05594538

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Performance Evaluation of WiMAX System in Various Morphological Scenarios

Wafaa Taie, Ahmed S. Ibrahim, Ashraf H. Badawi, and Hani Elgebaly

Intel Corporation, Egypt, and Center of Excellence for Wireless Applications (CEWA), Saudi Arabia.

{wafaax.taie, ahmedx.s.ibrahim, ashraf.h.badawi, hani.elgebaly}@intel.com

Abstract— In the recent years, the WiMAX cellular systemhas been greatly deployed worldwide as it can provide highdata rate to mobile subscribers. However, a few works has beendone to characterize the behavior of the WiMAX network indifferent deployment scenarios. In this paper, we investigate theperformance of the WiMAX system in various morphologicalscenarios, namely, dense urban, urban, and sub-urban. Moreover,we evaluate the behavior of the WiMAX system with differentenvironment parameters such as cell radius, penetration loss, andreceiver antenna gain. For each WiMAX scenario, relevant set of performance criteria such as the spectral efficiency is evaluatedvia system level simulations (SLS). Finally, this paper providesunderstanding and insights on the main design parametersaffecting the performance of WiMAX systems.

I. INTRODUCTION

Recently, there has been a great interest in deploying the

Worldwide Interoperability for Microwave Access (WiMAX)

cellular system in various countries across the globe such

as Japan, Russia, Saudia Arabia, and USA [1]. The mobile

WiMAX system, which is based on the IEEE 802.16e air

interface standard [2], aims to provide high data rate broad-

band services with high Quality-of-Service (QoS) to mobile

subscribers. Furthermore, the WIMAX system is an all-IP

network, and it needs such broadband capability to provide

many services such as VoIP, Mobile TV, and internet-related

services.

The merits of the WiMAX communication system aresignificantly due to utilizing the orthogonal frequency divi-

sion multiplexing (OFDM) and Multiple-input Multiple-output

(MIMO) physical layer technologies [3], [4]. The OFDM

technology mitigates the multi-path fading phenomenon in

wireless channels. Further, the MIMO technology can provide

robust communication via achieving diversity gain, or high

data rate via achieving spatial multiplexing gain. From the

medium access control (MAC) perspective, the WiMAX sys-

tem utilizes the orthogonal frequency division multiple access

(OFDMA) scheme, which optimally allocates the available

time-frequency resources among all the active subscribers.

Finally, the WiMAX system has a scalable bandwidth, which

facilitates the deployment conditions in different countries.While there is a great interest in deploying WiMAX world-

wide due to its merits, there are relatively few published

works that study the performance of the WiMAX network.

For instance the authors of [5] have studies the impact of

antenna configuration and channel coding on the performance

of the WiMAX network. In [6], different scheduling schemes

such as maximum throughput and round robin were evaluated.

In addition to the theoretical evaluation, there has been also

experimental evaluation for deployed WiMAX networks. For

instance in [7], the performance evaluation of a fixed WiMAX

network deployed in the city of Tulsa, OK, USA, at the

4.9GHz public safety band was presented. In these previous

works, there was no consideration for some important factors

that can affect the behavior of the WiMAX system, for instance

different cell radii and how it is related to noise-limited versus

interference-limited scenarios, and what the optimal cell radius

for a particular environment could be. These questions and

more represent our motivation for the work presented in this

paper.

In this paper, we investigate the performance of the WiMAX

system in various morphological scenarios, namely, dense

urban, urban, and sub-urban. Such deployment scenarios varyin some parameters such as cell radius and indoor penetra-

tion loss, which significantly affect the performance of the

WiMAX network. Moreover, we characterize the behavior of

the WiMAX network as the cell radius increases and identify

three distinct regions, namely, high interference, interference-

limited, and noise-limited. In each deployment scenario, the

performance of the WiMAX system is determined via running

SLS. The performance is evaluated via some important perfor-

mance criteria, such as spectral efficiency and user throughput.

