  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.
