Int. J. Communications, Network and System Sciences, 2010, 3, 863-869
doi:10.4236/ijcns.2010.311117 Published Online November 2010 (
Copyright © 2010 SciRes. IJCNS
On the Performance of Mobile WiMAX System:
Measurement and Propagation Studies*
Furaih Alshaalan1, Saleh Alshebeili2, Abdulkareem Adinoyi3
1Department of Electrical Engineering , King Saud University (KSU), Riyadh, Saudi Arab ia
2Prince Sultan Advanced Technologies Research Institute (PSATRI)/STC Chair, KSU, Riyadh, Saudi Arabia
3Swedtel Arabia, Riyadh, Saudi Arabia
E-mail: falshalan@yahoo .com,, aadinoyi@
Received August 6, 2010; revised September 13, 2010; accepted October 24, 2010
In this paper, we present drive test results for mobile WiMAX system for desert and cosmopolitan terrains
where there are few studies reported in the literature. The extensive measurement is performed in the
framework of the physical performance of the WiMAX technology which is often considered as a 4G system.
Path loss model is fitted for the collected data. The work is unique in the sense that most empirical channel
models are produced in regions where the environments (weather, buildings, vegetation, among others) are
quite different from the desert terrains that are considered in this study. We also show that shadowing is truly
lognormal in dB and the standard deviation values are calculated for the desert terrain from the measurement
data. The measurements are collected using WiMAX BS station, with greenpacket dongle, and NEMO ver-
satile outdoor drive test equipment to evaluate and characterize the performance of the system. The received
signal strength indicators measured, are analyzed to complement network design and network optimization
for regions where the popular models may not be accurate.
Keywords: OFDMA, WiMAX, RSSI, Path Loss Models
1. Introduction
Worldwide Interoperability for Microwave-Access
(WiMAX) has emerged as wireless access technology
that is capable of providing fixed and mobile broadband
connectivity. More accurately, WiMAX is a certification
mark for IEEE 802.16-based broadband wireless solu-
tions that have passed a set of conformity and interop-
erability tests defined by WiMAX forum [1]. Their uni-
fied approach to testing manufacturers’ equipment has
ensured that various vendors’ products interwork to-
Fixed WiMAX is targeted for fixed and nomadic
broadband services while mobile WiMAX are designed
to provide high mobility services. While the performance
and propagation models of the fixed WiMAX system
have been widely studied [2-5], the mobile WiMAX is
yet to witness widespread attention. To the best of our
knowledge, there are no reports in the literature for mo-
bile WiMAX performance presented for the desert ter-
rains that are the subject of this paper. A closely related
study is reported in [6] although for GSM network,
where it is argued that the Okumura-Hata propagation
model is not fully suitable for the desert, dry Oman ter-
rains since there is no much rain in Oman. On the other
hand, most propagation models are developed in regions
with significant amount of rainfall and flourishing vege-
tation. The fact of this statement is much applicable to
most cities in the Kingdom of Saudi Arabia (KSA). The
authors in [6] suggest that further improvement of Oku-
mura-Hata model in the open area is necessary. However,
their study is limited to GSM900 operating in the 900
MHz frequency spectrum.
Operators do drive-tests on a continuous basis, col-
lecting signal levels, network quality and performance
which are then used to refine empirical propagation
models for system-planning and/or existing network op-
timization [7-9]. Reference [3] provides measurement
results for some Italian University campuses in the 3.5
GHz frequency band where they attempted to extract and
refine previous path-loss models for their experimental
*This work was supported by a grant (No. 09-ELE302) from The Unit
of Science and Technology, King Saud University.
data. Note that the Walfish and Bertoni model is an ex-
tension of the COST-231 (and COST-231 Hata model is
an extension of the Hata-Okumura model). The accuracy
of this model varies depending on the environments.
If the desert terrain is examined carefully, it will be
discovered that it is significantly different from the
vegetation rich and densely populated region of Tokyo
city where Okumura’s path-loss model was developed. A
good engineering design suggests that these models
should be modified for other cities (or at least perform
parameter tuning) to capture the possible differences in
the propagation behaviors. The importance of a properly
chosen path loss model for evaluating the performance of
a system is stressed in [10]. We observed that the nature
of buildings and structures is remarkably different as
well in the sense that almost all buildings in the KSA are
of brick type in contrast to plank or wood of most ad-
vanced countries. Thus our study will be useful as it
represents an extension of mobile WiMAX study to de-
sert terrains.
