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Novel mobile-based location techniques for UMTS

Jakub Borkowski and Jukka Lempiinen

Institute of Communications Engineering, Tampere University of Technology

P.O. Box 553, FI-33101 TAMPERE FINLAND

Email: {jakub.borkowski, jukka.lempiainen}@tut.fi

Website: http://www.cs.tut.fi/tlt/RNG

Abstract The aim of this paper is to propose two new hybrid

cellular techniques for mobile positioning in UMTS. Developed

methods are entirely mobile-based since no modifications are

required on the network side. Proposed techniques exploit

signal strength and delay spread measurements from at least

two base stations. The estimation accuracy of the presented

location techniques has been assessed by measurement

campaigns performed in various network configurations and

in diverse propagation environments. Measurement results

have indicated that the accuracy of positioning based on signal

strength measurements can be improved in majority of

propagation environments if one of the proposed methods is

used. Namely, in a measured area in an urban micro cellular

environment the mean error in the range estimation has been

reduced from 58 m to 30 m when introduced method has been

used. Slightly smaller improvement (30%) has been observed

in macro cellular urban and no improvement - in macro

cellular suburban/rural environment. In addition, obtained

measurement results have illustrated that the range estimation

error resulting from signal strength - based positioning iscorrelated with the distance from the measured base station.

Observations of the range estimation error have been

concludedin a distance-dependent model.

Key words: mobile-based, positioning, UMTS

1. IntroductionNumerous developed location techniques provide sufficient

accuracy, however very often required hardware and

software modifications prevent from fast deployment in the

existing networks. Ideally, deployment of the location

techniques should not involve any changes in the network

infrastructure and in the UE (user equipment). However,practically location methods always interfere with the

network implementation. Depending on the strategy,

required modifications can be concentrated either on the

network or the mobile side. Typically, network-based

positioning methods are preferred by network operators in

order to enable location-sensitive services without

requesting users to change their terminals. Examples of

these methods include Cell ID+RTT (cell ID + round trip

time) [1] or PCM (pilot correlation method) [2]. In turn,

modifications implemented in the UE should enable

positioning in a wide range of networks. Hence, mobile-

based approaches seem to be preferred by mobile phone

manufacturers. In majority, these techniques estimate the

position of the UE based on air interface measurements, for

instance, time-biased OTDOA (observed time difference of

arrival) [1][3] or signal strength-based methods [4].

The major sources of error in the position estimationconstitute multipath and NLOS (non line-of-sight)

propagation. A failure to detect the direct signal component

degrades the accuracy of time-biased positioning algorithms

as well as techniques relying on signal strength

measurements. Behavior of the resulting estimation error

has been widely studied in order to enable NLOS mitigation

methods improving overall positioning accuracy [5][6][7].

In addition, awareness of expected estimation errors

facilitates implementation of simulators for reliable

performance evaluation of positioning. For instance, in [8]

NLOS error in TOA (Time of Arrival)-measurements is

approximated by mean excess delay of the channel, which

in turn is assumed to be proportional to the observable rootmean squared delay spread. Moreover, in [9] time-varying

NLOS error is modeled as a distance-dependent random

variable proportional to the median excess delay of the

channel.

In this paper, two entirely mobile-based positioning

techniques will be proposed and assessed with comparison

to the basic signal strength based estimation. In addition,

the analysis of an error in range estimation based on signal

strength measurements will be illustrated and concluded in a

distance-dependent model.

2. Mobile-based hybrid positioningIn the first proposed hybrid algorithm ( Hybrid 1), the

distances between the UE and the hearable NodeBs are

estimated independently based on the RSCP (received

signal code power) and DS (delay spread) measurements

conducted on CPICH (common pilot channel). The

technique requires hearability of minimum two pilots from

different sites. However, high overlapping of cells in typical

UMTS (Universal Mobile Telecommunication System)

configurations ensures good pilot availability, thus this

requirement does not constitute the performance bottleneck.

