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Sensors 2016, 16(3), 305;

Leasing-Based Performance Analysis in Energy Harvesting Cognitive Radio Networks

College of Information Science and Engineering, Hunan University, Changsha, 410012, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Academic Editor: Ingrid Moerman
Received: 30 December 2015 / Revised: 2 February 2016 / Accepted: 14 February 2016 / Published: 27 February 2016
(This article belongs to the Special Issue Intelligent Internet of Things (IoT) Networks)
Full-Text   |   PDF [420 KB, uploaded 27 February 2016]   |  


In this paper, we consider an energy harvesting cognitive radio network (CRN), where both of primary user (PU) and secondary user (SU) are operating in time slotted mode, and the SU powered exclusively by the energy harvested from the radio signal of the PU. The SU can only perform either energy harvesting or data transmission due to the hardware limitation. In this case, the entire time-slot is segmented into two non-overlapping fractions. During the first sub-timeslot, the SU can harvest energy from the ambient radio signal when the PU is transmitting. In order to obtain more revenue, the PU leases a portion of its time to SU, while the SU can transmit its own data by using the harvested energy. According to convex optimization, we get the optimal leasing time to maximize the SU’s throughput while guaranteeing the quality of service (QoS) of PU. To evaluate the performance of our proposed spectrum leasing scheme, we compare the utility of PU and the energy efficiency ratio of the entire networks in our framework with the conventional strategies respectively. The numerical simulation results prove the superiority of our proposed spectrum leasing scheme. View Full-Text
Keywords: cognitive radio; spectrum leasing; energy harvesting; optimal strategy; energy efficiency ratio cognitive radio; spectrum leasing; energy harvesting; optimal strategy; energy efficiency ratio

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Zeng, F.; Xu, J. Leasing-Based Performance Analysis in Energy Harvesting Cognitive Radio Networks. Sensors 2016, 16, 305.

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