Energy-Efficiency Joint Cooperative Spectrum Sensing and Power Allocation Scheme for Green Cognitive Radio Network: A Soft Decision Fusion Approach
American Journal of Networks and Communications
Volume 7, Issue 2, June 2018, Pages: 6-16
Received: May 22, 2018;
Accepted: Jul. 1, 2018;
Published: Aug. 2, 2018
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Nouhoum Satarou Abdoul Galeb Yari, School of Information Engineering, Wuhan University of Technology, Wuhan, China; Key Lab Broadband Wireless Communication and Sensor Networks, Ministry of Education, Wuhan University of Technology, Wuhan, China
Mbembo Loundou Varus, School of Information Engineering, Wuhan University of Technology, Wuhan, China
Dong Doan Van, School of Information Engineering, Wuhan University of Technology, Wuhan, China; Key Lab Broadband Wireless Communication and Sensor Networks, Ministry of Education, Wuhan University of Technology, Wuhan, China
This paper focus on a jointly spectrum sensing parameter and energy efficiency (EE) optimization problem in OFDMA CRN system for enabling Green Communication. In this perspective, we firstly propose an algorithm to choose less spatially-correlated cognitive users to reduce the shadowing effect in wireless network. Furthermore, based on Lagrangian duality theorem with the aid of parametric transformation, the algorithm called an Iterative Dinkelbach Scheme (IDS) is proposed to optimize both transmission power allocation and sensing duration of the cognitive users (Cus) for maximizing Energy Efficiency under the constraints of overall outage of cognitive network, interference to the PU, maximum transmission power and minimum data rate requirement. Numerical result proves the effectiveness of our proposed algorithm. Compared with existing schemes, our proposed scheme outperforms in enhancing the EE with the same parameters.
Nouhoum Satarou Abdoul Galeb Yari,
Mbembo Loundou Varus,
Dong Doan Van,
Energy-Efficiency Joint Cooperative Spectrum Sensing and Power Allocation Scheme for Green Cognitive Radio Network: A Soft Decision Fusion Approach, American Journal of Networks and Communications.
Vol. 7, No. 2,
2018, pp. 6-16.
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