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Impact of the Base Station Antenna Beamwidth on

Capacity in WCDMA Cellular Networks

Jarno Niemelä and Jukka LempiäinenTampere University of Technology, Institute of Communication Engineering

P.O.BOX 553, FIN-33101 TAMPERE, FINLAND [email protected] and [email protected]

Abstract –- In this paper the impact of the base station antenna

horizontal beamwidth on radio network capacity in WCDMA

cellular networks in the downlink direction is studied. Influence

of coverage overlapping and radio propagation environment

together with antenna beamwidth are also studied. A radio

network planning tool is used to simulate WCDMA macro

cellular network performance in urban and suburban area in

Tampere, Finland. By utilizing digital morphology and

topography information in the simulations reliable and practicalresults were expected. The simulation results show that the effect

of the base station antenna horizontal beamwidth on capacity is

almost unremarkable in three-sectorised sites between 65° and

90° antennas. Meanwhile the capacity enhancement between 33°

and 65°/90° beamwidths in six-sectorised sites is significant.

Altogether, the results of this study yield for exact planning

guidelines of the base station antenna configuration for different

capacity needs.

I. INTRODUCTION

The demand of different mobile services in the 3rd generation mobile communication systems, especially the

varying bit rates, will rise causing a need of more efficientsystems to be designed and thus also new challenges in thefield of radio network planning. EuropeanTelecommunications Standards Institute (ETSI) selectedWideband Code Division Multiple Access (WCDMA) as a

multiple access technique for the radio interface in the 3rd

generation mobile telephone communication systems. Thisnew radio access technology changes the radio network

planning process and planning principles. Multiple accessschemes as TDMA and FDMA used in the 2

ndgeneration

mobile communications systems make it possible to divide

different network planning phases more clearly into individual parts because different frequencies are used at different timemoments. In WCDMA systems the same frequency is usedsimultaneously in neighbour cells and interference should betaken into account already in the coverage planning phase because the sensitivity of the base stations depends on the

number of users and their bit rates (= cumulative interference).Furthermore, coverage and capacity planning cannot beseparated into different phases because the coverage of a cell

is changing according to the amount of users (and their bitrates) in a cell. This well-known phenomenon is called ‘cell breathing’ and it shows that coverage and capacity depends on

each other. Hence, the downlink performance of coverage andcapacity depends heavily on the interference from theneighbour cells. This other-cell interference (also called inter-cell interference) in the downlink direction is moreover related

to the base station antenna configurations as beamwidth.

The simplest way to improve the capacity of a network is toadd more carriers. However, once all available carriers have been used, other methods have to be utilized. In reference [1]

the following methods have been proposed for capacityenhancements in WCDMA networks: transmit diversity, beamforming, additional scrambling codes, increased

sectorisation and micro cells. From these methodssectorisation is highly linked to the selection of the basestation antenna beamwidth because it plays an important and

crucial role in sectorisation. By a careful selection of antenna beamwidth in different sectorisation cases interference leakingto neighbour cells can be controlled at a certain level. Thereduction of the base station transmit power can be performeddue to higher gain of the sector antennas, and thus additional power is left over for capacity increase. In contrast, more

interference is radiated in the direction of the main beam withhigh gain sector antennas and also the coverage area maydiminish due to narrower antenna beamwidths forcing the base

station to rise its transmit power.

Downlink capacity equation for sectorised WCDMAcellular network has been derived as a function of antenna beamwidth in [2]. In consequence of this research an optimum

base station antenna beamwidth for WCDMA cellular network has been found in relatively ideal conditions. The effect of antenna beamwidth and sectorisation on capacity and coverage

are explored in [3] and [4]. Based on these simulationsnarrower antenna beamwidth brings more capacity into

WCDMA network and additionally an optimum antenna beamwidth exists for each site configuration. Narrower antenna beamwidth has also brought capacity enhancementsand interference reductions in CDMA wireless local loop

systems in [5].

