Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks
This paper investigates a multi-band harvesting (EH) schemes under cognitive radio interweave framework. All secondary users are considered as EH nodes that are allowed to harvest energy from multiple bands of Radio Frequency (RF) sources. A win-win framework is proposed, where SUs can sense the spectrum to determine whether the spectrum is busy, and hence they may harvest from RF energy, or if it is idle, and hence they can use it for transmission. Only a subset of the SUs can sense in order to reduce sensing energy, and then machine learning is used to characterize areas of harvesting and spectrum usage. We formulate an optimization problem that jointly optimize number of sensing samples and sensing threshold in order to minimize the sensing time and hence maximize the amount of energy harvested. A near optimal solution is proposed using Geometric Programming (GP) to optimally solve the problem in a time-slotted period. Finally, an energy efficient approach based on multi-class Support Vector Machine (SVM) is proposed by involving only training SUs instead of all SUs.
A. Alsharoa et al., "Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks," Proceedings of the 2018 IEEE International Conference on Communications (2018, Kansas City, MO), Institute of Electrical and Electronics Engineers (IEEE), May 2018.
The definitive version is available at https://doi.org/10.1109/ICC.2018.8422511
2018 IEEE International Conference on Communications, ICC 2018 (2018: May 20-24, Kansas City, MO)
Electrical and Computer Engineering
Keywords and Phrases
Energy efficiency; Harvesting; Mathematical programming; Support vector machines, Cognitive radio network; Energy efficient; Geometric programming; Multi-class support vector machines; Near-optimal solutions; Optimization problems; Radio frequency source; Spectrum harvesting, Cognitive radio
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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01 May 2018