TY - GEN
T1 - An Effective Radio Frequency Signal Classification Method Based on Multi-Task Learning Mechanism
AU - Liu, Hongwei
AU - Hao, Chengyao
AU - Peng, Yang
AU - Wang, Yu
AU - Ohtsuki, Tomoaki
AU - Gui, Guan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.
AB - With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.
KW - automatic modulation classification
KW - deep learning
KW - multi-task learning
KW - Radio frequency fingerprint
UR - http://www.scopus.com/inward/record.url?scp=85146985654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146985654&partnerID=8YFLogxK
U2 - 10.1109/VTC2022-Fall57202.2022.10012794
DO - 10.1109/VTC2022-Fall57202.2022.10012794
M3 - Conference contribution
AN - SCOPUS:85146985654
T3 - IEEE Vehicular Technology Conference
BT - 2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Y2 - 26 September 2022 through 29 September 2022
ER -