Sensors | |
Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations | |
Muhammad Naeem1  Kandasamy Illanko1  Ashok Karmokar1  Alagan Anpalagan1  | |
[1] ELCE Department, Ryerson University, 350- Victoria Street, Toronto, ON M5B 2K3, Canada; E-Mails: | |
关键词: power allocation; nonlinear optimization; cognitive radio sensor network; | |
DOI : 10.3390/s130811032 | |
来源: mdpi | |
【 摘 要 】
Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a
【 授权许可】
CC BY
© 2013 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
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RO202003190033865ZK.pdf | 228KB | download |