Agent-Based Simulation and Its Application to Analyze Combat Effectiveness in Network-Centric Warfare Considering Communication Failure Environments

Abstract

Many parts of platforms are expected to be replaced by unmanned systems in modern warfare. All the assets and supporting vehicles are linked to each other with a communication network, and it is called the network-centric warfare environment. Hence, it is critical when communication failure occurs during engagement in ground battlefield because this failure will directly affect overall combat effectiveness of one’s owned assets. However, research regarding communication failure issues is scarce. We herein propose a new agent-based modeling process to measure the overall combat effectiveness combined with communication success ratio, based on the terrain condition of the ground engagement. Additionally, we provide the effectiveness analysis result when a communication repeater is applied during communication failure as an alternative measure.

Full text

Research Article
Agent-Based Simulation and Its Application to Analyze
Combat Effectiveness in Network-Centric Warfare Considering
Communication Failure Environments
Jaeyeong Lee ,1Sunwoo Shin,2Moonsung Park ,2and Chongman Kim 3
1Senior Research Fellow, Industrial and Academic Cooperative Group, Myongji University, 116, Myongji-ro, Cheoin-gu,
Yongin-si, Gyeonggi-do, Republic of Korea
2Master’s Course, Department of Industrial Engineering and Management, Myongji University, 116, Myongji-ro, Cheoin-gu,
Yongin-si, Gyeonggi-do, Republic of Korea
3Professor, Department of Industrial Engineering and Management, Myongji University, 116, Myongji-ro, Cheoin-gu,
Yongin-si, Gyeonggi-do, Republic of Korea
Correspondence should be addressed to Chongman Kim; chongman@mju.ac.kr
Received 27 July 2018; Revised 23 October 2018; Accepted 7 November 2018; Published 17 December 2018
Academic Editor: Giuseppe D'Aniello
Copyright ©  Jaeyeong Lee et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Many parts of platforms are expected to be replaced by unmanned systems in modern warfare. All the assets and supporting
vehicles are linked to each other with a communication network, and it is called the network-centric warfare environment. Hence,
it is critical when communication failure occurs during engagement in ground battleeld because this failure will directly aect
overall combat eectiveness of one’s owned assets. However, research regarding communication failure issues is scarce. We herein
propose a new agent-based modeling process to measure the overall combat eectiveness combined with communication success
ratio,basedontheterrainconditionofthegroundengagement.Additionally, we provide the eectiveness analysis result when a
communication repeater is applied during communication failure as an alternative measure.
1. Introduction
To construct a war-game model, the Lanchester-type equa-
tion is a typical tool to generate the value of attrition rates
for both sides of the battle. It appears reasonable when the
game proceeds unit by unit and asset by asset, which is called
the platform-centric warfare. However, it has been rapidly
changed, in modern warfare, to the network-centric warfare,
in which all platforms are linked to each other to create a large
and complex warfare environment.
erefore, agent-based modeling (ABM) has been used
widely to build a war-game model recently, because it can
produce more realistic results based on its own decisions
andactionsforallplatformsregardedasagentsinacomplex
system of the battle.
Furthermore, in the previous war-game model, the com-
munication error eect (CEE) was not considered and its
possible eect on each weapon system was not reected
either. However, the CEE is an important factor in network-
centric warfare because all platforms in a battle are connected
to each other to share the target and damage information, as
well as exchanging the order and report among related units
according to the echelon chain.
e agent-based simulation framework we propose
herein consists of three key themes: ABM, CEE, and line of
sight (LOS), as shown in Figure .
In this study, we consider both ABM and CEEs in a
network-centric warfare environment and provide a new
modeling process to measure the combat eectiveness in a
high resolution war-game model considering communica-
tion failure.
As for the quantitative measurement of combat eective-
ness (CE), Hayward [] proposed three factors to quantify
CE as capabilities, environment, and missions. However, it is
still dicult to measure the quantitative combat eectiveness
owing to its intangible and subjective characteristics. It is also
Hindawi
Mathematical Problems in Engineering
Volume 2018, Article ID 2730671, 9 pages
https://doi.org/10.1155/2018/2730671