In this paper the effect of base station antenna beamwidth

on capacity in the WCDMA cellular networks is analysed. Theresults are based on the simulations done in real propagationenvironment. Various simulations with typical UMTSantennas of different scenarios have been done. The



information of digital topography has been used in the

simulations and also the effect of morphology information for different clutter types has been simulated. The main targets of these simulations are

- to evaluate the impact of base station antenna horizontal beamwidth as a function of base station transmit power,

- to analyse the impact of distance between base station sites

(coverage overlapping),- to evaluate the practical capacity per site basis when the

impact of environment is taken into account and- to analyse the impact of base station antenna configuration

(beamwidth and number of sectors) on the on capacity and

also on coverage.

II. THEORY OF THE DOWNLINK CAPACITY

In this section downlink capacity related functions as well

as the effect of antenna beamwidth on downlink load factor are presented. Carrier-to-interference ratio (C/IC) in WCDMAnetworks is considered and finally discussion about

propagation model and slope as well as the effects of theenvironmental factors on propagation model are brought up.

The downlink load factor can be defined by the followingequation [6]

( )( )[ ]∑

=

+−⋅⋅=

N

j

j j j

jb

j DL i RW

N E

1

01 α υ η (1)

where υ j is the channel activity factor for jth user, E b /N 0 is the

required energy per bit divided by noise spectral density, W is

the system chip rate, R j is the bit rate of jth user, α j is the

orthogonality factor and i j is the other-to-own-cell

interference. This parameter is different for each user becauseit depends on the location of user. Required transmit power P BS for a base station can be defined mathematically as afunction of downlink load factor [7]

( )

DL

N

j j

jb

jrf

BS

RW

N E L N

P η

υ

−

⋅⋅

=

∑=

1

1

0

(2)

where N rf is the noise spectral density and L is the average path loss between base station transmitter and mobile receiver.

When the downlink load factor saturates, the system

approaches its pole capacity and the required transmission power approaches infinity. Equation (1) shows also that

orthogonality and interference are directly proportional to thedownlink load factor. Moreover, the total received interference(noise + other users) is a function of load called interference

margin (IM)

)1(log10 10 DL IM η −−= . (3)

Because WCDMA capacity is interference limited as

Equations (1) and (3) show it is crucial to control the basestation transmit power accurately. This power is controlled in

the downlink direction based on carrier-to-interference ratio

(C/IC) and it can be defined by Equation (4),

∑∑=

−

−

=

== N

n

n

N

n n

D

R

D

R

I

C

11

1

1

γ

γ

γ

γ

(4)

where R is the distance between the serving base station andmobile and Dn is the distance between the nth interfering basestation and the mobile. The exponent of the attenuation of the

radio wave is denoted by γ. In the 2nd

generation systems, e.g.GSM, a certain (C/IC)–ratio has to be achieved for a proper quality of speech and it is mainly determined by the frequency

reuse factor utilized in a network. In WCDMA systems allcells are using the same frequencies and thus the interfering base station can locate within the same distance as the serving base station.

In Figure 1 an arbitrary situation is depicted where the

mobile is camped on the cell of antenna direction 90° of the

base station at centre. Because of wide antenna beamwidthinterference is leaking from adjacent cells thus rising the noise

floor at the serving cell and forcing the serving base station torise its transmit power.

Figure 1: Other-cell interference with 90° antenna beamwidth.

The situation is improved by narrowing the base stationantenna beamwidths in Figure 2. Thus, by utilizing narrower

antenna beamwidth other-cell interference is decreased due toless radiation power leakage to other cells and due to the factthat the base station antenna main beam is directed more

precisely towards the mobiles. However, when narrowingantenna beamwidth too much it is possible that coveragethresholds can not be anymore exceeded.



Figure 2: Other-cell interference with 65° antenna beamwidth.

Interference is also decreased because of environment.Radio wave attenuates in free space proportional to the squareof distance r , i.e. 20 dB/dec in dB scale. Above the water theattenuation does not differ much from free space attenuation but in other terrain types (e.g. forest, open areas, buildings)

radio wave propagation slope changes typically between25 – 40 dB/dec. Compared to homogeneous terrain, i.e. theterrain is flat and the same terrain type covers the area,different propagation slopes cause strong variations of the

coverage areas. In a certain direction a radio wave can propagate for a long distance simultaneously causinginterference to surrounding cells and moreover reducing the

capacity of a network.

