Dynamic PSD map construction based on compressed sensing in Cognitive Radio networks

In Cognitive Radio systems, using spatial-frequency opportunistic reuse is emerged interestingly by constructing and deploying spatial-frequency PSD maps. Since the CR sensors are distributed in the region of support, the sensed PSD by each sensor should be transmitted to a fusion center (FC) in order to construct the PSD maps in space and frequency domains and when the number of sensors is large, this data transmission can be challenging. Static (standard) compressive sampling algorithms can be used to reconstruct the received PSD of sensors, but to construct the dynamic of PSD maps which caused by changes in primary users´ traffic and locations, we need dynamic algorithms. In this paper we propose a compressive sensing based method which considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors´ and FC. By using the proposed method, an acceptable PSD map for CR purposes can be achieved by only 20% of full data transmission between sensors and FC. Also, simulation results show the robustness of the proposed method against the channel variations and diverse compression ratios in comparison with similar static methods.