Pairs of distances to each hearable NodeB are estimated

from the measurements performed by the UE. Namely,

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utilization of a simple path loss prediction model (COST-

231-Hata [10]) allows for a rough derivation of a

propagation distance from the RSCP measurements, as the

UE is aware of the CPICH transmit power. Similarly,application of an empirical DS distance-dependent model

provides an estimate of a distance (di,DS) to the i-th NodeB

based on the measured DS [11], (1):2

,

1

iRMS

i DSd

T

=

. (1)

In (1), RMSistands for the RMS (root mean square) of the

DS of the signal transmitted by the i-th NodeB and T1 is the

mean value of the RMS at 1 km distance from the

transmitter. For each measured NodeB, ranges estimated

from the RSCP and DS measurements are weighted and

averaged. The aim of such combining is to achieve better

accuracy of the range estimation than in case of a distanceestimated from the individual measurements. The definition

of weights for averaging is based on the observation that

due to local scattering a median of the received power has a

tendency to decrease more sharply than the RMS of the DS

at small UE-NodeB distances. Consequently, at larger

distances from the NodeB, the propagating rays are exposed

for more significant influence of various obstacles causing

variations of the DS, while RSCP measurements are

considered to be more reliable. According to these

assumptions, values of the weights have been empirically

defined, Table 1. In table, Wi,RSCP and Wi,DS represent the

weights of the ranges di,RSCP and di,DS estimated from the

RSCP and DS measurements, correspondingly. Finally, theposition of the UE is calculated by an unconstrained LMS

(least mean square) optimization algorithm based on the

averaged estimated UE-NodeB distances assuming that

coordinates of the NodeBs are forwarded to the UE.

The second proposed mobile-based positioning method

( Hybrid 2) exploits correlation properties between the

RSCP and the RMS DS. In the LOS situation, COST-231-

Hata model does not provide reliable distance estimates

from the RSCP measurements. Similarly, in a heavy

shadowed area, the estimated range based on the utilized

pathloss prediction model is also erroneous. In the Hybrid

2, DS measurements are utilized to provide information

about the multipath propagation in the measured radio link.Hence, proper exploitation of the DS measurements can

improve the accuracy of the RSCP-based positioning. In the

first step of the Hybrid 2 algorithm, the distances between

the UE and at least two hearable NodeBs are estimated from

the RSCP measurements based on the path loss prediction

model. Consecutively, the DS is measured on the CPICH

transmitted by the same NodeBs. According to the observed

RMS DS, the measured RSCP values are modified in order

to improve the accuracy of the range estimation. Namely,

the measured RSCP is lowered in locations that are

estimated to have LOS connection with the respective

NodeB, i.e., when the observed RMS DS is very small.

Correspondingly, in locations considered as NLOS, the

measured RSCP is enlarged. The degree of introduced

corrections depends on the measured RMS DS, as

illustrated in Table 2. Presented values of corrections have

been defined empirically from measurements conducted in

an urban UMTS network. Corrected RSCP values are

mapped to the distance based on COST-231-Hata model. At

the last stage of the positioning, the obtained ranges are

processed by the unconstrained LMS optimization

algorithm implemented in the UE that provides the final

position. Naturally, coordinates of the adequate NodeBs

need to be transported to the UE.

3. Measurement environmentThe performance of the proposed methods was assessed by

field measurements performed in a commercial UMTS

network. In order to evaluate practical applicability of the

hybrid positioning techniques, the measurements were

carried out in diverse propagation environments: in urban

micro cellular, urban macro cellular network, as well as insuburban/rural macro cellular environment. Measurement

area in a micro cellular topology mainly consisted of 3-

sectored sites deployed with a density that on average 400

m site spacing was maintained. In turn, analyzed macro

cellular urban network was formed by sites with similar

sector configuration deployed with 600 m mean site spacing

distances. The average base station antenna height in this

scenario exceeded the average rooftop level (15 m) by 5-10

m on average. In turn, sites of the suburban/rural macro

cellular network were deployed in a less dense manner, with

on average 1.2 km site spacing distances and 30 m mean

base station antenna height.

Table 1: Assignment of weights with respect to the relationship

between estimated ranges.

Condition between estimated ranges Wi,RSCP Wi,DS

di,RSCP > di,DS 0 2

di,RSCP < di,DS 2 0

di,RSCP di,DS;di,RSCP > 60% of the cell range

0.4 1.6

di,RSCP di,DS;di,RSCP < 60% of the cell range

1.6 0.4

Table 2: Defined RSCP correction in a function of a measured

RMS DS.

Measured RMS DS [s] Introduced RSCP correction [dB]

0.03 -15< 0.3 -10

< 0.45 -8

< 0.5 0

< 0.7 +5

0.7 +10

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(a) (b) (c)

(d) (e)

Figure 1: Comparison of cdf (cumulative distribution function) of error in range estimation performed by proposed hybrid mobile-based

algorithms and pure RSCP-based method in different propagation environments; (a) small cell area in micro cellular urban, (b) large

cell area in micro cellular urban, (c),(d) macro cellular urban, (e) macro cellular suburban/rural.