III. SIMULATION PARAMETERS

The utilized static radio network planning tool uses Monte-

Carlo simulations. In the network simulation process mobiles(also called users and terminals) are spread randomlyaccording to created traffic raster over the area under

investigation. Required transmit powers in the network arethen computed iteratively for each mobile and base station.Morphology and topology information of the simulation area

were used in part of the simulations defined by a digital mapof 5m x 5m resolution. The digital map included basic terraintypes and also buildings of different heights. While

investigating the effect of more accurate propagation model

the building vectors were used to give more realistic results.The network consisted of a regular hexagonal grid of 10

base stations covering total area of 35 km2. The locations of

the base stations were kept fixed (except in the coverageoverlapping simulations where the simulation area wasdiminished but the network layout remained the same). The

base station antenna orientations were 60°, 180° and 300° for three-sectorised sites and 0°, 60° and 120°, 180°, 240° and300° for six-sectorised sites in all simulations. The antenna

installation height at every base station site was constantly25 m.

Okumura-Hata propagation model was used as for a large

city environment with a path loss exponent of 3.5 and mobilestation heights were set to 1.5 m. Area correction factors (i.e.altered propagation slope for each terrain type) were not

utilized in the first part of the simulation because oneobjective of this paper was to simulate the effect of non-homogeneous terrain on capacity. Coverage threshold in eachsimulation case was demanded to be -90 dBm at least with

95% probability. The user profile consisted of a homogeneousdistribution of speech (8 kbit/s) users with activity factors of 60% in both directions (DL and UL). Soft/softer handover gain was maximally 2.9 dB in the downlink direction. Power control standard deviation was set to 1dB to illustrate the

effect of an unideal power control.

Three and six-sectorised base station sites were simulated.

In three-sectorised sites the selected horizontal half power antenna beamwidths were 65° and 90° and in six-sectorisedsites 33°, 65° and 90°, respectively. Table 1 gathers the

essential simulation parameters used in simulations.

TABLE 1: SIMULATION PARAMETERS.

Base StationMaximum transmit power 43dBm

Pilot power 33dBm

Common channel power 33dBm

Maximum transmit power per connection 40dBm

Noise figure 5 dB

Mobile Station

Maximum transmit power 21dBm

Dynamic range 70dB

Active set 6

Power step size 0.5dB

Required Ec/I0 -21dB

Noise figure 9 dB

3G service – Speech

Downlink

- E b/N0 8dB

- Activity factor 0.6

Uplink

- E b/N0 6dB

- Activity factor 0.6

Other

Slow fading standard deviation 8dB

Uplink noise rise 6dB

Orthogonality 0.6

Handover margin 3dB

Chip rate 3.84Mchips

Power control std deviation 1dB

The capacity results are plotted as a function of required

base station transmit power that can be calculated from

( )

G

L N N E P

terminal req DL,b

BS

⋅⋅=

0 (5)

where (E b /N 0 ) DL,req is the required energy per bit divided bynoise spectral density ratio, L is the path loss between a basestation and a mobile and G is the processing gain. The mostinteresting variable in this equation is N terminal which is the

noise received at the mobile. N terminal is a function of orthogonality, other-to-own-cell interference and noise figure



of the mobile receiver and it describes the total interference

received by a mobile. Thus, comparing the needed transmit powers between different site configurations the effect of different antenna beamwidths can be clearly outlined.

IV. SIMULATION RESULTS

In the first part of the simulations different base stationantenna horizontal beamwidths with different sectorisationcases were simulated. Networks of three-sectorised sites were

already highly loaded with relatively low user densities whenin the networks of six-sectorised sites the load remained lowand thus the average required transmit power for the basestations remained also low. The capacities of the differentsimulation cases are presented as a function of transmit power in Figure 3.

