CommRad RF: A dataset of communication radio signals for detection, identification and classification

The rapid growth in wireless technology has revolutionized the way of living but at the same time, raising security concerns of unauthorized access of spectrum, both military and commercial sectors. The subject of Radio Frequency (RF) fingerprinting has got special attention in recent years. Researchers proposed various datasets of radio signals of different types of devices (drones, cell phones, IoT, and Radar). However, presently there is no freely available dataset on walkie-talkies/commercial radios. To fill out the void, we present an innovative dataset including more than 2700 radio signals captured from 27 radios located in an indoor multipath environment. This dataset can enhance the security of the communication channels by providing the possibility to analyse and detect any unauthorized source of transmission. Furthermore, we also propose two innovative deep learning models named Light Weight 1DCNN and Light Weight Bivariate 1DCNN, for efficient data processing and learning patterns from the complex dataset of radio signals.