multiparameteroptimizationinwcdmaradionetworks

8/9/2019 MultiparameterOptimizationinWCDMARadioNetworks

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Multi-Parameter Optimization in

WCDMA Radio Networks

Houtao ZhuNokia Research Center

Nokia Japan Co., Ltd.Tokyo, Japan

[email protected]

Theodore BuotNokia Networks

[email protected]

Abstract This paper proposes a novel sensitivity-matrix-basedmulti-parameter optimization algorithm, aiming to optimize theperformance of WCDMA radio access networks (RAN) in an adaptive

and automated way. A correlation approach is proposed to examinethe sensitivity between Key Performance Indicators (KPIs) and tuned

parameters. Simulation results proved that the proposed algorithm iseffective to lead the network performance to the optimum point andachieve around 10% improvement at throughput, Call Success Rate

(CSR) and 2-5% improvement at link power outage and Bad-Quality

(BQ) call rate in a simulated hexagonal macro-cell network.

Keywords-multi-parameter; optimization; sensitivity-matrix;

WCDMA; RAN

I. INTRODUCTION

WCDMA radio access networks are interference-limitedradio networks, which implies that the importance of real-timeand automated network operation mechanisms to deal with thetime-varied own-cell and other-cell interferences. Carefully

tuning various network and cell parameters are extremelyimportant in network operation to maintain or even enhance thenetwork performance. Auto-tuning these parameters is one

promising optimization approach to make the network alwaysreach its peak efficiency with reduced maintenance efforts. Theauto-tuning approach have been widely studied and validatedin [1][2][3][4]. For example, [4] studied how to auto-tune theaddition window (SHO parameter) to minimize the costfunction.Their approaches are based on the assumption that thevariations of KPIs (such as call blocking rate in their cases) aredependent on the tuned parameters (i.e., addition window intheir cases). However, the sensitivity between parameters andKPIs are not clear and not guaranteed in the above studies. If

the KPIs are not sensitive to the tuned parameters, they will notreact to the tuning actions. In addition, normally one parameteris chosen in the previous auto-tuning process. In the realnetwork, if we tune parameters separately, it is not efficient andthe multiple KPIs can be correlated. Then the results canconflict with each other. Therefore, this study first investigatesthe sensitivity between parameters and KPIs by using acorrelation approach, then proposes a novel sensitivity-matrix-

based multi-parameter optimization algorithm, and finallyvalidates the algorithm by using a dynamic WCDMA simulator.Section II describes the correlation approach to examine thesensitivity between parameters and KPIs. The sensitivity-

matrix-based multi-parameter optimization algorithm isexplained in section III. Section IV shows the simulationscenario and results.

II. SENSITIVITY BETWEEN KPIS AND PARAMETERS

Tuning parameters in the WCDMA network is also called asparameter optimization, which aims to search for a set of best

parameter values that determines the equilibrium point of

network performance, i.e., a trade-off between capacity,

coverage and quality. As mentioned before, it is important to

clarify the sensitivity relations between KPIs and parameters

at first. A correlation approach is proposed for this purpose,which correlates the KPI results with parameter values for a

set of parameter-tuning rounds. In addition, the variation of

KPI after tuning parameters also needs to be taken into

account. There are some cases that the correlation is high but

the KPI does not change much, i.e., variation is low.

Accordingly, sensitivity, S, can be defined as the product of

correlation coefficient C and the variation factorVas:VCS = (1)

yx

xy

yxC

),cov(== (2)

max

minmax )(X

XXVx

= (3)

Where , y represents the KPI x and parameter y. maxX and

minX represents the maximum and minimum values of the

KPI xs results in the set of measurement rounds while tuning

parameter y.

To verify our approach, a series of simulations were rununder three different scenarios: the capacity-limited network,the coverage-limited network and the network without capacityand coverage limitations. Several key parameters, additionwindow (window add), drop window (window drop) and droptimer in soft handover control, target values of uplink noise rise(PrxTarget) and target values of downlink transmission power(PtxTarget) in admission control, maximum allowable channel

power (PtxDLabsMax), and the offset of the primary CommonPilot Channel (CPICH) transmission power to the downlinktransmission power of the reference service channel

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(CPICHToRefRABOffset) are chosen to examine thesensitivity relations with such KPIs as CSR, BQ call rates, link

power outage etc. in the simulated WCDMA networks. Figure1 shows an example of the correlation relationships betweenthe parameter addition window (wadd) + drop window (wdrop)with different KPIs. Here the difference between wadd andwdrop is kept as 2 dB. Sensitivities of this parameter(wadd+wdrop) with different KPIs were calculated in different

scenarios and were shown in Figure 2. Under differentscenarios, the sensitivity value of the parameter with KPIs (e.g.,SHO overhead) is quite close, which proves that this approachcan be applied in different scenarios to examine the sensitivityrelations between parameters and KPIs. The difference ofsensitivity values under different scenarios also implies that wecant apply the fixed sensitivity values to set up the sensitivityrelations in specific scenarios.

