Radio frequency fingerprint recognition based on deep learning

Tang, Chunyang; Yan, Tianfeng; An, Yupeng

doi:10.1109/icitbs53129.2021.00177

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…27,28 explored the channel estimation using feed forward neural networks (FNNs) and 19 studied the RF signal classification in dynamic spectrum environments using CNNs. RF modulation classification and RF fingerprint recognition have been recently explored 29,30 as well. The goal of modulation classification is to identify and classify a given RF signal to a known modulation type whereas RF fingerprint recognition is a method to identify the annunciator or the transmitting circuit.…”

Section: Related Workmentioning

confidence: 99%

RF Signal Transformation and Classification using Deep Neural Networks

Khalid¹,

Karim²,

Rahnavard³

2022

Preprint

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Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data types that are suitable for off-the-shelf DNNs by introducing a convolutional transform technique. In addition, we propose a simple 5-layer convolutional neural network architecture (CONV-5) that can operate with raw RF I/Q data without any transformation. Further, we put forward an RF dataset, referred to as RF1024, to facilitate the future RF research. RF1024 consists of 8 different RF modulation classes with each class having 1000/200 training/test samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values. Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to demonstrate the improved classification performance.

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