In this study we have performed a comprehensive
measurement study of the desert terrain. Propagation
models are fitted for the measured data. Furthermore, in
contrast to [6], in our study, we investigate the WiMAX
technology (that is competing with 3GPP’s long-term
evolution (LTE) as beyond 3G technology [11]) in the
2.5 GHz band. Our measurement efforts can be adopted
for network planning and optimization. These efforts
have been motivated by the work in [12], among other
references. The authors (in reference [12]) have stressed
the importance of careful evaluation of technology to aid
network design and optimization. However, their study
was limited to fixed WiMAX system in contrast to our
mobile WiMAX. Among the benefits of our effort for
accurate propagation model for desert environment is to
assist network operators not to over- or under-engineer
their networks, which is not a cost-effective way of
managing link budget. For the design of power-efficient
networks it is essential that every fraction of decibel (en-
ergy) is utilized effectively through adoption of the most
suitable model in link budget. This power consciousness
is a valuable effort towards reducing power consumption
which has both positive environmental and cost impacts.
The remaining discussion in this paper is organized as
follows. The network, system set-up and measurement
tools are described in Section 2. This is followed by test
result presentation and analyses in Section 3. Finally,
conclusion is drawn in Section 4.
2. System Set-Up and Measurement Tool
We used a NEMO test tool-equipped laptop, housed in a
vehicle containing a global positioning system (GPS) to
synchronize its location with incoming data provided by
the WiMAX terminal (Figure 1). The terminal is a
greenpacket (WiMAX) receiver. We have used the ver-
satile NEMO outdoor air interface measurement tool that
has been widely accepted in the industrial. The outdoor
tool, GPS receiver, and WiMAX scanner are connected
to the laptop through a USB serial converter. The GPS in
conjunction with the NEMO outdoor system is used to
measure the distance of the WiMAX receiver from the
base station.
The system uses an air interface based on orthogonal
frequency division multiplexing (OFDM), which is very
robust against multi-path propagation and frequency se-
lective fading. The WiMAX system uses the Frequency
Division Duplexing (FDD) mode of transmission. Col-
lection of measurement data was performed using Wi-
MAX BS and communications towers with antennas at a
height of 17 meters. The roof of the car (i.e., the omni
directional antenna) is two meters in height. The meas-
urement data are collected by driving through the city
around the WiMAX BS as shown in the representative
signal map shown in Figure 2. Each point represents
Figure 1. The set-up of the drive test.
Figure 2. A sample signal map for the collected data for one
site. The color legend are Green: RSSI > 70 dBm, Yellow:
–80 RSSI < –70 dBm, White: –89 RSSI < –80 dBm, Red:
RSSI < –90 dBm.
Copyright © 2010 SciRes. IJCNS
sample collected at the position shown. Table 1 shows
the summarized specifications for the WiMAX system.
3. Drive Test Results and Analyses
The measurement campaign earmarked a number of
WiMAX sites. The sites are selected to provide a good
mix of various structures that characterize the region’s
settlements that are the subject of this study. For instance,
Riyadh is a fast growing and developing metropolitan
and in the suburb, there are pockets of new settlements
that are connected by open and mostly desert areas. Thus,
this terrain provides an unique environment for new
measurement study and more importantly, as far as we
know, there has been no study of this kind reported in the
open literature.
A sample signal map is shown in Figure 2 where sig-
nal level distribution is depicted. As expected, excellent
signal level is seen at areas close to the BS. It is also ob-
served that some areas though close to BS experience
shadowing leading to low signal level. This can be at-
tributed to the building in the neighborhood of the BS.
The cumulative density function (CDF) of the RSSI is
represented in Figure 3 for three sites. The RSSI values
are obtained over a large range of distance. We observed
in Figure 3 that the two sites (A and B) that are located
in the same geographical area and thus having the same
terrain tend to behave similarly in statistical sense than
the other site (Site C). This difference in behavior can be
explained using the climatic or slight different in weather
conditions. The city where site C is located is largely a
coastal region compare to that of sites A and B. Although
it hardly rains in both cities, City C is mostly humid.
From this figure, it can be observed that there will be less
accurate link budget calculation if the same path loss
model is adopted for the two different cities without ap-
propriately adapting the model to the environments. For
instance, it will be found that for RSSI of about –70 dBm,
the city C link budget will be about 10% of the time in-
accurate (if the same model is used without adapting it),
compared to that of other sites A and B.
Table 1. WiMAX parameters and system settings.
