  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.
