A robust energy efficiency power allocation algorithm in cognitive radio networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks (CRNs), which may lead to performance degradation in real systems with disturbances or uncertaintie...

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Bibliographic Details
Published inChina communications Vol. 15; no. 10; pp. 150 - 158
Main Authors Zhou, Mingyue, Zhao, Xiaohui
Format Journal Article
LanguageEnglish
Published China Institute of Communications 01.10.2018
College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
Key Laboratory of Information Science, College of Communication Engineering, Jilin University, Changchun 130012, China%Key Laboratory of Information Science, College of Communication Engineering, Jilin University, Changchun 130012, China
Subjects
cognitive radio
Energy efficiency
Interference
Optimization
power allocation
Resource management
Robustness
Signal to noise ratio
spectrum sharing
Uncertainty
power allocation
cognitive radio
spectrum sharing
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ISSN1673-5447
DOI10.1109/CC.2018.8485477

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Summary:In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks (CRNs), which may lead to performance degradation in real systems with disturbances or uncertainties, we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio (CR) systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint. The ellipsoid sets are used to describe the channel uncertainty, and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach. A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented. Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio (SINR) requirement of each cognitive user under channel uncertainty.
ISSN:1673-5447
DOI:10.1109/CC.2018.8485477