功率的条件概率密度函数（PDF）。此外，我们还设计和实现了一种深度迁移学习方案，使得信道模型能够动态适应通信环境的变化。通过在 Nakagami 衰落信道模型和 Log-normal 阴影信道模型上进行广泛实验，结果表明新方法比以前基于深度学习的信道模型在统计准确性、速度和鲁棒性方面更好。