Parameter Value
Air interface (Multiple access) OFDM(OFDMA)
BS transmit power (dBm) 23
Carrier frequency (GHz) 2.5
Total bandwidth (MHz) 5
No of subcarrier 512
Duplexing FDD
Table 2 shows some recorded snapshots of the net-
work statistics (averaged over a number of samples) for
one site1. The significance of this table is to demonstrate
the impact of interference dynamics when analyzing the
performance of the network. It is observed that the rates
are quite good for distance with 700 m from the BS. One
thing that should be noted from these results is that the
received signal strength indicator (RSSI) alone is not
enough to represent the network performance as previ-
ously observed in [7]. It is observed that a rate of 7.270
Mbps is obtained for RSSI equal to –76.5 dBm whereas
for a seemingly better RSSI (of –72 dBm), a lower rate
of 6.080 Mbps is recorded. The explanation is that RSSI
does not directly take the interference situation into ac-
count. Therefore, RSSI may be very good but the inter-
ference could also be very high to produce a low effec-
tive signal to-noise plus interference ratio (SINR). It is
known that SINR ultimately determines the achievable
rate. However, the RSSI is an important parameter for
deriving the path loss model [7]. We now embark on the
analysis and model fitting for the collected data.
Figure 3. The CDF of the RSSI for the three sites.
Table 2. A site RSSI and measured throughput.
Throughput (Mpbs) RSSI (dBm) Distance (m)
7.21 –67.5 710
7.27 –76.5 678
6.08 –72.0 675
6.76 –64.0 674
6.09 –69.5 624
3.19 –77.5 503
6.98 –63.5 436
6.13 –69.0 432
7.21 –65.5 413
1Note that the results presented in Table 2 are extracted to help illus-
trate the observed network behavior.
Copyright © 2010 SciRes. IJCNS
3.1. Scattered Plots, Curve Fitting and Results
Figure 4 through Figure 6 show the scattered plots of
the measured path loss for a number of cell sites, Site 1,
Site 2 and Site 3. The free space path loss (FSPL) and the
Walfisch-Ikegami propagation model are also shown in
the figures. We denote Walfisch-Ikegami model as WIM.
The curve fitting (to the measured data) was done in the
least square sense (in the log-log scale) using the
MATLAB software. We observed that WIM could be
used as an approximate model in the absence of our
newly derived model.
The variation of the path-loss introduced by the
shadowing effects is seen in these scatter plots. The path
loss is obtained as, PL = Pt + Gt + Gr RSSI Losses,
Figure 4. The scatter plots of path loss and curve fitting for
site 1.
Figure 5. The scatter plots of path loss and curve fitting for
site 2.
where Gt, Gr, and Losses are respectively, the base sta-
tion gain (10 dBi), the receive terminal gain (0 dB), and
connection losses (3 dB). Performing curve fitting to
derive path loss model will be less accurate if the signal
variation is not smoothen out. To towards that end, we
conduct measurements by taking a number of readings at
a particular location and obtained the average. We refer
to this approach as filtering (i.e., filtering the variations).
This implies that we take the average path loss PL =
E[PL], where E[·] denotes averaging operators. Curve
fitting is performed on the obtained (measured) data and
the values for the path loss exponent (n) derived from the
fitting are tabulated in Table 3. Both the filtered and
unfiltered model parameters are shown.
Figure 7 shows the model fitting for unfiltered data
from three sites using log-log scale. It is seen that a sig-
nificant variation exists among the three different meas-
ured data.
Thus, it is difficult to accept any of the models as a
good representative of the environment. Figure 8 how-
ever shows that by filtering the data, a significant simi-
larity is observed among the data. The fittings for the
three sites, except for a negligible difference, are more or
Figure 6. The scatter plots of path loss and curve fitting for
site 3.
Table 3. The empirical path loss exponent n.
Path loss Intercept
Site 1 3.1702 2.8259 117.9715 115.0860
Site 2 2.3108 2.5117 115.3560 115.2515
Site 3 1.9737 2.6993 116.0743 115.3619
Average2.4849 2.6790 116.4673 115.2331
Copyright © 2010 SciRes. IJCNS
Figure 7. The unfiltered data scatter plot and curve fitting
for the path loss.
Figure 8. The filtered data scatter plot and curve fitting for
path loss.
less the same. Note that in both Figure 7 and Figure 8
the scatter points are shown for only one site in order not
to congest the figures. The other findings are presented
in Table 3.
