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

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.

Deep Learning based Modeling of Wireless Communication Channel with Fading

In the realm of wireless communication, stochastic modeling of channels is instrumental for the assessment and design of operational systems. Deep learning neural networks (DLNN), including generative adversarial networks (GANs), are being used to approximate wireless Orthogonal frequency-division multiplexing (OFDM) channels with fading and noise, using real measurement data. These models primarily focus on channel output (y) distribution given input x: p(y|x), limiting their application scope. DLNN channel models have been tested predominantly on simple simulated channels. In this paper, we build both GANs and feedforward neural networks (FNN) to approximate a more general channel model, which is represented by a conditional probability density function (PDF) of receiving signal or power of node receiving power Prx: f_p_rx|d(()), where is communication distance. The stochastic models are trained and tested for the impact of fading channels on transmissions of OFDM QAM modulated signal and transmissions of general signal regardless of modulations. New metrics are proposed for evaluation of modeling accuracy and comparisons of the GAN-based model with the FNN-based model. Extensive experiments on Nakagami fading channel show accuracy and the effectiveness of the approaches.