Reliability analysis for wireless communication networks via dynamic Bayesian network

doi:10.23919/JSEE.2023.000130

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1368-1374.doi: 10.23919/JSEE.2023.000130

• Reliability • Previous Articles

Reliability analysis for wireless communication networks via dynamic Bayesian network

Shunqi YANG¹(), Ying ZENG²^,³^,*(), Xiang LI²(), Yanfeng LI²(), Hongzhong HUANG²()

¹ School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
² School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
³ China University of Petroleum at Karamay, Karamay 834000, China

Received:2021-01-14 Online:2023-10-18 Published:2023-10-30
Contact: Ying ZENG E-mail:shunqiyang@foxmail.com;zengying@uestc.edu.cn;lixiang@std.uestc.edu.cn;yanfengli@uestc.edu.cn;hzhuang@uestc.edu.cn
About author:
YANG Shunqi was born in 1982. He received his bachelor’s and M.S. degrees in mechanical engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2007 and 2010, respectively. He is pursuing his Ph.D. degree in the School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China. His research interests include system reliability analysis and prognostic and health management. E-mail: shunqiyang@foxmail.com

ZENG Ying was born in 1994. He received his M.S. and Ph.D. degrees in mechanical engineering from University of Electronic Science and Technology of China, Chengdu, China, in 2019 and 2023, respectively. He is a post doctor at University of Electronic Science and Technology of China. His research interests include reliability analysis of electronics and prognostics health management for electronics. E-mail: zengying@uestc.edu.cn

LI Xiang was born in 1989. He received his M.S. degree from China University of Petroleum-Beijing in 2012. He is a Ph.D. at the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China. His research interests include system reliability analysis and prognostics health management. E-mail: lixiang@std.uestc.edu.cn

LI Yanfeng was born in 1981. He received his Ph.D. degree in mechatronics engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 2013. He is currently a professor with the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China. His research interests include reliability modeling and analysis of complex systems, dynamic fault tree analysis, and Bayesian networks modeling and probabilistic inference. E-mail: yanfengli@uestc.edu.cn

HUANG Hongzhong was born in 1963. He is a full professor of mechanical engineering with the School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China. He is also the Director of the Center for System Reliability and Safety, University of Electronic Science and Technology of China. He has held visiting appointments at several universities in the USA, Canada, and Asia. His research interests include reliability engineering, optimization design, fuzzy sets theory, and product development. E-mail: hzhuang@uestc.edu.cn
Supported by:
This work was supported by the Chinese Universities Scientific Fund (ZYGX2020ZB022) and the National Natural Science Foundation of China (51775090).

Abstract

Abstract:

The dynamic wireless communication network is a complex network that needs to consider various influence factors including communication devices, radio propagation, network topology, and dynamic behaviors. Existing works focus on suggesting simplified reliability analysis methods for these dynamic networks. As one of the most popular modeling methodologies, the dynamic Bayesian network (DBN) is proposed. However, it is insufficient for the wireless communication network which contains temporal and non-temporal events. To this end, we present a modeling methodology for a generalized continuous time Bayesian network (CTBN) with a 2-state conditional probability table (CPT). Moreover, a comprehensive reliability analysis method for communication devices and radio propagation is suggested. The proposed methodology is verified by a reliability analysis of a real wireless communication network.

Key words: dynamic Bayesian network (DBN), wireless communication network, continuous time Bayesian network (CTBN), network reliability

Shunqi YANG, Ying ZENG, Xiang LI, Yanfeng LI, Hongzhong HUANG. Reliability analysis for wireless communication networks via dynamic Bayesian network[J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1368-1374.

Figures/Tables 12

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References 30

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B	A
B	1	2
1	$1 - \dfrac{ { { {\rm{e} }^{ - \lambda t} }\left( {1 - { {\rm{e} }^{ - \alpha \lambda t} } } \right)} }{ {\alpha \left( {1 - { {\rm{e} }^{ - \lambda t} } } \right)} }$	$1 - {{\rm{e}}^{ - \alpha \lambda t} }$
2	$\dfrac{ { { {\rm{e} }^{ - \lambda t} }\left( {1 - { {\rm{e} }^{ - \alpha \lambda t} } } \right)} }{ {\alpha \left( {1 - { {\rm{e} }^{ - \lambda t} } } \right)} }$	${ {\rm{e} }^{ { - \alpha \lambda t} } }$

Node	Description	Node	Description
T	Communication fails	X3	Antenna fails
M1	Transmitting failure	X4	Earphone fails
M2	Receiving failure	X5	Screen fails
M3	Power system fails	X6	Battery I fails
X1	Microphone fails	X7	Battery II fails
X2	Baseband fails

D1		1	2
Xi (i=1, 2, ···, 7)	1	$1 - \dfrac{ { { {\rm{e} }^{ - {\lambda _i}t} }\displaystyle\int_0^t { {f_{D1} }\left( { {t_{D1} } } \right){ {\rm{e} }^{ {\lambda _i}{t_{D1} } } }{\rm{d} }{t_{D1} } } } }{ {\displaystyle\int_0^t { {f_{D1} }\left( { {t_{D1} } } \right){\rm{d} }{t_{D1} } } } }$	0
Xi (i=1, 2, ···, 7)	2	$\dfrac{ { { {\rm{e} }^{ - {\lambda _i}t} }\displaystyle\int_0^t { {f_{D1} }\left( { {t_{D1} } } \right){ {\rm{e} }^{ {\lambda _i}{t_{D1} } } }{\rm{d} }{t_{D1} } } } }{ {\displaystyle\int_0^t { {f_{D1} }\left( { {t_{D1} } } \right){\rm{d} }{t_{D1} } } } }$	1

Number	Device	λ	Number	Device	λ
X1	Microphone	0.00004	X5	Screen	0.00001
X2	Baseband	0.00002	X6	Battery I	0.000067
X3	Antenna	0.000023	X7	Battery II	0.000067
X4	Earphone	0.00004

W2	W1
W2	1	2
1	$ {F_{W2}} $	$ {F_{W2}} $
2	$ {R_{W2}} $	$ {R_{W2}} $

Reliability analysis for wireless communication networks via dynamic Bayesian network

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