Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks
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Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system. Prior work in manual modulation system design or learned modulation system design has largely focused on simplified analytic channel models such as additive white Gaussian noise (AWGN) or Rayleigh fading channels, in more recent work we consider the usage of generative adversarial networks (GANs) to jointly approximate of a wireless channel response and design an efficient encoding and decoding of information to robustly survive it. In this paper, we focus more specifically on characterizing how well a GAN can capture the stochastic nature of a typical wireless channel response, and the topic of effectively designing the network and loss function to accurately capture its stochastic behavior in a probabilistic sense. We illustrate the problems with certain approaches and share results capturing the performance of such as system over a range channel distributions.
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