Figure 1: Correlation between wadd+wdrop with KPIs in the scenario

of the capacity-limited network.

Figure 2: Sensitivity of wadd+wdrop with KPIs in the differentscenarios.

III. SENSITIVITY-MATRIX-BASED MULTI-PARAMETEROPTIMIZATION

To optimize the network performance by tuning multiple

parameters simultaneously, a sensitivity-matrix-based

optimization approach is designed. A tuning matrix is built

with one example shown in Table 1. It consists of columns ofKPI preference, KPI deficiency and the columns of the

sensitivity matrix. This sensitivity matrix is calculated bytaking different proportions from both the heuristic sensitivity

matrix and the current sensitivity matrix. Here the heuristic

matrix consists of the sensitivity relations between KPIs and

parameters, whose relations are statistically obtained from the

extensive simulations or from the measurement data in the realnetworks. Current sensitivity matrix is calculated by using

current KPI variation and parameters in the cell

clusters/cells/network we are tuning now. In the sensitivity

matrix, row represents the sensitivity relations of one KPI with

multiple parameters, i.e.,

),...,( 21 nii paraparaparagKPI = (4)

On the other hand, column represents the sensitivity relationsof one parameter with multiple KPIs, i.e.,

),...,( 21 mll KPIKPIKPIfPara = (5)

It is expected that some KPIs may be deviated from their

target values in the real network. Then KPI deficiency is

defined as a function of current KPI and KPI target:

etKPItetKPItKPI

encyKPIdeficie arg)arg(

= (6)

In case of KPI deficiency it is obvious to tune all parameters

relevant to the problematic KPIs so that these KPIs can be

improved. In addition, operators can have preferences on

different KPIs, e.g., prefer call success rates than bad-qualitycall rates in the network rollout stage. Hence, the tuning index

is defined as the product of KPI deficiency, preferences and

sensitivities, shown as follows:

=i

iii ysensitivitdeficiencypreferenceIndex (7)

Where i represents the row i of the tuning matrix. The indexdetermines whether and in which direction to tune the relevant parameters. A cost function is also monitored as the whole

performance index of cell clusters/cells/network. The

optimization ends when there is not much improvement of the

cost function between 2 consecutive optimization steps.

Therefore para1 is decided to tune up because of its positive

index. Vice versa, para 2 will be tuned down. KPI targets and

KPI preference will be input by network operation staffs

before the optimization.

Table 1: Example of a tuning matrix

Preference KPI

deficiency

KPI Para 1 Para 2

5 10 CSR 0.3 -0.4

1 -2 BQ-call rate -0.1 0.2

5 1 Throughput 0.1 0

1 -1 BsTxP 0.2 0

Tuning index 0.5 -0.4

0

0.2

0.4

0.6

0.8

1

1.2

3 5 7 9 11 13 15

wadd+wdrop [dB]

poweroutage[%]

0

100

200

300

400

500

600

No.ofblockedc

allsorSHOO

[%

]

DL pow er outage (%) Packe t blocked calls SHO Overhead

-1

-0.5

0

0.5

1

power

outage

[%]

BSTxp[W]

SHO

Overhead

ASU

period

[sec]

speech

BQ[%]

packetBQ

[%]

Sensitivityofwadd+wdrop

capacity-l imited coverage-l imited no-limitation

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IV. SIMULATION RESULTS AND ANALYSIS

A. Simulation Setting and ScenarioAn 18 hexagonal macro-cell scenario, shown in Figure 3, is

used to examine the proposed optimization algorithm. Adynamic WCDMA simulator [5] is used, whose dynamic

feature implies that terminals move inside the simulated

network and fading and interference are time-varied. Main

simulation parameters are listed in Table 2. Mixed traffic

(speech 70%, packet 30%) with total 5700 terminals is

simulated in the network. The whole simulation round consistsof 2 simulations: (1) reference simulation with the initial

values of the parameters; (2) simulation with the parameters

adapted according to the optimization algorithm. The whole

simulation takes 100000 frames (1000 sec.) and after every

5000 frame the tuning action will be carried out. This 5000-frame period is called as quality update period. After each

period, quality counters, or KPI counters, will be examined

and tuning decisions will be made. No action is done in the

first 20000 frame of the simulation. The cost function CF in

the simulation, used as the whole performance index of the

network, is expressed as follows:

BsMaxBsTxPtthroughtpuoutageBQCSRCF *1*5*5*5)1(*5

1++++=

Where CSR, BQ, outage, throughput, BsTxP and BsMax

represent call success rate, bad-quality call rate, link poweroutage, combined throughput of speech and packet bearers,

averaged total transmission power at the Base Station (BS)

and the maximum allowed BS transmission power,

respectively. Table 3 shows the initial values and step-sizes of

parameters used in the optimization. Table 4 gives the target

and preference values of KPIs.

