simulation of 3g networks in realistic propagation environments
TRANSCRIPT
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A. Zreikat, K. Al-Begain: Simulation of 3G Networks in Realistic Propagation Environments
Simulation of 3G Networks in Realistic Propagation Environments
Aymen I. Zreikat and Khalid Al-Begain,Mobile Computing and Networking Research Group,
Department of Computing, University of Bradford, BD7 1DP, Bradford, UK
A.I.Zreikat, K.begain @bradford.ac.uk
Abstract
The coverage of a mobile system depends significantly on the geographical nature of the covered area. The signal
propagation can be dramatically different in downtown area with many high buildings than in a building free area. This
is particularly critical in third generation (3G) mobile systems based on Code-Division Multiple Access (CDMA) air
interfaces where the power management is a core part of the call admission control of the system. Therefore, performancestudies of such systems based on free space assumptions may lead to optimistic results. In this paper, the performance
of a 3G UMTS mobile network covering an urban area and surrounding suburban areas is considered. For modelling the
propagation, the COST-231 extended Hata model has been used which represents more realistic propagation models for
urban-suburban environments. Based on this model, closed expressions have been derived for the capacity bounds in the
existence of interference due to non-ideal orthogonality of codes in the used CDMA system and background noise. These
expressions are used to develop a network level sophisticated call admission control (CAC) algorithm to achieve nearly
equal blocking probability and balanced utilization over the whole network area. Detailed simulation is used to study the
performance of the network under different traffic and interference conditions. The results show that the proposed CAC
algorithm performs very well in achieving equal blocking probability by releasing the load on the heavily loaded central
area and, thus, achieving better balanced load on the network under different interference conditions. Additionally, some
design and environment parameters are studied like the height of the base station and the average height of the mobile.
Keywords: 3G Mobile Networks, Propagation Models, Capacity Bounds, Performance Evaluation.
1 Introduction
Coverage and capacity optimization have always been hot
research topics in the Third Generation (3G) mobile net-
works. The dynamic nature of the capacity stems from
the characteristics of the physical air interface which
uses the Code Division Multiple Access (CDMA) con-
cept [1][2]. Therefore, the traditional static call admis-
sion control (CAC) alogirthms that were suitable for 2G
mobile networks (For example [3]) are not applicable to
3G networks like the Universal Mobile Telecommunica-tion System (UMTS) [2].
In CDMA systems like UMTS, the scarce resource is the
transmission power. Given the Frequency Division Du-
plex mode (FDD) of the UMTS, the power budget of
the uplink and downlink are independent of each other.
The power of the uplink is limited by the transmission
power of the user equipment (UE) while the power bud-
get of the down link depends only on the capabilities of
the Node B (Base station). Furthermore, the Wide band
CDMA (W-CDMA) used in UMTS uses a set of spread-
ing sequences or codes with optimal correlation charac-
teristics to separate user connections. The interference
of the different signal depends on this correlation and in-
creases with the increase of the number of multiplexed
data streams. Since most services require a given signal-
to-noise rate, the number of admitable data streams will
be fewer than number of available codes [11][4]. As a
result, many CAC algorithms were proposed in the liter-
ature that are based on either admitted power level ([10],
[9], [12]) or on the value of the Signal-to-Interference Ra-
tio (SIR) ([18],[19]). In [5], the authors proposed a new
CAC algorithm based on capacity bounds of the UMTSsystem due to both interference and limited transmission
power of the UE. This algorithm was extended in [7] to
a multicell CAC using the soft handover feature of the
UMTS network. In the later, the new connection request
will be transferred to one of the accessible neighboring
Node B-s if there is no available capacity in the near-
est Node B or if the admission of this new connection
will disturb any of the existing connections unless this
connection can be accommodated in another cell. The
CAC algorithm aims to uses the soft handover feature of
the UMTS systems to provide multiple goals: (i) provide
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A. Zreikat, K. Al-Begain: Simulation of 3G Networks in Realistic Propagation Environments
efficient utilization of the available capacity, (ii) protect
the QoS of existing connections, and (iii) prevent the loss
of coverage resulting from the so-called Cell Breathing
[11].