It is observed from Table 3 that the intercepts from
both the filtered and unfiltered data are very close to
each other. However, the intercepts from the filtered data
are almost the same. As for the path loss exponent, the
filtered data produces (as expected) a representative
value. Thus, the average intercept and the average path
loss exponent (of the filtered data) are chosen to model
the propagation characteristics of the terrain. Hence, the
following expression is obtained:
(d)log 26.79115.23 PL(d)10
 (1)
and it represents a good model for the terrains. The ac-
curacy of this model is investigated in the next section
through goodness fit.
3.2. Goodness Fit
To investigate the goodness fit for the model to the
measured data and to also compare the new model with
the common/close models, we embark on the following
statistical tests. We consider two criteria for examining
the goodness fit; the root mean square error (RMSE) and
the coefficient of determination (R2). The following ex-
pression is used to calculate the RMSE
iii yy
RMSE (2)
where i denotes the estimate of data i and M is the
data length. The statistical measure R2 on the other hand
is given as
 M
1R (3)
where i
y is the mean of the measured data. The results
in Tables 4-5 show the RMSE for the filtered and unfil-
tered data, respectively. Given the lower RMSE recorded,
it is concluded that the new model shows a better fit to
the filtered data compared to other models.
For the unfiltered data the models show poor match,
although the new model still shows a better fit.
The calculated values for R2 are shown in Table 6 for
the new model. Simply put, the coefficient of determina-
tion (R2) represents the percentage of the data that is
closest to the fitted curve. For instance, R2 = 0.9031 (for
the new model for Site 2), implies that about 90% of the
total variation in the measured data can be explained by
Table 4. The average root mean square error for filtered
New Model WIM F ree Space
Site 1 2.8587 5.6399 13.9703
Site 2 2.7004 5.3174 13.7131
Site 3 2.7952 4.8595 13.2062
Table 5. The average root mean square error for unfiltered
New Model WIM Fr ee Space
Site 1 9.3470 10.6977 16.8344
Site 2 8.8637 10.9561 17.7060
Site 3 9.5029 11.2039 17.8007
Copyright © 2010 SciRes. IJCNS
Table 6. The coefficient of determination for the new model.
Filtere d Data Unfil tere d Data
Site 1 0.8773 0.3709
Site 2 0.9031 0.2452
Site 3 0.9046 0.4960
the linear relationship between the estimated and meas-
ured data (as described by the regression equation). The
remaining about 10% of the total variation in the meas-
ured data is unexplained. Thus, the coefficient of deter-
mination is a measure of how well the fitted curve repre-
sents the data. Considering the results in Table 5 the
fitted model represents excellent fit to the measured data
given that over 87% of the measured data are explained
using the new model. Note that the coefficient of deter-
mination is not shown for WIM and FSPL since they are
not derived from the data (and not obtained through least
square fit).
We make a final remark on the path loss exponent that
is obtained for the desert terrain. From the collected data
and fitting, we found that n is approximately 2.7. This
value is worse than the free space value (n = 2) but better
than 3 and 3.5 that is usually assumed for urban and
shadowed urban, respectively [13]. This is a significant
finding or observation. The plain nature and clear sky of
the desert environment could be the reason why the path
loss exponent is less than that of typical urban or shad-
owed urban terrains.
3.3. The Standard Deviation of Shadowing
Here we confirm through field measurements that shad-
owing (the relatively long-term fading) is truly log-nor-
mal. The histogram shown in Figure 9 exhibits the
bell-shaped of a random variable is that normally distrib-
uted. The parameter (i.e., standard deviation) of this fad-
ing phenomenon is evaluated for the terrain under study.
The following expression, modified from [13] is em-
ployed for evaluating this parameter.
))(CDF 2(erfcinv 2
where PL is the average path loss,
and CDF(
are arbitrary path loss and the density function of
From the CDF of the path-loss values (the type shown
in Figure 11) the standard deviation can be calculated
through appropriate mapping. The data for this part of
the study are captured at different locations designated as
d = {450 m, 600 m, 900 m, 1200 m}. Figure 10 shows
the distribution of the measured signal RSSI while Fig-
ure 11 is a representative CDF of the path loss for cal-
culating the values of the spread (or variance or standard
deviation) of the data. The calculated standard deviation
values are tabulated in Table 7.
It is observed that the standard deviation is between 8
and 9. The lowest and highest recorded values are re-
spectively, 8.36 and 8.81 (See Table 7). Therefore, for
network design, the knowledge of this fading parameter
would aid system designers in their link budget calcula-
tion. It provides the necessary guidance for determining
the fading margin allowances that will ensure minimal
network outage conditions.
