Identification of legacy radios in a cognitive radio network using a radio frequency fingerprinting based method

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.