Mathematical Problems in Engineering
A B M
(Detect & Attack)
A B M
(Damage Report)
A B M
(Move)
CEE
NLOS
Non CEE
LOS
F : Conguration of a typical battleeld in high resolution.
impractical to measure the CE experimentally. Recently, Kim
et al. [] reported a literature review of the work by CE. Lee
and Lee [] and Lee et al. [–] proposed a network-based
metric for measuring CE.
ABM related studies in war-game simulation are Hill et
al.[],Ciletal.[],Seo,etal.[],Connorsetal.[],and
omson et al. []. Regarding the study of communication
factor and modeling in the NCW environment, several
researches have reported the partial impact to CE in a
particular situation such as Sen et al. [], Karedal et al.
[, ], Kang et al. [], Shin et al. [], Cheng et al. [], Li et
al. [], Akhtar et al. [], Shin et al. [], and Lee et al. [].
Our study retains three factors that are dierent from
the papers above because we developed our own model to
generate the communication error, provide an alternative
measure for communication failure, and compare the CE
resultswiththosefromthearmyweaponanalysismodel
(AWAM), which is an ocial analysis model used in the US
andKoreanarmy.
2. Communication Process in NCW
2.1. Overview of Communication Impact to Combat Eective-
ness. It is assumed, without loss of generality, that the overall
measurement of CE is the probability of success in combat
operations. erefore, the primary measure of eectiveness
(MOE)inourmodelwouldbethebluesurvivalratio(BSR),
meaning the ratio of remaining assets (when blue wins) over
the initial assets for the blue force side against the red force.
e study showed how the CEE changes the BSR depending
upon the level of communication success probability.
For the representation of CEE, we used terrain map that
shows the altitude of the terrain in each specied location.
Hence, dierent altitude levels are expressed by each small
cell area depending upon the geographic surface pattern of
the battle ground. When the LOS between two platforms is
visible, no CEE would be applied. Meanwhile, if the LOS
between two platforms is blocked, CEE will occur and the
communication success probability (CSP) would be calcu-
latedbythemodelwedeveloped.Duringtheengagement,all
orders from command and control (C) and the responding
actions from all platforms such as tanks and unmanned
ground vehicles will be delivered via the communication
process. e overall structure of the communication process
for delivering orders and reports during the engagement is
shown in Figure .
We also used AnyLogic . to represent all these processes
and conditions to validate the logic in the NCW war-game
environment.
2.2. Communication Failure Function. To consider the eect
of communication error within a war-game model, we used
the path loss model that is a function describing the commu-
nication in the physical layer between the transmitter (TX)
and receiver (RX) as a method of expressing communication.
ismodelisbasedonthefreepathlossfunctionandis
implemented by the communication channel environment
and the distance between TX and RX. Two types of path loss
function used according to the LOS or non-LOS (NLOS) are
shown in Table .
3. Model Development
3.1. Basic Scenario. To generate the overall measurement of
MOE and to estimate the average value of the BSR, a typical
scenario was established. e input data are shown in Table .
Avirtualareaofsizekm× km was extracted from the
demilitarized zone region and is formulated as a digitized
mapwitheachaltitudeillustratedineachterraincell.e
combat assets were initially deployed to the engagement for
boththeblueandredsides.ePODandPOHforeach
side were assigned as a linear function depending upon the
distance from the ring platform to the target.
3.2. Structure of the Agent-Based Model. e agent-based
model proposed consists of three subprograms: primary,
unit agent, and subagent. e primary program provides
the environment of the battle ground and the generating,
positioning, and setting avenues of approach for the unit
agent. e unit agent is also known as the platform agent
that denes all types of functions that the subagents would
perform based on the prespecied rules dened by the unit
agent. Subagents are function-oriented agents that perform a
mission assigned by the corresponding unit agent.
e overall structure of the agent-based model is shown
in Figure .
3.3. Process of Communication Agent. Both the TX and RX
are always required to perform communication success or
failure. Basically, three steps are required to send a message
to the receiver such as (1) Comm.On, (2) Sending Msg., and
(3) Comm.End.edetailedprocessisshowninFigure.