Figure 9. The histogram of the received signal strength in-
dicator at a fixed location, demonstrating the log-normal
nature of the shadowing phenomenon.
Figure 10. The received signal strength indicator at some
fixed locations. The signal level can be mapped to histo-
gram such as the one shown in Figure 9.
Copyright © 2010 SciRes. IJCNS
Copyright © 2010 SciRes. IJCNS
5. References
Table 7. Standard deviations of signal variation due shad-
Standard Deviation
Distance d = 450 m d = 600 m d = 900 m
Site 1 8.493 8.4536 8.8114
Site 2 8.5944
(d = 65 0 m) 8.7441 8.3613
(d = 12 00 m)
[1] WiMAX.
[2] P. Gronsund, T. Johnsen, P. Engelstad and T. Skeie, “The
Physical Performance and Path Loss in a Fixed WiMAX
Deployment,” Proceedings of the 2007 International
Conference on Wireless Communications and Mobile
Computing, August 2007, pp. 439-444.
[3] D. De Luca, F. Fiano, F. Mazzenga, C. Monti, S. Ridolfi
and F. Vallone, “Outdoor Path Loss Models for IEEE
802.16 in Suburban and Campus-Like Environments,”
IEEE International Communications Conference, June
2007, pp. 4902-4906.
[4] K. Dongmyoung, C. Hua, N. Minsoo and C. Sunghyun
“Performance Measurement over Mobile WiMAX/IEEE
802.16e Network,” International Symposium on a World
of Wireless, Mobile and Multimedia Networks, June 2008,
pp. 1-8.
[5] B. Belloul, A. Aragon-Zavala and S. R. Saunders,
“Measurements and Comparison of WiMAX Radio Cov-
erage at 2.5 Ghz and 3.5 Ghz,” European Conference on
Antennas and Propagation, March 2009, pp. 3287-3291.
[6] E. Nazar, M. A. Salam, A. Al-Lawati, O. Al-Qasmi, M.
Al-Gheithi and Z. Nadir, “Modification of an Open Area
Okumura-Hata Propagation Model Suitable for Oman,”
IEEE TENCON, November 2005, pp. 1-4.
[7] J. De Bruyne, W. Joseph, L. Verloock and L. Martens,
“Measurements and Evaluation of the Network Perform-
ance of a Fixed Wimax System in a Suburban Environ-
ment,” IEEE International Symposium on Wireless
Communication Systems, October 2008, pp. 98-102.
[8] N. LaSorte, W. Barnes, B. Zigreng and H. Refai, “Per-
formance Evaluation of a Deployed WiMAX System
Operating in the 4.9 GHz Public Safety Band,” IEEE
Consumer Communications and Networking Conference,
January 2009, pp. 1-5.
Figure 11. The CDF of the path loss at some fixed locations.
This CDF is used in conjunction with (4) to calculate the
standard deviation of the shadowing phenomenon.
4. Conclusion [9] V
. Teterin, S. Hurley and S. M. Allen, “A Staged Opti-
mization Framework for Cost Optimized Wimax Net-
work Design,” The International Conference on Wireless
and Mobile Communications, 2008, pp. 185-190.
This paper presents drive test results for mobile WiMAX
network in deserts, cosmopolitan, and fast growing ter-
rains that have not witnessed such measurement efforts.
Extensive performance data are collected and analyzed.
The propagation behavior is also examined. The path-loss
exponent for the desert terrain is obtained which is worse
than that of free space but better than that of urban
shadowed environment. This is a significant new obser-
vation for a terrain that has not witnessed much of this type
of study. Thus, our efforts represent a significant contribu-
tion to the body of literature in measurement and propaga-
tion model in the context of mobile WiMAX systems.
[10] WINNER.
[11] 3GPP, “3rd Generation Partnership Project; Technical
Specification Group Radio Access Network; Require-
ments for Further Advancements for Evolved Universal
Terrestrial Radio Access (E-UTRA) (LTE-Advanced)
(Release 8),” available online, March 2009.
[12] Z. Nemeth and C. Szabo, “Measurements to Assist Ac-
cess Network Design with Fixed WiMAX in Urban En-
vironment,” ACM International Conference Proceeding,
[13] T. S. Rappaport, “Wireless Communications: Principles
& Practice,” Prentice Hall, New Jersey, 2002.