The capacity enhancement due to narrower antennas is

significant between 33° and 65°/90° beamwidths in six-sectorised sites. The difference between 65° and 90° antennasin three-sectorised sites is small even if the load and transmit

powers are higher. Moreover, sectorisation clearly enhancesthe capacity of a network. Transmit power in three-sectorisedcases starts to run out a bit after 120 users per sector, while in

six-sectorised sites there is sufficiently power left. The curvesin Figure 3 show that capacity differences are getting higher when the load of the network growths. In order to present the

effect of higher load all the six-sectorised sites were stronglyloaded in the second part of the simulation. The results of loading are depicted in Figure 4.

Figure 3: Capacity (users per site) as a function of downlink transmit

power in three and six-sectorised sites.

The achieved capacity enhancement of 33° antennacompared to 65° and 90° antennas is obvious already with low base station transmit powers in Figure 4. The capacity

difference is even higher when the needed base stationtransmit power (or load) is higher: base station transmit power of 42 dBm in Figure 4 corresponds to approximately 70%

load. Capacity increase of six-sectorised network of 33°antennas compared to network of 65° antennas is up to 20%and even higher compared to network of antennas of 90°

beamwidth. The capacity difference between six-sectorised

and three-sectorised networks is 70 – 80% when comparing

base station transmission levels 40 – 42 dBm referring highload situation in the network (Figures 3 and 4). Theenhancement of capacity is achieved due to smaller

interference level in a case of narrower base station antenna beamwidth.

Figure 4: Capacity as a function of downlink transmit power per site in

case of six-sectorised sites of 33°, 65° and 90° antennas.

The impact of distance between the base stations i.e.coverage overlapping and the impact of area correction factors

i.e. radio propagation environment on capacity was simulatednext. The impact of coverage overlapping was simulated bydiminishing the distance between the base station sites. While

2.0 km base station separation was used as a reference case,the distances of 1.5 km and 1.0 km were chosen for thesesimulations. All cases were simulated with six-sectorised sites

and with 33° antenna beamwidth. In all cases averagedownlink transmit power was 41.5 dBm. Table 2 shows that

2.0 km distance between the base stations was best possiblefrom capacity and interference point of view in downlink. Onthe other hand, 1.5 km configuration creates less load in thenetwork.

TABLE 2: THE IMPACT OF COVERAGE OVERLAPPING (DISTANCE) ON RADIO

NETWORK CAPACITY, LOAD AND OTHER -TO-OWN CELL INTERFERECE WITH

EQUAL DOWNLINK TRANSMIT POWER .

Distance [km] 1.0 1.5 2.0

Capacity per site [users] 295 310 313

Load [%] 62.5 61 63

Other-to-own cell interference 0.55 0.50 0.49

Next, the area correction factors of average weighting of

-8.4 dB were added to the propagation model and newsimulations were done. The average base station transmit

powers were also adjusted to the same level as in referencecase. The load of the network was set to 60 – 70% and thedistance between the base station sites was 2.0 km.

Table 3 shows that the capacity of the reference case (noarea corrections) is slightly better than in the first part of thesimulations because the power control in this case wasassumed not to have deviation. Without and with area

correction factors the network was able to serve average 56.2users per sector with 82.20% service probability and average



55.1 users per sector with 86.90% service probability,

respectively. The capacity of the network reduces when moreaccurate propagation model is used and when non-homogeneous radio propagation environment is taken into

account but the degradation is not enormous as seen from theresults. The growth of service probability is achieved due tothe improved coverage thus also rising interference level andcausing degradation of capacity.

TABLE 3: THE EFFECT OF AREA CORRECTION FACTORS (ACF).

Without ACF With ACFService Probability [%] 82.20 86.90

Capacity per sector [users] 56.2 55.1

Capacity per site [users] 337.3 330.6

Downlink TRCH power [dBm] 41.45 41.49

Finally coverage areas were studied based on thesimulations that were done for capacity evaluations. In three-sectorised sites required transmit power of the base stations inorder to achieve 95% coverage at -90 dBm threshold level was

higher than in six-sectorised sites. The required transmit

power also decreased when a narrow antenna beamwidth wasutilized. The impact of different antenna beamwidths on

coverage areas was not as huge as in [3] where no digitaltopography or morphology information was utilized.

