Citation: Li, Z.; Hong, H.; Pang, L.;
Mei, M.; Yang, Q. Stochastic Network
Calculus-Aided Delay Analysis of
Wireless-Power Line Mixed
Networks. Electronics 2022, 11, 1326.
https://doi.org/10.3390/
electronics11091326
Academic Editor: Djuradj Budimir
Received: 22 March 2022
Accepted: 19 April 2022
Published: 22 April 2022
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Article
Stochastic Network Calculus-Aided Delay Analysis of
Wireless-Power Line Mixed Networks
Zheng Li
1
, Haiming Hong
1
, Lin Pang
1
, Muyu Mei
2,3
and Qinghai Yang
2,3,
*
1
State Grid Laboratory of Power Line Communication Application Technology, Shenzhen Guodian Technology
Communication Co., Ltd., Shenzhen 518028, China; lizheng3@sgitg.sgcc.com.cn (Z.L.);
honghaimin@sgitg.sgcc.com.cn (H.H.); panglin@sgitg.sgcc.com.cn (L.P.)
2
State Key Laboratory of ISN, School of Telecommunications Engineering, Xidian University, 2 Taibainan-lu,
Xi’an 710071, China; mei_muyu@163.com
3
Lab of Industrial Internet, Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China
* Correspondence: qhyang@xidian.edu.cn
Abstract:
In this paper, we investigate the delay performance of a wireless-power line mixed network
via a stochastic network calculus (SNC)-based approach. The data transmission in this mixed network
is modeled by a two-stage tandem queue, wherein the data is first relayed through a wireless fading
channel and then transmitted over a power line communication (PLC) system. The Rayleigh fading
captures the wireless fading channel; whereas, the PLC channel gain is characterized by the log-
normal distribution. The statistical characteristics of the service processes of both the wireless channel
and PLC channel are derived. With any given traffic arrival and the service capability derived, the
delay can be easily bounded via SNC.
Keywords: stochastic network calculus; power line communications; delay
1. Introduction
Due to the rapidly availability and huge geographical area coverage of power lines,
power line communication (PLC) has been widely utilized to meet the ever-increasing
user demand for speedy access to data [
1
]. For example, PLC has been applied for smart
grid communication from transmission and distribution networks to consumer-end home
area networks, for enabling the local area network and for home automation using indoor
wiring infrastructure [2].
However, different from conventional wired communication, PLC has its own set of
challenges, since the power lines were not initially designed for communication purposes.
In particular, the channel fading, noise power and attenuation in PLC randomly fluctuate
as the location, time and frequency vary. Thus, it remains a great challenge to integrate
PLC into modern communication system and to model PLC channels.
Additive noise in PLC systems is more complex than that in wired communications
since it is a mixture of background noise and impulsive noise, which can be described by
the Nakagami-m distribution and middleton class A distribution [
3
]. In addition to these
two noise models, Bernoulli–Gaussian distributions are widely utilized to model both types
of noises in the literature. Similar to conventional wired and wireless communications,
the PLC channel is also affected by multiplicative noise due to the adverse influence of
multipath and mismatches in the impedances at the joints. These effects can be well imitated
by Rician and Rayleigh fading models [
4
]. Furthermore, the log-normal distribution has
been proven to be an excellent model for PLC communications by the measurement
campaign conducted in US urban and sub-urban areas over medium voltage power lines
across frequencies of 1.8 to 30 MHz [5].
The PLC channel model has been thoroughly investigated, and the performance of
PLC system has been well analyzed over recent decades. The attention and the focus of
Electronics 2022, 11, 1326. https://doi.org/10.3390/electronics11091326 https://www.mdpi.com/journal/electronics
