Framework for automatic signal classification techniques (FACT) for software defined radios

Abstract

The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) for software defined radios (SDR) capable of classifying multiple signals simultaneously. The focus of this work is to create a modular classification framework to facilitate the testing and implementation of new classification methods. The framework is divided into three parts: (i) Sensor resource manager (SRM), which performs the initial signal detection, preprocessing and the delegation of secondary receivers to the corresponding signals of interest (SOIs); (ii) Modulation classifier block (MCB), which takes the received signal from SRM and performs the required modulation classification and (iv) Data library and statistical block contains all the templates required to perform classification, thresholds for signal detection and also known parameters of expected signals. To prove the feasibility of the framework, FACT is implemented and tested on a Universal Software Radio Peripheral (USRP) test bed using GNU radio signal processing toolkit. We evaluate the performance of signal detection based on the probability of detection (P d ) in varying signal-to-noise ratios (SNR). Additionally, the USRP based experiments demonstrate FACT operating as a single unit, preforming both blind detection and classification of multiple SOIs using different classification methods.