Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Conference

IEEE International Conference on Communications (ICC)

Publication Version

Accepted Manuscript

Link to Published Version

https://doi.org/10.1109/ICC.2018.8422511

Publication Date

2018

Journal or Book Title

2018 IEEE International Conference on Communications (ICC)

DOI

10.1109/ICC.2018.8422511

Conference Title

IEEE International Conference on Communications (ICC)

Conference Date

May 20-24, 2018

City

Kansas City, MO

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.

Comments

This is a manuscript of a proceeding published as Alsharoa, Ahmad, Nathan M. Neihart, Sang W. Kim, and Ahmed E. Kamal. "Multi-band RF Energy and Spectrum Harvesting in Cognitive Radio Networks." In 2018 IEEE International Conference on Communications (ICC), (2018): 1-6. DOI: 10.1109/ICC.2018.8422511. Posted with permission.

Rights

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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