Localization based on active learning for cognitive radio networks

Most of existing works on primary user (PU) localization locate primary transmitter (PT) while the purpose of localization is to avoid interfering with primary receiver (PR). Therefore, it is more important to locate PR. In this paper, we propose a PR localization method based on active learning. The secondary user (SU) initiatively sends a probing signal to interfere with the PU, then the PU adapts transmit power and/or rate upon the interference signal and such transmit adaptations are observed by the SU, whereby the SU learns the PU´s strategy for transmit adaptations. From the observation, the SU estimates the distance between the PR and the SU. Finally, the location of the PR can be estimated by maximum likelihood estimation without prior information of the PR transmission power. Simulation results are provided to evaluate the effectiveness of the proposed schemes under different system setups.