The main problem with the performance evaluation pre-
sented in [7] is that it assumes free space propagation
within the investigated area. This assumption is usu-ally not realistic and leads to optimistic prediction of the
system performance. In reality, the propagation of the
transmitted signal depends largely on the geographical
and building intensity of the area ([15],[20]). In this pa-
per, the same CAC algorithm of [7] is implemented in
a UMTS network of 7 Node B over an area that repre-
sent and medium size town with a highly built city center
and surrounding less built suburban area. For this sake,
the COST 231 Urban-Suburban propagation models [6] is
used which is an extended version of the Hata model [16].
In a previous work[8], the authors have derived capacity
bounds for different propagation environments includingdense urban, urban, suburban, rural in addition to the free
space environment.
The paper is organized as follows. Section 2 introduces
the investigated environment by defining the used propa-
gation model and summarizes the capacity bounds as the
maximum number of users and maximum distance cov-
ered by the Node B in both urban and suburban environ-
ments. In Section 3, the CAC is introduced. Section 4,
then, defines the simulation settings and the numerical re-
sults of the investigation before some concluding remarks
are given at the end of the paper.
2 The Investigated System
2.1 Basic assumptions
The investigation of this paper is based on a cluster of
UMTS mobile network comprising 7 Node B stations
(7 cells as shown in Figure 1 ). In [7] the Call Ad-
mission control algorithm is presented in a network of 7
cells where the ideal free propagation model is assumed.Whereas, in this paper, different propagation environ-
ments (urban, suburban) have been assumed using the
extended Hata model,[8]. The middle cell is the urban
(hot spot) area and the 6 surrounding cells are the sub-
urban ones. It is assumed that every UE will be softly
connected to the three nearest Node B-s, but the actual
data transmission will take place through one at a time.
The term Softly means that the UE and Node B are
exchanging signaling information but no resources are
allocated to the connection initiated by the UE unless a
proper CAC procedure has taken place. Although the
work is going on the multi service case, this paper will
concentrate on the introduction of the CAC algorithm for
single service case. For this service class, we introduce
the Service Factor, as where SF is
the spreading factor and SNR is the minimum Signal-to-
Noise ratio required for this service. It is assumed that
each Node B can ideally serve connections at service
factor .
d3
d1
d2
Suburban
Suburban
SuburbanSuburban
Suburban
Suburban
Urban
Figure 1: A seven cell structure in a macro cellular sys-
tems
The actual capacity and the coverage of each cell within
the network depends strongly on the interference levels in
the cell. The interference stems from some basic noise,
, and the interference from the non-ideal orthogonality
of the codes in the used CDMA system. Let denote this
non-orthogonality factor.
In this investigation, we do not consider mobility as it
makes the introduction of the algorithm much more com-
plex. This matter will be the subject of later work.
2.2 Capacity bounds
In this section, the capacity bounds for urban-suburbanenvironments are introduced,[8].
2.2.1 Extended COST-231 Hata model
The original Hata model was published in 1980 by Masa-
haru Hata [16]. Hata took the information in the field
strength curves produced by Yoshihisa Okumura [17] and
formed a set of equations for the path loss. The gen-
eral Hata model has two limitations. It has a limited path
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A. Zreikat, K. Al-Begain: Simulation of 3G Networks in Realistic Propagation Environments
length and a limited frequency range. Therefore, a num-
ber of modified models have been produced to extend the
path length and frequency range in order to cope with the
requirements of the new technology.
The Hata empirical model uses a propagation equation
split up into two terms. A term that has a logarithmic de-
pendence on distance, , and a term that is independent
of distance. The Hata model also includes adjustments tothe basic equation to account for urban, suburban, dense
urban, rural propagation losses.
The general propagation loss in dB is given by [6]:
(1)
Where,
is a propagation loss in environment of type , in dB.
is the frequency of the transmission in MHz.
is the height of base station or transmitter in meters
(30-200m).
is the height of the mobile or receiver in meters (1-
10m).
is the distance between the receiver and the transmitter
in kilometers (1-20km).
mobile antenna correction factor.
is the correction factor which has different value for
each environment.
As can be readily seen, the path loss in the free space
model depends only on the frequency and the distance.
Whereas the other propagation models further parameters
are introduced such as : the height of the mobile ( ),
height of the base station (
).
Note that, the above general formula (1) has been given
for urban environments. However, the formulae for
other environments can be obtained from this formula by
adopting the suitable correction factors.
