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COVERAGE PERFORMANCE OF COGNITIVE RADIO NETWORKS POWERED BY RENEWABLE ENERGY

Part of: Geometric probability and stochastic geometry

Published online by Cambridge University Press:  24 May 2017

XIAOLONG CHEN Open the ORCID record for XIAOLONG CHEN [Opens in a new window] ,
XIANGBO MENG Open the ORCID record for XIANGBO MENG [Opens in a new window] ,
XIAOSHI SONG Open the ORCID record for XIAOSHI SONG [Opens in a new window]  and
CHUN SHAN
XIAOLONG CHEN*
Affiliation:
Jinhua Polytechnic, Jinhua, 321017, China email [email protected]
XIANGBO MENG
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China email [email protected], [email protected]
XIAOSHI SONG
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China email [email protected], [email protected]
CHUN SHAN
Affiliation:
School of Accountancy, Guangdong Polytechnic Normal University, Guangdong 510665, China email [email protected]
*
✉[email protected]
Rights & Permissions [Opens in a new window]

Abstract

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We analyse the coverage performance of cognitive radio networks powered by renewable energy. Particularly, with an energy harvesting module and energy storage module, the primary transmitters (PTs) and the secondary transmitters (STs) are assumed to be able to collect ambient renewables, and store them in batteries for future use. Upon harvesting sufficient energy, the corresponding PTs and STs (denoted by eligible PTs and STs) are then allowed to access the spectrum according to their respective medium access control (MAC) protocols. For the primary network, an Aloha-type MAC protocol is considered, under which the eligible PTs make independent decisions to access the spectrum with probability $\unicode[STIX]{x1D70C}_{p}$. By applying tools from stochastic geometry, we characterize the transmission probability of the STs. Then, with the obtained results of transmission probability, we evaluate the coverage (transmission nonoutage) performance of the overlay CR network powered by renewable energy. Simulations are also provided to validate our analysis.

Keywords

cognitive radioopportunistic spectrum accessstochastic geometryPoisson point processcoverage probabilityrenewable energy

MSC classification

Primary: 60D05: Geometric probability and stochastic geometry
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 287 - 305
Copyright
© 2017 Australian Mathematical Society 

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