Cooperative sparse spectrum mapping for cognitive radio networks in a history-aware framework

Spectrum Sensing (SS) is the first step to establish a cognitive radio network. Current non-cooperative spectrum sensing methods exhibit a poor performance in certain applications. Therefore, cooperation can be pursued to improve the sensing performance. In addition, some applications need overall space-frequency perspective of spectrum in which spectrum mapping can be applied instead of point-by-point spectrum sensing. Generally, the spectrum mapping algorithms lead to computationally extensive optimization problems. Reducing the computational costs of algorithms, would extend the application domain of these methods. In this paper we propose a solution to attain space-frequency spectrum map of cognitive radio networks with a low computational complexity using the past behavior of space and frequency variations in time. The proposed algorithm offers a solution to reduce complexity and estimation error of spectrum sensing by forming a sensing time queue.