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Channel modeling is a critical topic when considering designing, learning, orevaluating the performance of any communications system. Most prior work indesigning or learning new modulation schemes has focused on using highlysimplified analytic channel models such as additive white Gaussian noise(AWGN), Rayleigh fading channels or similar. Recently, we proposed the usage ofa generative adversarial networks (GANs) to jointly approximate a wirelesschannel response model (e.g. from real black box measurements) and optimize foran efficient modulation scheme over it using machine learning. This approachworked to some degree, but was unable to produce accurate probabilitydistribution functions (PDFs) representing the stochastic channel response. Inthis paper, we focus specifically on the problem of accurately learning achannel PDF using a variational GAN, introducing an architecture and lossfunction which can accurately capture stochastic behavior. We illustrate whereour prior method failed and share results capturing the performance of such assystem over a range of realistic channel distributions.

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