Figure 5 depicts two different coverage schemes. In theleft-handed picture coverage areas of one six-sectorised site of 33° antenna beamwidth is presented and in the right-handed picture a three-sectorised site of 90° beamwidth antennas.

Figure 5: Coverage schemas of two different sectorisation cases.

In this case the difference between the required base stationtransmit powers was the highest. For six-sectorised sites therequired coverage threshold was achieved with 4 dB lower power than for three-sectorised sites. Figure 5 shows also that

more interference is directed to other cells in six-sectorisedconfiguration but in contrast coverage near the base stations is better (the darkest colour) and thus capacity is increased due

to sectorisation and narrowbeam antennas.

V. DISCUSSION AND CONCLUSIONS

In this paper the impact of base station antenna horizontal beamwidth has been evaluated by using a sophisticated radio

network planning tool for simulations which are based on areal radio wave propagation environment defined by a highresolution digital map. Simulation results show that the effect

of antenna beamwidth is less significant with low base station

transmit powers (low load) in the WCDMA radio network.Furthermore, with higher transmit power (higher load) the

effect of narrower antenna beamwidth is more distinct even if capacity increase due to ‘optimal’ antenna beamwidth is notevident as other studies have been pointed out before.

Moreover, coverage area overlapping and environment (areacorrection factors) increase interference and thus reducecapacity. This reduction of 2 to 5% was expected to be higher

as it was shown in the results. Finally, it was presented thenumber of users per site as a function of the required transmit power and number of sectors. The increase of sectors in

WCDMA network brings clearly more capacity into network as simulation results show.

ACKNOWLEDGEMENTS

Authors would like to thank European CommunicationsEngineering (ECE) Ltd for helpful comments concerningsimulation parameters and simulation environment, Nokia

Networks for providing NetAct Planner tool for simulationsand FM Kartta for providing a digital map.

REFERENCES

[1] Jaana Laiho, Achim Wacker, Tomáš Novosad, “ Radio network Planning

and Optimisation for UMTS ”, John Wiley & Song Ltd, 2002.

[2] B. Christer V. Johansson, Sara Stefansson, “Optimizing Antenna Parameters for Sectorized W-CDMA Networks”, Proceedings of IEEE

Vehicular Technology Conference, VTC2000, pp. 1524-1531.

[3] Janna Laiho-Steffens, Achim Wacker, Kari Sipilä and Kari Heiska, “The

Impact of the Base Station Sectorisation on WCDMA Radio Network

Performance”, Proceedings of IEEE Vehicular Technology Conference,

VTC1999, pp. 2611-2615.

[4] Jaana Laiho-Steffens, Achim Wacker, Pauli Aikio, “The Impact of the

Radio Network Planning and Site Configuration on the WCDMA Network

Capacity and Quality of Service”, Proceedings of IEEE Vehicular

Technology Conference, VTC2000, pp. 1006-1010.

[5] Yan Zhou, Francois Chin, Ying-Chang Liang, Chi-Chung Ko, “ Downlink

Capacity of Multirate CDMA Wireless Local Loop System with

Narrowbeam Antenna and SIR Based Power Control ”, Proceedings of

IEEE Vehicular Technology Conference, VTC2001, pp. 2359-2363.

[6] Harri Holma, Antti Toskala, “WCDMA for UMTS ”, John Wiley & Sons

Ltd, 2001.

[7] Jaana Laiho, “ Radio Network Planning and Optimisation for WCDMA”,

Thesis for the Degree of Doctor of Science in Technology, Helsinki

University of Technology, Radio Laboratory, July 2002, p. 66.

[8] Mathias Coinchon, Ari-Pekka Salovaara, Jean-Frédéric Wagen, ”The

Impact of radio propagation predictions on urban UMTS planning ”,

Broadband Communications, 2002. Access, Transmission, Networking.

2002, pp. 32-1 – 32-6.

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