Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

• Main Authors: Haiyan Ye, Jiabao Jiang
• Format: Article
• Language: English
• Published: SpringerOpen 2021-04-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Linear weighted fusion; Cooperative spectrum sensing; Signal-to-noise ratio; Cognitive radio networks; Fusion center
• Online Access: https://doi.org/10.1186/s13638-021-01977-5

Abstract The lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.