In addition to the performance evaluation, this paper provides

understanding and insights on the main parameters affecting

the WiMAX systems.

The rest of the paper is organized as follows. In the nextsection, we give an overview of the SLS environment utilized

in this paper. In Section III, the performance evaluation of

the WiMAX network in various network configurations is

presented. Finally, Section IV concludes the paper.

I I . SYSTEM LEVEL SIMULATIONS (SLS)

In order to produce the results in this paper, we have

utilized our proprietary system level simulator which follows

the IEEE 802.16 Evaluation Methodology document [8] for

the downlink. In this section, we give an overview of the SLS

including the system parameters and performance metrics.

The SLS models 19 hexagonal cells, each cell has a basestation (BS) at its center and 3 non-overlapping sectors.

We consider 10 independent monte carlo trials and in each

one, 10 subscriber stations (SSs) are uniformly deployed in

each sector. The simulation duration in each monte carlo

trail is 300 frames, and each frame duration is 5 msec. The

independent monte carlo trials provide averaging process over

the placement of the users, while the multiple frames provide

time averaging over the channels variation. Table I shows the

network configuration parameters.

978-1-4244-3574-6/10/$25.00 ©2010 IEEE

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Parameter Value

Number of cells 19

Sectors per cell 3

SSs per sector 10

Frames per trial 300

Number of trials 10

Carrier frequency 2.5 GHz

Reuse factor 1x3x1

Cell load 100%

TABLE I

Network configuration parameters.

Parameter Value

Channel model Extended ITU mixed PedB (3km/h)and VehA (30 and 120 km/h)

Path loss 130.19+37.6log10(d) dB(d in km)

Antenna configuration 2× 2

Max BS tx power 40 dBm

BS antenna pattern 70 (-3 dB)with 20 dB front-to-back ratio

BS antenna gain 18 dB

BS antenna spacing 4 wavelength

SS antenna pattern Omni-directional

SS antenna gain 0 dB

SS antenna spacing 0.5 wavelength

Cable loss 1.5 dB

Detection MMSE

Scheduling Proportional fairness

Noise figure 6.5 dB

MCS QPSK (R=1/12, 1/8, 1/4, 1/2, 3/4),16-QAM (R=1/2, 3/4),

64-QAM (R=1/2, 2/3, 3/4, 5/6)

TABLE II

System model parameters.

Each SS (or user) experiences slow fading phenomenon,

such as shadowing and path loss, as well as fast fading channelbehavior. The SLS models the evolution of the desired signal

and interference received by the SS in time, and employs a

PHY abstraction model to predict the link layer performance.

Then, a suitable Modulation and Coding scheme (MCS) is

assigned based on the signal-to-interference-plus-noise-ratio

(SINR) value. Table II depicts the system model parameters.

Each user is allocated one or more slots based on pro-

portional fairness (PF) scheduling criterion. For a system

bandwidth of 10 MHz, the available resources in each down-

link sub-frame are 30 frequency sub-channels and 24 OFDM

symbols. In our SLS and as an example, we set the minimum

allocated resource unit for each user to be 6 frequency sub-

channels and 24 OFDMA symbols, resulting in a total of 5available resources per frame. Table III shows the OFDMA

air interface parameters in details.

The SLS provides a large list of performance criteria includ-

ing the Cumulative Distribution Function (CDF) of the users’

signal-to-noise ratio (SNR) and SINR distributions, users’

average throughput, MCS probability density function, Hybrid

Automatic Repeat request (HARQ) retransmission probability,

aggregate sector throughput, and finally the system spectral

efficiency. The aggregate sector throughput is defined as the

Parameter Value

System bandwidth 10 MHz

FFT size 1024

Subcarrier spacing 10.9375 KHz

Data sub carriers 720

CP length 1/8

OFDMA symbol duration 102.86 u sec

Permutation PUSC

Frame duration 5 ms

Sub-channels/Frame 30OFDMA symbols/Frame 47

OFDMA DL:UL ratio 29:18

TABLE III

OFDMA parameters.

