Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/210396
Title: Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT
Authors: Zhenzhen Hu;Ning Wei;Zhongpei Zhang
Year: 2017
Publisher: IEEE
Abstract: Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energy-harvesting scheme for max&x2013;min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.
Description: 
URI: http://localhost/handle/Hannan/210396
volume: 5
More Information: 23383,
23394
Appears in Collections:2017

Files in This Item:
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8003470.pdf2.91 MBAdobe PDF
Title: Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT
Authors: Zhenzhen Hu;Ning Wei;Zhongpei Zhang
Year: 2017
Publisher: IEEE
Abstract: Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energy-harvesting scheme for max&x2013;min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.
Description: 
URI: http://localhost/handle/Hannan/210396
volume: 5
More Information: 23383,
23394
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8003470.pdf2.91 MBAdobe PDF
Title: Optimal Resource Allocation for Harvested Energy Maximization in Wideband Cognitive Radio Network With SWIPT
Authors: Zhenzhen Hu;Ning Wei;Zhongpei Zhang
Year: 2017
Publisher: IEEE
Abstract: Wideband sensing-based cognitive radio with simultaneous wireless information and power transfer can be designed for efficient spectrum and energy usage. We first aim to maximize the sum energy harvested by all the energy harvesters subject to constraints on rate, transmit power, interference, and subchannel assignment. Due to the non-convexity of the formulated problem, we relax the integer variable and introduce an auxiliary variable to transform the original problem into a convex problem. Then, the Lagrangian and subgradient methods are adopted to obtain the optimal solutions. However, this scheme may lead to a severe fairness issue among different links. In view of this fact, we further propose an energy-harvesting scheme for max&x2013;min fairness. In particular, we aim to maximize the energy harvested by the worst case individual link. We show that this problem can be solved in a similar manner to the problem of sum harvested energy maximization. Simulation results are presented to verify the convergence and fairness performance of the proposed algorithm, and to reveal a novel tradeoff between the network harvested energy and sensing time.
Description: 
URI: http://localhost/handle/Hannan/210396
volume: 5
More Information: 23383,
23394
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8003470.pdf2.91 MBAdobe PDF