Multi-Band RF Energy and Spectrum Harvesting in Cognitive Radio Networks

Abstract

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.

Meeting Name

2018 IEEE International Conference on Communications, ICC 2018 (2018: May 20-24, Kansas City, MO)

Department(s)

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)

978-1-5386-3180-5

International Standard Serial Number (ISSN)

1938-1883

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 May 2018

Link to Full Text

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