Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks

Understand

Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system.

• Most prior work in designing or learning new modulation schemes has focused on using highly simplified analytic channel models such as additive white Gaussian noise (AWGN), Rayleigh fading channels or similar.
• Recently, we proposed the usage of a generative adversarial networks (GANs) to jointly approximate a wireless channel response model (e.g.
• from real black box measurements) and optimize for an efficient modulation scheme over it using machine learning.