Mathematical Problems in Engineering 
T : Path loss functions applied to evaluate the communication eect.
Scenario Path loss [dB] Shadow
fading St[dB]
Applicability range,
Ant. Height default value
C
LOS
(i) A=.8B= 41.2,C=20
(ii) PL = 40.0 log10([]) + 11.65 − 16.2 log10(𝑟𝑥)
−16.2log10 𝑅𝑋 + 3.8 log10 𝑐[]/5.0
=
=
30m<d<
𝐵𝑃,
𝐵𝑃 <d<5km,
𝑇𝑋 = 25,𝑅𝑥 = 1.5
NLOS (i) PL = (44.9 − 6.55log10(𝑟𝑥 )) log10([]) + 31.46
+5.83log10 𝑟𝑥 +23log10 𝑐[]/5.0 = 50m<d<5km,
𝑇𝑋 = 25,𝑅𝑥 = 1.5
• Moving through the initial paths
• In order to C2 , tanks move following the order
• Combat’s over, Keep going move
C2
LOAD
SHOT
BDA
COMBAT
Reload
REPORT
_COMBAT
IDLE
WAIT
Shooting
• If the target exists,
reload & Order of
Fire
• If the target not
exists, Move to Stats
“REPORT_COMBAT”
Send Target Info.
∗BDA : Battle Damage Assesment
FOR
_TANK
Detecting
Maneuver
Send Target Info.
Send Target
Info.
• Target’s Platform
Target Info. From UGVs
Tar get In fo. From Tan ks
•Tank’s Message
• Tank’s Moving & Detecting “Moving” Order
∗
It’s mean Sub Agent
in Tank
-
-
F : Communication process delivering orders and reports during engagement.
e communication agent serves as an information
exchange channel for transmitting and receiving all com-
mandments. For example, a command “re target” by the
detecting agent that can exist in a tank or unmanned ground
vehicle (UGV). e overall process of the communication
agent is as follows. Both the TX and RX can create a
communication agent containing information. Further, the
TX sends a message to the RX, known as the “communi-
cation start.” e Rx that received the message transmits
an acknowledgment (ACK) message indicating that the
corresponding message has been received from the TX.
Subsequently, both channels are open to communication and
an information/order such as a specic coordinate or text
message can be sent. e success of this process creates the
communication agent in the TX, and the RX is deleted aer
passing the command.
Additionally, if the transmission fails within  s, transmis-
sion is attempted again. If this process is not successful aer
three times, we assume that the transmission has failed.
3.4. Process of C2 Agent. e C agent delivers all types of
messages to either send orders or receive the information
required via the communication agent. It assigns orders to
thetanksandUGVs.Italsocollectstheenemytarget-related
T : A scenario for engagement of ground battle.
Virtual area  k m x km
 reconnaissance routes between
blue and red forces
Te rr a i n C el l
Cell Size  m × m
the number
of Cell ,
Cell’s
Attr ibute Altitude
Combat Assets
Blue
Tan k  
C 
UGV 
Red Tan k  
C 
POD (probability of detection) &
POH (probability of hit)
POD POH
Blue −.d[m]+ −.d[m]+
Red −.d[m]+ −.d[m]+

Mathematical Problems in Engineering
Detect
Agent
Shoot
Agent
C2
Battle
Field
Main
• Battle eld for combat
• Create and set location UGV,
Tank, C2
• Set moving path to enemy
zone for UGV, Tank, C2 Main
Mossion
Detecting
Combat
Ordering
Tank
UGV
Sub-Agent
•Agent used by unit agents.
•Mission-centric agent
Unit Agent
• Identify UGV, Tank, C2 by
selecting Sub-agent among 6 Sub-
agent based main mission.
Cannon Agent
Command
and Control
(C2)
Basic
Sub-agent
Maneuver
Agent
Communication
BDA
Ta
n
k
UGV
F : Agent structure of the model.
Process of each sending
& receiving
LINK_ON
DISCARD
LISTENR
• Sending msg success
condition
– Sending msg in 2
seconds
• DISCARD condition
– Sending error over
2 seconds
– Re-sending over 3
times
LINK_ON
DISCARD
LISTENR
Sending
Sending ACK
TX Agent RX Agent
Sending ACK
① Comm. on
② Sending Msg
Sending ACK
Comm.
success
Full Process Comm.
• If it success ① ~
③ process
– Comm. Success
• Even 1:1 comm.
Or 1 :n comm.
– Try comm. Each
agent
③ comm. end
Sender Receiver
F : Detailed process of the communication agent.
intelligence. For example, when the UGV nds the enemy
target, its information goes to the C agent with a message
“Find.” Subsequently, the C agent executes the inner process
and assigns an order of either “Move” or “Fire” to the
sender. e role and process of the C agent is shown in
Figure .
3.5. Process of BDA Agent. When a battle occurs, we must
perform a battle damage analysis (BDA) that produces casu-
alties of both red and blue forces. e BDA agent performs an
assessment to calculate the casualties during the battle. When
a platform is hit by an adversary weapon, one of three cases
occurs:M-Kill,A-Kill,orT-Kill,asfollows.

