  For many wireless communication applications, traffic pattern modeling of
radio signals combined with channel effects is much needed. While analytical
models are used to capture these phenomena, real world non-linear effects (e.g.
device responses, interferences, distortions, noise) and especially the
combination of such effects can be difficult to capture by these models. This
is simply due to their complexity and degrees of freedom which can be hard to
explicitize in compact expressions. In this paper, we propose a more model-free
approach to jointly approximate an end-to-end black-boxed wireless
communication scenario using software-defined radio platforms and optimize for
an efficient synthesis of subsequently similar 'pseudo-radio-signals'. More
precisely, we implement a generative adversarial network based solution that
automatically learns radio properties from recorded prototypes in specific
scenarios. This allows for a high degree of expressive freedom. Numerical
results show that the prototypes' traffic patterns jointly with channel effects
are learned without the introduction of assumptions about the scenario or the
simplification to a parametric model.