2.2.2 Extended COST-231 Hata model for urban en-
vironment
From (1), the urban model is defined as :
(2)
Where:
(3)
and
2.2.3 Extended COST-231 Hata model for suburban
environments
From (1), the suburban model is given by :
(4)
Which means that the Hata propagation model for the
suburban can be written as:
(5)
Where:
(6)
and
2.3 Capacity bounds for urban environ-
ment
In all the proposed models we have a relationship of the
form [15]:
(7)
where
is the received power,
is the transmission power,
is a function of
, the height of the mobile and ,
the height of the base station, and the frequency, f.
is the environment type, 0 for urban and 1 for suburban
is the distance between the mobile and the base station,
is the frequency in MHz,
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Clearly from (7), the distance, can be defined as:
(8)
The complete derivation of the extended Hata model foreach environment is in the Appendix at the end of this ar-
ticle.
The propagation loss for the extended Hata model in an
urban environment in the PCs (Personal Communication
services) range is given by:
(9)
The values of
for each propagation envi-ronment are given in (10, 11.
(10)
(11)
According to the above equation: (8), (10), (11)
-The formula for the maximum distance between the UE
and Node B can be defined as:
(12)
-The uplink capacity of the UMTS cell can be defined as:
(13)
3 CAC Algorithm
The CAC algorithm is implemented in a network of 7
cells. Let
denote the numbers of existing ac-
tive connections where
denotes the number of active
users in cell ,
. In the case of a new UE
appearing in the network, the UE must perform a regis-
tration phase before being able to request resources for
actual transmission of information which is controlled by
CAC algorithm.
The CAC algorithm aims to:-
1. Provide a required QoS for admitted connec-
tions by not allowing more connections than
the network can serve efficiently.
2. Protect existing connections from being dis-
turbed because of the admission of a new con-
nection.
3. Distribute the load over the network in an
efficient way by transferring the new calls
or even some existing connections from the
heavily loaded cell to those with lighter load.
4. Avoid coverage loss due to the so-called cell
breathing; i.e., when the coverage of more
than one neighboring cell shrinks below a cer-
tain limit.
Registration phase:
(a)- The UE measures the signal power of all ac-
cessible Node B-s.
(b)- The UE selects the 3 best signals and softly
registers at these Node B-s. Let
, and
denote the distances to these Node B-s in ascend-
ing order. The information (cell-number, received
power level) for these 3 stations are stored in
UE (as we do not consider movement, otherwise
the power levels should be measured and the list
should be updated dynamically). The UE will at-
tempt to connect to the one of these three Node Bin the same order and the connection will only be
rejected if it is rejected by all three Node B-s. The
CAC algorithm works as follows:-
The Call Admission Control algorithm:-
Assume that Node B is the closest with distance
to the new UE asking for connection (In other
words, the UE falls into the coverage area of cell
). Therefore, the UE will try first to get admission
in this Node B as follows:
1. For cell , IF
then
goto the Step labeled REJ, otherwise con-
tinue with next step.
2. Calculate
as shown in equation
12 above.
3. IF
then gotoREJ, other-
wise continue with next step.
4. Calculate
for all other cells
.
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A. Zreikat, K. Al-Begain: Simulation of 3G Networks in Realistic Propagation Environments
5. For all neighboring cells to cell , (
) check :
IF
, where is the distance between Node
and Node
then gotoREJ otherwise con-
tinue with next step.
6. For cell , check for all existing active con-
nections
, IF for any connection
the distance
then goto
TRANSFER, otherwise gotoACCEPT.
7. TRANSFER: is a procedure to transfer an
active connection to another cell to which the
UE is softly connected. Therefore, this pro-
cedure is a complete replica of this CAC al-
gorithm except for the TRANSFER in order
to avoid an infinite loops. If the TRANSFER
of Connection is successful then goto AC-
CEPT, otherwise gotoREJ.
8. REJ The connection cannot be admitted tocell , therefore
(a)- , that is try with next cell (Node B),
(b)- IF
then Goto to Step 1 otherwise
gotoREJECTconnection.
9. ACCEPTconnection of the UE in cell .
GotoEND.