Figure 3: Simulation scenario of a macro-cell network.

Table 2 Main simulation parameters

Simulation setting

Simulation environment Hexagonal macro

Terminal velocity 3 km/hour

Cell radius 2000 m

Propagation algorithm Okumura-Hata

Fast-fading Jakes model

Slow-fading

Log-normal (8dB

deviation)

Multi-path model

2-path Pedestrian-A {-

0.266, -12.27} dB

Carrier frequency 2 GHz

Chip rate 3.84 Mcps

BS maximum transmission

power

20 W

CPICH transmission power 1 W

Power control dynamic range

70 dB in UL,

30 dB in DL

Mobile station maximum

transmission power125 mW

PrxTarget/Offset 4dB/1dB

PtxTarget/Offset 40dBm/1dB

Call arrival rate for a mobile

station5 calls/hour

Average speech call length 120 s

Speech data rate 8 kbps

Packet data rates8, 12, 32, 64, 144

kbps

Table 3: List of tuning parameters and step size

Parameter Initial value Step size

Window add 4 dB 0.5 dB

Window drop 6 dB 0.5 dBDrop timer 32 frame 20 frame

PrxTarget 4 dB 0.2 dB

PtxTarget 10 W 0.2 dB

CPICHToRefRABOffset 5 dB 0.5 dB

PtxDLabsMax 36 dBm 0.5 dB

Table 4: KPI targets and preference

KPI Target values Preference

CSR 95 % 5

BQ-call rate 2 % 1

Power outage 2 % 5

Throughput 5BsTxP 1

B. Results and AnalysisFigure 4 compares the values of cost function in the reference

simulation with that of optimization simulation. On the

average, cost of optimized case is reduced around 20%, which

proves the effectiveness of the optimization algorithm. The

detailed improvements are listed in Table 5.

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Table 5: KPI improvements and degradations after optimization

absolute = KPI_optimized-KPI_non-optimized, relative =(KPI_optimized-KPI_non-optimized)/KPI_non_optimized. Both arecompared by the average value).

KPI Changes

DL throughput 7.2 % (relative)

UL throughput 3.3 % (relative)

DL packet CSR 12.3 % (absolute)UL speech BQ-call rate -0.4 % (absolute)DL speech BQ-call rate -2.0 % (absolute)

UL packet BQ-call rate -1.1 % (absolute)

DL packet BQ-call rate -0.1% (absolute)

Average BsTxP 21 % (relative)

UL power outage 0.1% (absolute)

DL power outage -4.5% (absolute)

From the graph, we can see the fluctuation of cost function in

the reference case due to the dynamic performance of

WCDMA radio networks, which implies the importance of

real-time and automated network operations. Figure 5, Figure

6, and Figure 7 illustrate the changes of parameter values in

the optimization process. The parameter values converge after

quality-update period 12. Please be noted that tuning range of

wdrop-wadd is limited as min of 1 dB to max of 5 dB and

PtxTarget is also limited by the maximum value of 42 dBm.

The rationale of the variations of parameter values can be

understood by observing the changes of KPI values, shown in

Figure 8, Figure 9, Figure 10, Figure 11, and Figure 12. From

these graphs, DL power outage first reacted to the tuning of

parameters with the improvement from 6 % outage to 1%

outage during the period 1 to 3. PtxTarget is continuously

increased, aiming to improve the DL CSR. But DL CSR is not

increased until period 6. This is probably because of thecompetition between DL power outage and DL CSR. As the

result of competition, DL link power is increased at first (if

looking at the changes of PtxDLabsMax and

CPICHToRefRABOffset), which erased the impact of the

increase of PtxTarget. This also implies that the tuning step-

size of each parameter is critical. If we increased PtxTarget at

a large step-size but increased PtxDLabsMax and

CPICHToRefRABOffset at a small step-size, maybe we can

improve both DL CSR and DL power outage. UL power

outage is not improved, which proves that this is a coverage-

limited network scenario. We can improve the DL power

outage by tuning DL parameters. But with the limitedmaximum transmission power of 21 dBm at UE, parameter

tuning is not possible to change the coverage limitation at UL.

