Sensor selection for an accurate closed form localization in cognitive radio networks: The trade-off between model accuracy and geometry structure

In cognitive radio networks (CRNs), localization of primary user is the key problem for location-aware spectrum allocation. In order to improve spectrum utilization and decrease interference to primary users, localization should be computationally efficient and as accurate as possible. Based on an accurate closed form localization method, the tradeoff between model accuracy and geometry structure is analyzed. By selecting part of the secondary users, the estimation accuracy is increased and the computation amount is reduced at the same time. Finally, the simulations are performed and the proposed tradeoff analysis is done. The simulation results show the significant improvement on the localization performance by sensor selection.