Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges

Abstract

In modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value from them is a problem that needs to be studied. In this paper, a big data processing architecture for radio signals is presented and a new approach of end-to-end signal processing based on deep learning is discussed in detail. The radio signal intelligent search engine is used as an example to verify the architecture, and the system components and experimental results are introduced. In addition, the applications of the architecture in cognitive radio, spectrum monitoring, and cyberspace security are introduced. Finally, challenges are discussed, such as unified representation of radio signal features, distortionless compression of wideband sampled data, and deep neural networks for radio signals.

Keywords

• Radio signals
• big data
• deep learning
• neural networks
• search engine
• cognitive radio