Unauthorized Broadcasting Identification: A Deep LSTM Recurrent Learning Approach

Radio broadcasting plays an important role in our daily life. Meanwhile, with the development of wireless communications, the application of software-defined radio platforms gives rise to cheap and easy design of illegal broadcasting stations. These unauthorized broadcasting stations sometimes illegally occupy licensed frequency band, especially associated with amateur radios and unlicensed personal communication devices and services. These unauthorized broadcasting stations may severely interfere with the authorized broadcasting and further disrupt the management of spectrum resource in civil applications, such as emergency services and air traffic control. However, it still remains a challenging task to automatically and effectively identify the unauthorized broadcasting in complicated electromagnetic environments. Aiming at developing an intelligent and efficient unauthorized broadcasting identification system, in this article, a novel identification approach is proposed based on long short-term memory (LSTM) recurrent neural network (RNN), and LabVIEW software. In our approach, first, a series of LabVIEW applications are developed to drive USRP 2930s for the acquisition of broadcasting signals. Then, the LSTM identification network is proposed to recognize unauthorized broadcasting. Through the special gate structure inside, the proposed LSTM framework can effectively extract the distinguishing features, such as channel state information and RF device fingerprinting. Simulation results show that the proposed LSTM-based approach perform better than other contrastive methods, especially in identification accuracy. Implementation results also demonstrate that the proposed method has an outstanding unauthorized broadcasting identification performance with a high accuracy, i.e., identify the unauthorized broadcasting signals with 99.83% accuracy at the licensed frequency of 107.8 MHz, in realistic electromagnetic environments.