Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing

TL;DR

This paper introduces a novel deep learning-based model combining mixture density networks and transfer learning to accurately and adaptively characterize time-varying wireless channels with fading and shadowing effects.

Contribution

It presents a new approach that improves statistical accuracy and adaptability of wireless channel modeling using deep neural networks with transfer learning.

Findings

More statistically accurate channel modeling

Faster adaptation to environmental changes

Enhanced robustness over previous models

Abstract

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.