Dense urban Urban Sub-urban

Cell radius (km) 0.86 1.26 3.01

Penetration loss (dB) 18 15 12

Shadowing standard dev. (dB) 8 8 7

Sector throughput (Mbps) 7.09 6.72 4.96

Spectral eff. (bps/Hz/Sector) 1.15 1.09 0.8

Mean user throughput (Kbps) 709 672 496

TABLE IV

Simulation parameters and performance metrics of the three morphological

areas.

number of information bits per second that the sector can

successfully deliver. The spectral efficiency (in bps/Hz) can

be obtained by dividing the aggregate sector throughput by

the effective channel bandwidth as

SE =R

W × tr, (1)

where R is the aggregate sector throughput, W is the total

bandwidth, and tr is the downlink time ratio, which is equal

to 29/47 as in Table III.

III . WIMAX PERFORMANCE EVALUATION

In this section, we evaluate the performance of the WiMAX

network in various deployment scenarios with different cell

radii and penetration losses. Moreover, we characterize the be-

havior of the WiMAX network, as the cell radius changes. Fur-

ther, we illustrate the impact of some of the input parameters

such as penetration loss and SS receive gain on the WiMAX

performance. For each of these scenarios, we calculate the

spectral efficiency and the average user throughput.

A. Morphological Scenarios

In this sub-section, we study the performance of the

WiMAX network in various morphological scenarios, namely,

dense urban, urban, and sub-urban. Each deployment areais distinguished by its cell radius, indoor penetration loss,

shadowing standard deviation, as shown in Table IV. In

Table IV, it is shown that the maximum spectral efficiency and

similarly the sector and user throughputs are achieved in the

dense urban scenario, while the minimum spectral efficiency

is achieved in the sub-urban scenario.

The spectral efficiency values in Table IV can be explained

by illustrating the SINR and SNR distributions of the users

shown in Fig. 1. The SNR distribution depends mainly on the

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−15 −10 −5 0 5 10 15 20 25 30 35 400

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SNR or SINR (dB)

C D F

Suburban, SINR Distribution

Urban, SINR Distribution

Dense urban, SINR Distribution

Suburban, SNR Distribution

Urban, SNR Distribution

Dense urban, SNR Distribution

Suburban

Urban

Dense urban

Solid: SINRDashed: SNRBlack: SuburbanBlue: UrbanRed: Dense urban

Fig. 1. SNR and SINR distributions CDF for the three morphological areas.

0 200 400 600 800 1000 1200 1400 1600 18000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User throughput (Kbps)

C D F

Suburban

Urban

Dense urban

Suburban

Urban

Dense urban

Fig. 2. User throughput CDF for the three morphological areas.

SS received signal power, which varies significantly with the

path loss and consequently with the cell radius. Hence, there

is a horizontal shift between the SNR curves corresponding

to the three morphological areas in accordance with the

corresponding cell radius.

In Fig. 1, it is also shown that the SINR distributions for

the dense urban and urban are close. We note that the SINRdistribution depends not only on the desired signal power, but

also on the interfering signals powers. We notice that there is a

dramatic reduction of the SINR distributions compared to the

SNR distributions in the cases of small to moderate cell radii

(such as dense-urban and urban). Such scenarios represent

interference-limited scenarios, in which the interference is the

dominant factor affecting the link performance as opposed to

the noise. Hence, although the dense urban scenario results

in higher SNR distribution compared to the urban case, we

find that both scenarios have approximately the same SINR

distribution.

On the contrary we find that in the high cell radii (e.g. sub-

urban), the SNR distribution has low values, and there is asmall gap due to the interference in the SINR curve. Hence,

the noise is the dominant factor in the sub-urban case, and

hence it represents a noise-limited scenario. In the following,

we show that there is difference in the behavior between the

interference-limited and noise-limited scenarios.

The SINR distribution affects directly the user throughput,

which is shown in Fig. 2. As shown, the dense urban scenario

has the highest CDF compared to the other two scenarios.

Finally, we note that in Table IV, the average user throughput

Fig. 3. User MCS probability distribution.

Fig. 4. User HARQ probability distribution.

equals the aggregate sector throughput divided by the number

of users per sector (10 SS/sector). In other words, the sector

throughput is divided almost equally across all the randomly-deployed users, which is due to utilizing the proportional

fairness scheduling scheme.

The WiMAX system provides link adaptation, by choosing

the proper MCS mode according to the channel quality, which

is proportional to the SINR value. In particular, the WiMAX

system with 2 × 2 antenna configuration provides 19 MCS

modes, which are divided into two sub-groups. The first sub-

group consists of 11 MCS modes, which are listed in Table II

and are achieved using 2 × 2 space-time block code (STBC)

scheme. The second sub-group consists of the last 8 MCS

modes, which correspond to spatial multiplexing (SM) mode

with utilizing 2 spatial streams. Fig. 3 depicts the probability

distribution of the utilized MCS in each deployment scenario.As shown for all the deployment scenarios, the SM models

happens with low probability compared to the STBC modes.

This is because the SM modes correspond to high SINR, which

happens to small percentage of users.

Finally, we show the probability of HARQ retransmission

in Fig. 4. As shown, about 95% of the transmissions do

not require an HARQ retransmission. Moreover, the HARQ

retransmission distribution is almost independent of the mor-

phology.

B. Spectral Efficiency Behavior Characterization

As noted in Table IV, the spectral efficiency varies from

high value for the dense urban scenario to lower value expe-rienced in the sub-urban case. In this section, we characterize

the behavior of the spectral efficiency as the cell radius varies.

Fig. 5 shows the spectral efficiency values for different cell

radii.

As shown for the indoor case, the spectral efficiency

behavior can be divided into three regions, namely, high

interference, interference-limited, and noise-limited regions.

In the high interference region (less than 100m), there is a

low spectral efficiency due to the high interference impact. In

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0 500 1000 1500 2000 2500 30000.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

1.25

Cell radius (m)

S p e c t r a l e f f i c i e n c y ( b p s / H z / s e c t o r )

Indoor

Outdoor

Noise limited region

Highinterference

impact

Interference limited region

Fig. 5. Spectral efficiency for the indoor and outdoor scenarios.

the interference-limited region (200m- 1000m), the spectral

efficiency curve varies slightly and achieves the highest pos-

sible spectral efficiency value. We find that within this region

there is a balance between the desired signal power and the

interference power. Finally in the noise-limited region (greater

than 1000m), the spectral efficiency decreases as the cell

radius increases due to decreasing the desired signal power.

Moreover, it is shown that the optimum cell radii for the indoor

case, at which the spectral efficiency is maximized, are equal

to 250m and 750m.

The same spectral efficiency behavior, which includes the

three different regions, will always happen irrespective of

the simulation input parameters. For instance, Fig. 5 also

depicts the spectral efficiency performance of the outdoor

case with zero penetration loss. As shown, changing the

simulation parameters results in a scaled and shifted curve

of that of the indoor curve, however, the spectral efficiency

curve still experiences the three behavioral regions. As shown,

the optimum cell radius for the outdoor case is 750m. From

a network design perspective, it is highly desirable that theindoor and outdoor scenarios have the same optimum cell

radius of 750m, as there will be one cell size which is

optimized for both indoor and outdoor users. However, there is

no guarantee that this will be always the case if the deployment

environment is changed.

In order to understand the reason of having such behavior

in the spectral efficiency, we show the average SINR for the

outdoor case in Fig. 6. For each cell radius, we plot the average

of the SINR values of all the users in the network. As shown,

the spectral efficiency curve behave in a similar fashion to

that of the average SINR. Hence, the variation of the SINR is

the main responsible of having such behavior in the spectral

efficiency curve. The interference-limited region is of specialinterest as there is non-monotonic behavior in that region, and

it can be explained by focusing on the SINR expression as

follows.

