TY - GEN
T1 - Identification of legacy radios in a cognitive radio network using a radio frequency fingerprinting based method
AU - Hu, Nansai
AU - Yao, Yu Dong
PY - 2012
Y1 - 2012
N2 - Cognitive radio (CR) networks provide an open architecture for effectively utilizing communication resources through flexible opportunistic spectrum access methods. To successfully realize its benefits and minimize the misuses of a CR network, distinguishing radio/user classes (legacy radios/users versus secondary radios/users) and individual radio/user terminals (within one class/type) is a critical and challenging task in CR network operation. In this paper, we propose a radio frequency fingerprinting (RFF) based approach combined with machine learning algorithms to differentiate radio/user classes and terminals. In our experiments, the proposed method is implemented for distinguishing radio class (MOTOROLA walkie talkies (as legacy radios) versus Universal Software Radio Peripheral (USRP) (as secondary radios)) and distinguishing individual radio terminals within one radio class. The experimental results demonstrate that the proposed method is very effective in differentiating radio types and radio terminals.
AB - Cognitive radio (CR) networks provide an open architecture for effectively utilizing communication resources through flexible opportunistic spectrum access methods. To successfully realize its benefits and minimize the misuses of a CR network, distinguishing radio/user classes (legacy radios/users versus secondary radios/users) and individual radio/user terminals (within one class/type) is a critical and challenging task in CR network operation. In this paper, we propose a radio frequency fingerprinting (RFF) based approach combined with machine learning algorithms to differentiate radio/user classes and terminals. In our experiments, the proposed method is implemented for distinguishing radio class (MOTOROLA walkie talkies (as legacy radios) versus Universal Software Radio Peripheral (USRP) (as secondary radios)) and distinguishing individual radio terminals within one radio class. The experimental results demonstrate that the proposed method is very effective in differentiating radio types and radio terminals.
KW - Cognitive radio
KW - machine learning
KW - radio frequency fingerprinting
UR - http://www.scopus.com/inward/record.url?scp=84871987901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871987901&partnerID=8YFLogxK
U2 - 10.1109/ICC.2012.6364436
DO - 10.1109/ICC.2012.6364436
M3 - Conference contribution
AN - SCOPUS:84871987901
SN - 9781457720529
T3 - IEEE International Conference on Communications
SP - 1597
EP - 1602
BT - 2012 IEEE International Conference on Communications, ICC 2012
T2 - 2012 IEEE International Conference on Communications, ICC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -