IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 19, NO. 9, SEPTEMBER 2018 3081
Performance Analysis of Multi-Source Multi-Destination
Cooperative Vehicular Networks With the Hybrid Decode-
Amplify-Forward Cooperative Relaying Protocol
Hailin Xiao , Member, IEEE, Zhongshan Zhang, Senior Member, IEEE,
and Anthony Theodore Chronopoulos, Senior Member, IEEE
Abstract— This paper provides symbol-error-rate (SER) performance
analysis and minimum power allocation for multi-source multi-
destination cooperative vehicular networks using the hybrid decode-
amplify-forward (HDAF) cooperative relaying protocol. Previous studies
of power allocation minimize the outage probability subject to a total
power constraint. Our approach aims to minimize the power allocation
in order to maintain the SER below a specific threshold and thus it
achieves lower power consumption. Numerical tests show that HDAF
has significantly reduced SER compared with the forward strategies of
amplify-and-forward (AF) and decode-and-forward (DF). Furthermore,
the power consumption in our proposed approach is much less than that
in AF and DF.
Index Terms—Intelligent transportation systems, cooperative vehicular
communication, power allocation, HDAF, symbol-error-rate.
I. INTRODUCTION
W
IRELESS vehicular networks have shown to play a key role
in Intelligent Transportation Systems (ITS), where nodes are
usually integrated in infrastructures and vehicles [1]. Consequently,
power consumption of wireless nodes is constrained when increas-
ing both reliability and lifetime of vehicular networks [2]. There-
fore, energy-efficient transmission techniques are very important
for the energy constrained communication of devices between road
infrastructures and vehicles (I2V) [3].
To support various ITS applications, cooperative vehicular net-
works have been receiving growing attention by the scientific com-
munity and the industry [4], [5], which are applied to extend
coverage, enable network connectivity, and enhance link reliability
through distributed spatial diversity. On the other side, multi-source
Manuscript received March 31, 2017; revised August 15, 2017; accepted
October 19, 2017. Date of publication November 22, 2017; date of cur-
rent version September 7, 2018. This work was supportedin part by the
National Natural Science Foundation of China under Grant 61472094 and
Grant 61261018, in part by the Guangxi Natural Science Foundation
under Grant 2014GXNSFGA118007, and in part by the Key Research and
Development Plan Project of Zhejiang Province under Grant 2018C01059.
The Associate Editor for this paper was F. Qu. (Corresponding author:
Hailin Xiao.)
H. Xiao is with the College of Physics and Electronic Information Engi-
neering, Wenzhou University, Wenzhou 325035, China, and also with the Key
Laboratory of Cognitive Radio and Information Processing, Guilin University
of Electronic Technology, Ministry of Education, Guilin 541004, China
(e-mail: xhl_xiaohailin@163.com).
Z. Zhang is with the Beijing Engineering and Technology Research Center
for Convergence Networks and Ubiquitous Services, University of Science and
Technology Beijing, Beijing 100083, China (e-mail: zhangzs@ustb.edu.cn).
A. T. Chronopoulos is with the Department of Computer Science, University
of Texas at San Antonio, San Antonio, TX 78249 USA, and also with the
Department of Computer Engineering and Informatics, University of Patras,
26500 Rio, Greece (e-mail:antony.tc@gmail.com).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TITS.2017.2766267
multi-destination cooperative communication is able to reduce the
power consumption without degradation of network connectivity
and coverage, making it a potential candidate for the vehicular
communications [5]–[7]. For instance, when several sources such
as road infrastructures broadcast information to the entire vehicular
communication network, then vehicles can cooperatively forward
the broadcast messages to the destinations more efficiently. Note
that an important issue in multi-source multi-destination cooperative
vehicular networks turns out to be relaying protocols. There are
two major protocols: amplify-and-forward (AF) and decode-and-
forward (DF). Most of the existing works on relaying protocols
focus on these two major protocols. Another signal forwarding
protocol has been proposed that takes advantage of both AF and DF,
i.e., the so called hybrid decode-amplify-forward (HDAF) is taken
into consideration [8], [9]. In this paper, the adopted HDAF relaying
approach, where the relays will use the DF if they can decode the
information received from the sources successfully. Otherwise, AF
mode will be employed for forwarding the information.
Besides the considerations of relay cooperative protocols, another
important issue is how to lower power consumption for vehicular
communication networks [10]. The strategy, which can be employed
for allocating power between the source and the relay nodes, has
motivated a lot of research in recent years. Most of the work involving
power allocation focuses on minimizing the outage probability subject
to a total power constraint [9], [11], [12]. In this study, the problem
from a different perspective will be studied. Instead of minimizing
the outage probability, our approach tries to minimize the power
allocation in order to maintain the symbol-error-rate (SER) below a
specific threshold and as a consequence to lower power consumption.
To the best of our knowledge, few studies have been previously
proposed a lower power allocation strategy to achieve the desired
SER performance (e.g. see [9]). However, no study proposed how to
minimize the power allocation in a HDAF approach.
II. S
YSTEM MODEL
Our network model comprises several randomly distributed road-
way communication infrastructures (sources), and vehicles connect-
ing through an infrastructure once they fall into its coverage. In the
V2I networks, vehicles are capable of accessing the internet and
other broadband services through the infrastructures. Some theoretical
analysis and experimental results also demonstrate that the Rayleigh
channel model is available for V2I link [13], [14]. In this case,
vehicles can move at their desired speed and thus overtaking is
allowed. Our focus is on delivery of broadcast messages, and each
vehicle receives a broadcast packet and rebroadcasts the packet at
least once. Here, we do not consider a vehicle speed scheme.
Fig. 1 illustrates three source nodes S
i
, S
j
, S
k
and three des-
tination nodes D
i
, D
j
, D
k
. Although we limit ourselves to case
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