Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing
This work addresses the need for accurate and adaptive channel modeling in wireless communication systems, which is crucial for intelligent system design, but it appears incremental as it builds on existing deep learning methods with specific enhancements.
The paper tackled the problem of modeling time-varying wireless communication channels by proposing a deep learning neural network combined with a mixture density network to derive the conditional probability density function of receiving power, and it showed that this approach is more statistically accurate, faster, and more robust than previous models in experiments on Nakagami fading and Log-normal shadowing channels.
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.