Radio Frequency Signal Identification Using Transfer Learning Based on LSTM

• DOI: 10.1007/s00034-020-01417-7
• Publication Date: 2020-04-24T07:26:47Z
• Authors: Xueli Wang; Yufeng Zhang; Hongxin Zhang; Yixuan Li; Xiaofeng Wei
• Keywords: SIGNAL (programming language); Transfer of learning

Radio frequency distinct native attribute (RF-DNA) technology is very important in distinguishing RF devices. This paper presents a new method for distinguishing RF devices by combining transfer learning and long short-term memory (TL-LSTM). The main purpose of this paper is to identify which RF device sent the unknown RF signals. The data were collected from almost the same eight RF devices produced in 2011, 2014 or 2016. These RF devices emitted unintended signals at 2.4G bandwidth with frequency shift keying. The proposed method first used late production RF devices in 2011 or 2014 as source domain and transferred the trained model to target domain produced in 2016 and then used neural network LSTM model to identify the RF signals. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. The results reveal that the proposed method TL-LSTM can solve the problem of small sample training very well.