This may only be improved by revising the network plan. DL

throughput is increased because PtxTarget is continuously

increased. UL throughput doesnt change much if comparing

non-optimized case with optimized case. This is because the

UL power outage and UL BQ-call rates overwhelmed the

preference of improving UL throughput in the tuning process.

PrxTarget is actually tuned down for the quality and coverage

reasons.As to other KPIs such as DL&UL CSR of speech and

UL packet CSR, they are always kept at 100% in the

optimization.

Figure 4: Comparison of cost-function variation in optimized and

non-optimized cases.

Figure 5: Variation of parameter values in the simulation.

Figure 6:Variation of parameter values in the simulation.

Figure 7:Variation of parameter values in the simulation.

3

3.5

4

4.5

5

5.5

1 2 3 4 5 6 7 8 9 10 11 1 2 13 14 15 16

Quality update period

Costf

unction

Non-optimized case optimized case

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


Droptimer[frame]

0

2

4

6

8

10

1214

16

18

wadd+wdrop[dB]

drop t imer [frames] wadd+wdrop [dB]

33

34

35

36

37

38

3940

41

42

43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Quality update pe riod

Parametervalue

PtxTarget [dBm] PtxDLabsmax [dBm]

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Quality update pe riod

Parametervalue

wdrop-wadd [dB] PrxTarget [dB] CPICHoffset [dB]

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Figure 8: Comparison of KPIs in the optimized and non-optimized

cases.

Figure 9:Comparison of KPIs in the optimized and non-optimizedcases.

Figure 10:Comparison of KPIs in the optimized and non-optimized cases.

Figure 11:Comparison of KPIs in the optimized and non-optimized cases.

Figure 12: Comparison of KPIs in the optimized and non-

optimized cases.

V. CONCLUSION

Because of the dynamic behaviors of interference and

supporting multi-media traffic in the WCDMA networks, it

is a crucial and not-easy task for operators to optimize

WCDMA networks. This paper proposes an automatic andadaptive approach to simultaneously optimize multiple

parameters of the WCDMA RAN. Sensitivities between

parameters and KPIs are first examined in different networkscenarios. Then a sensitivity-matrix-based multi-parameter

optimization algorithm is adopted to optimize the

performance of a hexagonal macro-cell network. The

simulation results prove that the algorithm can not only

achieve significant improvements (20% reduction in terms

of cost function) but also can help operation staffsunderstand the rationale behind the process of tuning

parameters.

ACKNOWLEDGMENTWe thank Mika Kolehmainen for his support on the

implementation of the simulator.

REFERENCES

[1] Janna Laiho, Achim Wacker and Tomas Novosad, Chapter 10, Radionetwork planning and optimization for UMTS, John Wiley & Sons,

2002.

[2] T. Buot, H. Zhu, H. Schreuder, S. Moon, B. Song, and T. Eriksson,Soft handover optimization for WCDMA networks, in the

proceedings of the 4th Int. Symp. On Wireless Personal Multimedia

Communications, pp. 141-146, Sept. 9-12, 2001, Aalborg, Denmark.[3] K. Valkealahti et al., WCDMA common pilot power control with

cost function minimization, in the proceedings of 2002 IEEE 56thVehicular Technology Conf. (VTC Fall 2002) pp. 2244-2247, Sept.

2428, 2002 Vancouver, British Columbia, Canada.

[4] Adrian Flanagan and Tomas Novosad, Automatic selection ofwindow add in a WCDMA radio network based on cost function

minimization, in the proceedings of IEEE 7 th Int. Symp. On Spread-

Spectrum Tech. & Appl., pp. 672-676, Prague, Czech Republic, Sept.2-5, 2002.

[5] Hamalainen, S., et al., A novel interface between link and systemlevel simulations, in proceedings of ACTS Mobile CommunicationsSummit, pp. 599-604, Aalborg, Denmark, October 7-10, 1997.

0

1

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3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


D

Lpoweroutage[%]

non-optimized case optimized case

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120

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


DLpa

cketCSR[%]


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ULpoweroutage[%

]


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45

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


PacketBQ-

callrate[%]

non-opt imized (UL) non-opt imized (DL) opt imized (UL) opt imized (DL)

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550600

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16


Throu

ghputpercell[kbps]

non-optimized case (DL) non-optimized case (UL)

optim ized case (DL) opt im ized case (UL)

0-7803-8256-0/04/$20.00 (C) 2004 IEEE

multiparameteroptimizationinwcdmaradionetworks

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