Mathematical Problems in Engineering 
: Move & Fire
: Find
∗
UGV
Radar
FOR
_UGV
C2
Find To
Tar g e t
Radar
Find
Fire
Find
FOR
_TANK
Move To
Tar g e t
Move
Tan k
UGV
①Find
②Move & Fire
③Find C2
#N
F : Role and process of C Agent.
() M-Kill: mobility kill; it cannot move but res the
enemy targets.
() F-Kill: re kill; it cannot re to aim the targets but
moves to other locations.
() T-Kill: total kill; it is the case of total destruction in
both mobility and ring capability.
Hence, in M-Kill, it remains in position and performs ring
whenever required. e maneuvering agent is automatically
disconnected by the M-Kill agent.
Meanwhile, in F-Kill, the shooting agent is disconnected
andcontinuesmovingbasedonthemission.
When T-Kill occurs, it disappears in the battleeld until
theendofthewar-gamereplication.SeeFigure.
3.6. Measure of Eectiveness. To analyze the CE in a simu-
lation model, we used the BSR as a measure of eectiveness
indicating the level of capability to win in a battle. e BSR
andredsurvivalratio(RSR)arecalculatedasfollows.
Initially, the remaining assets (𝑇/𝑇) are calculated at
the end of engagement for both sides.
Next, they are compared with the initial assets (0/0),
and their ratios are counted for both sides.
Hence,theBSRandRSRwerecalculatedby
 = 𝑇
0× 100,
 = 𝑇
0× 100
()
e BSR and RSR represent the ratio of survival assets
compared to its corresponding original assets. In other words,
theyaremerelythebluesurvivalratioandredsurvival
ratio aer the battle has completed. e condition of the
battle termination is supposed to be predened before the
simulation is run.
4. Output Analysis
4.1. Communication Failure and Terrain Maps Are Considered.
e path loss functions in Table  are used to consider the
CEE within an engagement model. According to the distance
between the agents, the CSP is determined as shown in
Figure . e X-axis depicts the distance between agents,
and the Y-axis depicts the communication power arriving at
the RX. e threshold (depending upon the value of K in

Mathematical Problems in Engineering
Cannon(BDA) Agent
Target
BDA System
M-kill(Moving Kill)
(No Moving)
A-kill(Attack Kill)
(No Shooting)
T-kill : Total Kill
(M-kill + A-kill)
F : Role and process of BDA agent.
threshold
Path-loss function
distance
Power
(dB)
mean
F : Path loss function and threshold to decide CSP.
()) represents the minimum communication power that the
RXcanrecognize.Italsosigniesthereceiver’sperformance
capability to obtain the signal power. Hence, a larger K value
renders a lower threshold, thus providing a higher probability
of communication success. Meanwhile, the smaller value of K
produces a lower probability of communication success.
 = −(×
)+()−3≤≤3 ()
To assess the relationship trend depending upon the level
of LOS, terrain maps are also considered in two cases of both
the simplied and commercial cases, as shown in Table .
Case  provides more room for a higher probability of LOS
than Case .
Figure  shows the dierent values of the BSR for both
Case  and Case  and its changing trend over the level of
performance capability of the RX.
Based on this experiment, it is clear that more room for
theLOSandthehighqualityoftheRXprovideabetterMOE
T : Two cases for terrain condition.
Cases Blue Force Red Force
Case-
Simplied Digital Map K: , ., , -. K: 
Case-
Commercial Digital Map K: , ., , -. K: 
BSR(%)
K
40.0
1 0.5 0
50.2
59.2
BSR(Case 1) BSR(Case 2)
71.8
69.4
61.3
78.6
84.0
86.3
−0.5
50.0
60.0
70.0
80.0
90.0
F : BSR comparison depending upon both CSP and terrain
condition.
value(BSR)oftheCE.Inthisparticularscenario,Case
demonstrates a .%–.% higher BSR value than Case .
4.2. Alternative Measure Considered. To comp ensate and
overcome communication failure, a typical measure was
performed. In other words, a communication repeater is
addedwhenevercommunicationfailureoccurs.Inthemodel,
weassumedthatalltheblueunit’splatformsserveasa
communication repeater. ree scenarios are established, as
shown in Table .
In Scenario , the terrain condition is clear and the LOS
willfunctionatalltimes.InScenario,however,theLOS