We assume that the WiMAX network has a total of K cells.

In addition, we assume that a particular SS is deployed in

the main cell (cell index number 1). The SS experiences a

series of gain (e.g. Tx/Rx antenna gains) and loss (e.g. path

loss, shadow, and penetration loss) factors. Let G1 denote the

effective path gain (all gains - all losses) from the desired cell

500 1000 1500 2000 2500 30007

7.5

8

8.5

9

A v e r a g e S I N R ( d

B )

500 1000 1500 2000 2500 30001

1.05

1.1

1.15

1.2

1.25

Cell radius (m) S p e c t r a l e f f i c i e n c y ( b p s / H z / s e c t o r )

Fig. 6. Spectral efficiency and SINR for the outdoor scenario.

(number 1) to the SS.

Of special interest among the gains/losses is the path loss,

as it depends on the distance between the BS and SS (denoted

as d1). Simply, the effective gain can be modeled as G1(d1) =const−L(d1) , where const is a constant value that includes

all the fixed (i.e. distance-independent) gains and losses.

In addition to the desired signal, the SS receives co-channel

interference (CCI) from the neighboring cells (a total of K −1)that are using the same time-frequency channels. Similar to the

desired signal, the received signal from the k-th interfering cell

experiences effective path gain (or loss), which is denoted as

Gk. Therefore, the received SINR at the SS can be calculated

as

γ =P G1(d1)

K

k=2P Gk(dk) + N o

, (2)

where P is the BS transmission power, which is the same for

all the BSs, dk is the distance between the k-th BS and the

SS, and N o is the noise variance.

In (2), it is shown that the SINR depends on the distances

from the SS to all the neighboring cells. In general, there is no

monotonic (increasing or decreasing) behavior of the SINR in(2), as it depends on the interaction among the transmission

power and distances from the main and all the interfering cells.

Whenever all of these factors play non-negligible roles (as in

the interference-limited region), we get such ups and downs

in the average SINR curve.

We expect that there will be always such behavior consisting

of three regions in the SINR and hence the spectral efficiency.

We found that in the indoor and outdoor cases by changing

the penetration loss to 12dB and 0dB, respectively. Definitely,

the dips (how many, where, their values) will depend on the

specific input parameters and simulation scenarios. But, in

general we expect three distinct regions: 1) High interference

(small radius), which should be avoided, 2) Interference-

limited (medium radius), which is the dense-urban case, and

3) noise-limited (large radius), which is the sub-urban case.

Finally, Fig. 5 sheds the light on the tradeoff between

achieving larger coverage (i.e. higher cell radius) and reducing

the spectral efficiency (and hence lower the data rate).

C. Impact of Power Gains

In the currently available WiMAX components, subscriber

can utilize Customer Premises Equipment (CPE), which can

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CPE Rx gain 0 dB 6 dB

Dense urban 1.15 1.15

Urban 1.09 1.09

Sub-urban 0.8 0.94

TABLE V

Impact of CPE receive gain on the spectral efficiency (in bps/Hz/Sector) of

the three deployment scenarios.

−10 0 10 20 30 40 500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SNR or SINR (dB)

C D F

SINR Distribution (0 dB Rx gain)


SNR Distribution (0 dB Rx gain)


6 dB Rx gain

0 dB Rx gain

Solid: SINRDashed: SNRBlue: 0 dB Rx gainRed: 6 dB Rx gain

Fig. 7. SNR and SINR CDF distributions for the dense urban morphologywith different receive gains.

have receive (Rx) gain of 6 dB. In this sub-section, we

investigate the potentials gains of having 6dB Rx gain at the SS

in the three deployment scenarios considered in Section III-A.

Table V shows the spectral efficiency of indoor case with

receive gains of both 0dB and 6dB. As shown, the receive gain

has no impact on the urban and dense urban scenarios, while

it increases the spectral efficiency of the sub-urban scenario.

These findings can be explained by investigating the SINR

CDF distributions of the SSs in each deployment scenario.

