Generative Adversarial Networks for Pseudo-Radio-Signal Synthesis

TL;DR

This paper introduces a GAN-based method for synthesizing realistic radio signals by learning from real-world data, capturing complex wireless communication effects without relying on traditional analytical models.

Contribution

It presents a novel, model-free approach using GANs to generate pseudo-radio-signals that accurately reflect real-world wireless traffic and channel effects.

Findings

Successfully learned traffic patterns and channel effects from real data

Generated signals closely match real-world prototypes

No assumptions or parametric simplifications needed

Abstract

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.