Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/234865
Title: Cognitive Radio With Self-Power Recycling
Authors: Hang Hu;Ying-Chang Liang;Hang Zhang;Boon-Hee Soong
Year: 2017
Publisher: IEEE
Abstract: In cognitive radio networks, a secondary user (SU) equipped with radio frequency (RF) energy-harvesting circuits can not only harvest the RF energy from the primary transmitter, but also recycle its self-power when transmitting. In this paper, we are concerned with the following design metrics: SU's harvested energy, SU's energy efficiency, and SU's harvesting efficiency, which is defined as the ratio of the average energy harvested by SU over its average energy consumption. We are interested in two tradeoff designs: one is the tradeoff between energy efficiency and harvested energy and the other is the tradeoff between energy efficiency and harvesting efficiency. Multiobjective optimization is used to solve the tradeoff problems. To simplify the original problems, we propose two schemes to obtain the lower bounds of the objective functions. The sensing threshold, sensing time, and transmit power of SU are jointly optimized to solve the tradeoff problems. Efficient algorithms are proposed to derive these design parameters. Simulation results are presented to validate the effectiveness of the proposed algorithms, to show the two tradeoff designs, and to validate the effects of system parameters on these tradeoffs.
URI: http://localhost/handle/Hannan/234865
volume: 66
issue: 7
More Information: 6201,
6214
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7765029.pdf1.04 MBAdobe PDF
Title: Cognitive Radio With Self-Power Recycling
Authors: Hang Hu;Ying-Chang Liang;Hang Zhang;Boon-Hee Soong
Year: 2017
Publisher: IEEE
Abstract: In cognitive radio networks, a secondary user (SU) equipped with radio frequency (RF) energy-harvesting circuits can not only harvest the RF energy from the primary transmitter, but also recycle its self-power when transmitting. In this paper, we are concerned with the following design metrics: SU's harvested energy, SU's energy efficiency, and SU's harvesting efficiency, which is defined as the ratio of the average energy harvested by SU over its average energy consumption. We are interested in two tradeoff designs: one is the tradeoff between energy efficiency and harvested energy and the other is the tradeoff between energy efficiency and harvesting efficiency. Multiobjective optimization is used to solve the tradeoff problems. To simplify the original problems, we propose two schemes to obtain the lower bounds of the objective functions. The sensing threshold, sensing time, and transmit power of SU are jointly optimized to solve the tradeoff problems. Efficient algorithms are proposed to derive these design parameters. Simulation results are presented to validate the effectiveness of the proposed algorithms, to show the two tradeoff designs, and to validate the effects of system parameters on these tradeoffs.
URI: http://localhost/handle/Hannan/234865
volume: 66
issue: 7
More Information: 6201,
6214
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7765029.pdf1.04 MBAdobe PDF
Title: Cognitive Radio With Self-Power Recycling
Authors: Hang Hu;Ying-Chang Liang;Hang Zhang;Boon-Hee Soong
Year: 2017
Publisher: IEEE
Abstract: In cognitive radio networks, a secondary user (SU) equipped with radio frequency (RF) energy-harvesting circuits can not only harvest the RF energy from the primary transmitter, but also recycle its self-power when transmitting. In this paper, we are concerned with the following design metrics: SU's harvested energy, SU's energy efficiency, and SU's harvesting efficiency, which is defined as the ratio of the average energy harvested by SU over its average energy consumption. We are interested in two tradeoff designs: one is the tradeoff between energy efficiency and harvested energy and the other is the tradeoff between energy efficiency and harvesting efficiency. Multiobjective optimization is used to solve the tradeoff problems. To simplify the original problems, we propose two schemes to obtain the lower bounds of the objective functions. The sensing threshold, sensing time, and transmit power of SU are jointly optimized to solve the tradeoff problems. Efficient algorithms are proposed to derive these design parameters. Simulation results are presented to validate the effectiveness of the proposed algorithms, to show the two tradeoff designs, and to validate the effects of system parameters on these tradeoffs.
URI: http://localhost/handle/Hannan/234865
volume: 66
issue: 7
More Information: 6201,
6214
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7765029.pdf1.04 MBAdobe PDF