Fig. 7 depicts the SNR and SINR CDF distributions of the

SSs with CPE receive gain of both 0dB and 6dB for the dense

urban morphology. As shown, the SINR CDF distribution

does not change by increasing the CPE receive gain to 6dB.We note that any power gain (e.g. CPE receive gain) affects

both the desired signal and the interference signals by the

same weight. Therefore, in interference-limited scenarios the

SINR distribution does not change because of additional power

gains. We note in Fig. 7 that the SNR, on the other hand,

improves significantly with increasing the CPE receive gain.

In the sub-urban scenario, the CPE receive gain significantly

increases the SINR as shown in Fig. 8. Unlike the interference-

limited case, in noise-limited scenario (e.g. sub-urban) any

power gain can increase the SINR and consequently increase

the spectral efficiency. As shown in Table V, CPE gain of 6dB achieves 20% increase in the spectral efficiency for the

sub-urban case.We have shown above that the extra 6dB receive gain can

increase the spectral efficiency in the sub-urban morphology.

Alternatively, it can extend the cell radius without reducing

the spectral efficiency below that at 3.01 km. Considering a

cell radius of 4 km and CPE receive gain of 6dB, the resulting

spectral efficiency is 0.85 bps/Hz/Sector. Such value is even

higher than the default sub-urban case shown in Table IV with

spectral efficiency of 0.8 bps/Hz/Sector. Hence in the sub-

urban case, the additional CPE receive gain of 6dB can extend

−15 −10 −5 0 5 10 15 20 25 300

0.1

0.2

0.3

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0.6

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0.8

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SNR or SINR (dB)

C D F





Solid: SINRDashed: SNRBlue: 0 dB Rx gainRed: 6 dB Rx gain

0 dB Rx gain

6 dB Rx gain

Fig. 8. SNR and SINR CDF distributions for the sub-urban morphologywith different receive gains.

the cell radius by 1km, which is equivalent to 33% increase

in the coverage area, while guaranteeing the same spectral

efficiency.

IV. CONCLUSION

In this paper, we have evaluated the performance of the

WiMAX communication system in three morphological sce-narios, which are dense urban, urban, and sub-urban. We

have shown that the highest spectral efficiency is achieved in

the dense-urban case (1.15 bps/Hz/Sector), while the lowest

spectral efficiency is achieved in the sub-urban case (0.8bps/Hz/Sector). Moreover, we have characterized the behavior

of the spectral efficiency criterion with the cell radius into three

distinct regions, which are 1) High interference (small radius),

which should be avoided, 2) Interference-limited (medium

radius), which is the dense-urban case, and 3) noise-limited

(large radius), which is the sub-urban case. We have identified

the optimum cell radius to be equal to 750m for both the

indoor and outdoor scenarios. Finally, it was shown that

additional receive gain of 6dB in the sub-urban case (noise-

limited) achieves 20% increase in the spectral efficiency or

33% increase in the cell radius.

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[4] The WiMAX Forum, “Mobile WiMAX - part I: A technical overviewand performance evaluation,” Feb. 2006.

[5] S. Tiraspolsky, A. Rubtsov, A. Maltsev, and A. Davydov, “MobileWiMAX - deployment scenarios performance analysis,” Proc. 3rd In-ternational Symposium on Wireless Communication Systems (ISWCS’06),pp. 353 – 357, Sep. 2006.

[6] A. Maltsev, A. Pudeyev, and A. Maltsev Jr., “Multiuser frequency domainscheduling for WiMAX OFDMA,” Proc. 16th IST Mobile and WirelessCommunications Summit , pp. 1 – 4, Jul. 2007.

[7] N. LaSorte, W. J. Barnes, B. Zigreng, and H. Refai, “Performanceevaluation of a deployed WiMAX system operating in the 4.9GHz publicsafety band,” Proc. IEEE 6th Consumer Communications and NetworkingConference (CCNC’09), pp. 1 – 5, Jan. 2009.

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