Mathematical Problems in Engineering 
T : ree scenarios for dierent LOS and Non-LOS(NLOS) conditions.
Scenario Scenario- Scenario- Scenario-
Situation Communication in only LOS
situation
Communication in LOS / NLOS
situation
Communication using
Communication Repeater in
LOS / NLOS situation
Scenario #1
81.6%
83%
84.7%
BSR (%)
85
84.5
84
83.5
83
82.5
82
81.5
81
80.5
80
Scenario #2 Scenario #3
F : BSR comparison depending upon communication
repeater.
would be blocked and would depend on the terrain condition
where both the TX and RX are located. Scenario  is the same
as Scenario  but with a communication repeater.
Figure  shows the dierent values of BSR among the
threescenarios.eMOEvalueofScenarioisthehighest
whilethatofScenarioisthelowestandthatofScenario
issomewhereinbetweenthatofscenariosand.is
experiment shows the quantitative eectiveness of the new
measure of using a communication repeater when the CSP
value is poor.
We can also apply this result to decide whether to
purchase a communication repeater by performing a cost
benet analysis. Based on this assessment approach, more
valuable information will be obtained such as the optimal
number of communication repeaters and the optimal level of
CSP to add a communication repeater.
4.3. Validation of the Model Performed. An issue in the
simulation approach is the validation problem to verify
for tting to the real-world situation. To perform model
validation, we use the AWAM and compare its result to those
from our model called “ABSim.” e AWAM is the most
popular and powerful analytic tool for both the US and
Korean Army.
evalidationprocessisasfollows:
() Establish a scenario
() Build the input data
() Perform the experiment
() Compare the results
Exchange ratio
Second
1.5439
1.3299
0.9707 0.8786
0.7111
0.3313
0.8479
1.1953
2.0000
1.6000
1.2000
0.8000
0.4000
0.0000
036
ABSim AWAM
9
F : Comparison of both models depending upon time delay
owing to communication failure.
To create the same environment for fair comparison for both
models (AWAM and ABSim), input data such as the initial
assets for the blue and red forces are the same, and the output
performance is measured as an exchange ratio. Additionally,
we change the time delay owing to communication failure at
every  seconds and compare the result values of exchange
ratio. e exchange ratio is the number of red forces for one
unit of blue forces. For the blue side, the larger exchange ratio
is better.
Figure  shows the values of exchange ratio depending
upon the time delay owing to the communication failure of
both models. A gap exists between the results of the two
models, but it is fairly consistent over the time delay within
a certain range.
According to many subject matter experts (SMEs), we
found three reasons to create a gap between two models.
() e AWAM uses condential data such as probability
ofdetection,andprobabilityofhit,whicharenot
opened to the public; therefore, ABSim had to use
assumed data referenced by the SME.
() A gap exists in the level of delity on the terrain map
for both models.
() Dierent tactics are used for moving and the tactical
behaviors for both models.
Many SMEs reported that reasons above can compensate
for the gap shown in Figure  between two models.

