Paper Info
Title
Modeling the large-scale visible light backscatter communication network.
Abstract
The future Internet-of-things (IoT) is motivating the development of our daily services as well as revolutionizing the way we interplay with our life. The ubiquitous visible light communication (VLC) technique has been seamlessly combined into the energy-efficient backscatter communication system, called the visible-light backscatter communication (VL-BackCom), for powering the massive number of IoT devices and prolonging their working-time. In the VL-BackCom system, the tag can modulate and backscatter the visible light signal (by switching the liquid crystal display (LCD) shutter) illuminated from light source to its nearby receiver. However, few work focuses on modeling and analyzing the performance of the large-scale VL-BackCom network. To this end, this paper makes the first attempt to model and investigate the network performance, namely the success VL-BackCom probability and network capacity, by borrowing the analytical tractable tool from stochastic geometry. The network topology is modeled using the analytical tractable generalized Gauss-Poisson process (GPP) under some justifiable assumptions, yielding a lower bound for practical VL-BackCom network with irregular deployment. The expressions of the success VL-BackCom probability and network capacity are clearly derived to characterize the VL-BackCom link's reliability as well as the spatial success transmission density, respectively. Moreover, the effects of backscatter parameters, say duty cycle and reflection coefficient, on network performance are also studied.
Year
Venue
Keywords
2017
Asia-Pacific Conference on Communications
Backscatter communication,visible light,network modeling,performance analysis,stochastic geometry
Field
DocType
ISSN
Stochastic geometry,Telecommunications network,Computer science,Duty cycle,Backscatter,Communications system,Visible light communication,Real-time computing,Network topology,Network performance
Conference
2163-0771
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Xiaozheng Wang100.34
Kaifeng Han200.34
Minglun Zhang323.75