10. REJECTconnection of the UE.
11. END
4 The Simulation
4.1 Traffic model
As mentioned earlier, the studied system comprises 7 cell
where the central cell is heavily built area (Urban prop-
agation model used) and the surrounding cell are mod-
eled with Suburban propagation model. The arrival pro-
cess over the whole network is assumed to follow a Pois-
son process. The traffic is assumed to be uniformly dis-
tributed over the coverage area of each Node B. Two dif-
ferent traffic patterns are considered in this paper: ho-mogeneous and hotspot. In the homogeneous case, the
load is equal for all cells. In the hotspot scenario, we as-
sign a double load of the calls to cell number one which
is in the center of the network while the other six cells
have the same load. The latter is suitable for modelling
a metropolitan area where the central Node B serves the
city center area. The location of the user in the cell is
chosen randomly and then the absolute coordinates of the
user is determined by the Node B. The call duration is
assumed to be exponentially distributed with mean
and the user leaves the system as soon as the call ends.
This assumption is not realistic for packet switched net-
works but if we consider the system operation at burst
level where we assume that a group of packets will be
buffered for transmission in the UE before initiating the
call admission procedure , then this assumption can be
acceptable.
Parameter Symbol Values
Number of codes N 64
Radius of the cell r 578.03m
Spreading factor SF 64 chips/symbol
Service factor S 32
Signal to noise ratio SNR 2 db
Max. transmission rate
125 mW
Basic noise
-80 dBm
Interference factor 0.30, 0.50, 0.70
Wave length 0.15m
Height of the mobile
2m,5m
Height of the base station 50m,100m
Table 1: Simulation Parameters
4.2 Numerical results
4.2.1 The effect of the interference factor, ( )
The system has been studied via a detailed simulation us-
ing the parameters as shown in Table 1. The investigation
will concentrate on the most important performance mea-
sures, the blocking probability and the utilization versus
the call rate. All the result obtained in this paper were set
to 95% confidence intervals.
The performance of the system in such environment can
be influenced by many factors including the interference
levels, the physical characteristics of the network, like the
height of the Node Bs and the height of buildings in the
area. In this paper, we present only results that show the
capability of the proposed CAC algorithm on achieving
equal blocking probabilities over the whole area of the
network by balancing the load even in different interfer-ence conditions and different traffic loading (the hotspot
scenario). In this investigation, we set the Base station
height (average mobile height) in the urban and suburban
areas to be 100m and 50m, 5m and 2m, respectively.
Figures 2 - 7 show results grouped into three sets accord-
ing to different values of interference factor =0.3, 0.5,
0.7, respectively. Each set consists of two figures for the
blocking probability and cell utilization. The utilization
in the this case is calculated as the proportion of usage of
the available bandwidth (codes).
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Biographies
Aymen I. Zreikat obtained his BSc in
Computer Science from Yarmouk University, Jordan in
1990 and MSc in Computational Engineering from Uni-
versity of Erlangen, Germany in 2000. In Jan, 2001, he
joined the Mobile Computing and Communications Re-
search Group in the Department of Computing of Brad-
ford University, UK as research/Ph.D. student where he
is in his final year. His area of research is in the Per-
formance Evaluation and Resource Management of 3G
Mobile networks in which he has a set of journal and con-
ference publications in this field.
Khalid Al-Begain received his High
Diploma (1986), the Specialization Diploma of Com-
munication Engineering (1988) and his Ph.D. degree in
Communication Engineering (1989) from the Technical
University of Budapest in Hungary. From 1990, he held
the position of a Assistant Professor at the Deptartment
of Computer Science of the Mutah University/Jordan. In
1996,he became an Associate Professor at the same uni-
versity. In 1997 he moved to the Department of Com-
puter Science at the University of Erlangen-Nuremberg
in Germany as Alexander von Humboldt research fel-
low. Furthermore, he spent one year as Guest Professor
at the Chair of Telecommunications, Dresden University
of Technology, Germany. From 2000 to 2003, he has
been Senior Lecturer and Director of Postgraduate Re-
search in the Department of Computing of the University
of Bradford, UK. He is currently Professor in the School
of Computing in the University of Glamorgan, Cardiff,
Wales and the head of Mobile Computing and Network-ing research group. He co-authored the book Practi-
cal Performance Modelling published by Kluwer Aca-
demic Publishers and more than 60 journal and confer-
ences papers. He is senior member of the IEEE and many
other scientefic organisations. He also serves as Guest
Editor for a special issue of this journal on Analytical
and Stochastic Modelling Techniques and as Conference
Chair for the ASMT03 to be held in Nottingham,UK in
June 2003. His research interests are performance mod-
elling and analysis of computer and communication sys-
tems, analytical modelling and design of wireless mobile
networks.
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