Mathematical Problems in Engineering
5. Conclusion
We proposed a new simulation process considering commu-
nication failure in a network-centric warfare environment. To
measure the quantitative CE in a network-based battleeld,
we consider both the CEE and LOS depending upon the
altitude of the terrain cell.
e MOE values obtained from the model we developed
indicated that the LOS and CEE were highly correlated to
each other. is implies that a clear LOS scenario obtains a
higherBSR(weusedMOE)valuecomparedtotheNLOS
situation.
We also demonstrated the eectiveness when the com-
munication repeater was applied during communication
failure. is may provide insight into the method to obtain
the optimal policy for adapting the communication repeater,
such as the optimal number or optimal time to add a
communication repeater.
Finally, we compared the simulation results from our
model to the one from AWAM and found that both results
were fairly consistent.
We came to a conclusion that communication failure is
one of the key factors and has to be kept in good condition for
whole engagement process in a network-centric operational
environment.
Data Availability
edatausedtosupportthendingsofthisstudyare
available from the corresponding author upon request.
Conflicts of Interest
e authors declare that they have no conicts of interest.
Acknowledgments
is study was supported by the Future Ground System Anal-
ysis Laboratory (UCID) of the Agency for Defense
Development.
References
[] P. Hayward, “e measurement of combat eectiveness,” Oper-
ations Research,vol.,no.,pp.–,.
[]J.Kim,K.Park,J.Lee,S.Bae,J.Pyun,andC.Kim,“A
survey study of the combat eectiveness analysis models and
future research areas,” Transactions of the Society of CAD/CAM
Engineers,vol.,no.,pp.–,.
[] Y.LeeandT.Lee,“Network-basedmetricformeasuringcombat
eectiveness,” Defence Science Journal,vol.,no.,pp.–,
.
[] J. Lee, C. Kim, K. Park et al., “A study of experimental design
for unmanned ground vehicle eectiveness based on a small
unit combat scenario,” Journal of e Korean Society for Quality
Management,vol.,no.,pp.–,.
[] J. Y. Lee, S. W. Shin, J. S. Kim, S. M. Bae, and C. M. Kim,
“Interrelation analysis of UGV operational capability and com-
bat eectiveness using anylogic simulation,” Journal of Applied
Reliability, e Korean Reliability Society (Korea),vol.,no.,
pp.–,.
[] L. Jaeyeong, S. Sunwoo, and K. Chongman, “A new simulation
framework of operational eectiveness analysis for unmanned
ground vehicle,” Journal of eoretical and Applied Information
Tech n o l o g y ,vol.,no.,pp.–,.
[]R.R.Hill,L.E.Champagne,andJ.C.Price,“Usingagent-
based simulation and game theory to examine the WW-II bay
of biscay u-bo at campaign,” e Journal of Defense Modeling and
Simulation: Applications, Methodology, Technology (JDMS),vol.
,no.,pp.–,.
[] I. Cil and M. Mala, “A multi-agent architecture for modelling
and simulation of small military unit combat in asymmetric
warfare,” Expert Systems with Applications,vol.,no.,pp.
–, .
[]K.-M.Seo,C.Choi,T.G.Kim,andJ.H.Kim,“DEVS-
based combat modeling for engagement-level simulation,”
Simulation-Transactions of the Society for Modeling and Simu-
lation International,vol.,no.,pp.–,.
[] C. D. Connors, J. O. Miller, and B. J. Lunday, “Using agent-based
modeling and a designed experiment to simulate and analyze
a new air-to-air missile,” e Journal of Defense Modeling and
Simulation,vol.,no.,pp.–,.
[] B. ompson and J. Morris-King, “An agent-based modeling
framework for cybersecurity in mobile tactical networks,” e
Journal of Defense Modeling and Simulation,vol.,no.,pp.
–, .
[] I. Sen and D. W. Matolak, “Vehicle-vehicle channel models for
the -GHz band,” IEEE Transactions on Intelligent Transporta-
tion Systems,vol.,no.,pp.–,.
[]J.Karedal,F.Tufvesson,N.Czinketal.,“Ageometry-based
stochastic MIMO model for vehicle-to-vehicle communica-
tions,” IEEE Transactions on Wireless Communications,vol.,
no.,pp.–,.
[] J. Karedal, N. Czink, A. Paier, F. Tufvesson, and A. F. Molisch,
“Path loss modeling for vehicle-to-vehicle communications,”
IEEE Transactions on Vehicular Technology,vol.,no.,pp.
–, .
[] B. Kang, B. Kim, J. Bae et al., “Interoperation of NetSPIN
communication model and wargame model for analysis of
combat power,” Journal of the Korea Society for Simulation,vol.
, no. , pp. –, .
[] K. Shin, H. Nam, and T. Lee, “Communication modeling for a
combat simulation in a network centric warfare environment,”
in Proceedings of the 2013 43rd Winter Simulation Conference -
Simulation: Making Decisions in a Complex World, WSC 2013,
pp.–,USA,December.
[] L. Cheng, D. D. Stancil, and F. Bai, “A roadside scattering
model for the Vehicle-To-Vehicle communication channel,”
IEEE Journal on Selected Areas in Communications,vol.,no.
,pp.–,.
[] Y. Li, F. Baccelli, H. S. Dhillon, and J. G. Andrews, “Statistical
modeling and probabilistic analysis of cellular networks with
determinantal point processes,” IEEE Transactions on Commu-
nications,vol.,no.,pp.–,.
[]N.Akhtar,S.C.Ergen,andO.Ozkasap,“Vehiclemobility
and communication channel models for realistic and ecient
highway vanet simulation,” IEEE Transactions on Vehicular
Tech n o l o g y ,vol.,no.,pp.–,.
[] S. W. Shin, J. Y. Lee, S. M. Bae, and C. M. Kim, “A Study on
Simulation of future ground system eectiveness analysis model

Mathematical Problems in Engineering 
with communication eects,” Journal of Applied Reliability,vol.
, no. , pp. –, .
[] J. Y. Lee, C. M. Kim, and S. W. Shin, “A study of UGV
eectiveness analysis considered with communication path-
loss function,” Proceedings on Korea CAD/CAM Society for
Computational Design and Engineering, pp. -, .