Measurement equipment consisted of a laptop PC withradio interface measurement software connected to the test

UE, WCDMA (wideband code division multiple access)

scanner, and the GPS (Global Positioning System) receiver.

Evaluation of the range estimation accuracy was performed

by collecting signal strength and delay profile samples from

the serving cell over multiple routes. Measurement routes

were defined in such a manner that distribution of samples

in respect to the distance to the measured NodeB was

reasonably balanced. Over every route, at least 200 samples

from each measured cell were collected. In every case, the

reported accuracy constitutes a deviation between the

estimated range from the measured samples and the

indication of the GPS receiver.

4. Measurement results4.1.Hybrid positioning accuracyExecuted measurements illustrate that DS is not distance

dependent, as practically range estimation performed by the

Hybrid 1 algorithm does not improve the accuracy in the

considered environments. Exceptionally, when distances

from the serving base station are small, e.g., in the analyzed

micro cellular environment with mean UE-NodeB distance

144 m, the Hybrid 1 minimizes the range error by 50 %,

down to 30 m, Fig. 1a. At the same time, in the analyzedmicro cellular scenario, the second proposed positioning

approach, Hybrid 2, improves the accuracy by 20 m in

comparison to the basic RSCP-based estimation, which

returns 58 m mean range error. However, in the scenario

with larger distances to the serving micro cell (mean UE-

NodeB distance 320 m), basically the proposed methods do

not improve the range accuracy of pure RSCP-based

estimation, Fig. 1b.

Results obtained from performance assessment

conducted in the urban macro cellular environment indicate

that DS is correlated with RSCP to some extent, Figs 1c and

1d. In locations that are characterized by significant drops

in a measured RSCP, the DS is correspondingly higher,indicating impact of multipath. Hence, the Hybrid 2

improves the accuracy of the UE-NodeB distance

estimation in comparison to the pure RSCP-based

positioning method, Figs. 1c and 1d. Precisely, in the macro

cellular urban environment with 290 m mean UE-NodeB

distance, the mean error in range estimation is reduced from

108 m in the RSCP-based estimation to 94 m when the

Hybrid 2 algorithm is used, Fig. 1c. However, at the same

time, the analysis reveals that DS is not essentially distance-

dependent, thus the Hybrid 1 method does not improve the

accuracy. Similar results are obtained from measurements

performed in the same propagation environment but with

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(a) (b)

(c) (d)

Figure 2: Range estimation error in a function of distance for RSCP-based estimation; (a) modeled range estimation error for urban

and suburban / rural environments; (b)-(d) comparison of the modeled and measured range estimation error illustrated for different

propagation environments; (b) macro cellular urban, (c) macro cellular urban, (d) macro cellular suburban/rural.

slightly larger average UE-NodeB distance (330 m). As

illustrated in Fig. 1d, the range estimation accuracy is

improved from 124 m (RSCP-based) to 107 m (Hybrid 2).

Expectedly, when distances to the serving NodeB are

smaller in the macro cellular topology configurations, the

range estimation accuracy is at the higher level. However,

in these scenarios, the proposed algorithms do not

significantly enhance the accuracy of the standard RSCP-

based estimation. For instance, in the macro cellular urban

environment with 150 m mean UE-NodeB distance, themean range error is at the level of 73 m and 57 m for

Hybrid 1 and Hybrid 2, correspondingly. In the same

scenario, RSCP-based estimation returns 61 m mean error

(not illustrated in figures).

Fig. 1e illustrates the positioning performance based on

samples collected in the macro cellular suburban/rural

propagation environment with average UE-NodeB distance

670 m. As expected, with larger distances from the serving

NodeB, the range estimation error is significantly larger.

Moreover, proposed positioning methods do not improve

the accuracy.

4.2.Distance-dependent model of the range errorEstimated range based on RSCP measurements contains an

error due to fading propagation environment and non

ideality of the utilized path loss to distance mapping model.

Presented measurement results clearly illustrate a tendency

of increase of the range estimation error with the distance

from the measured NodeB. Naturally, this phenomenon is

inline with expectations. However, more detailed analyses

of the accuracy of pure RSCP-based estimation indicate that

the range error is not essentially correlated with the distance

in locations near the measured NodeB. Precisely, it was

observed that for UE-NodeB distances below 250 m, range

estimation error is not distance-dependent. In turn, for

larger distances, range estimation error (E) observed in a

macro cellular environment is directly proportional to the

UE-NodeB distance (d) and can be described by a simple

function:

( ) ( )E d A d y= . (2)

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In (2),A is a proportional coefficient for a medianE(d) and

y is a lognormal variate defined as Y=log(y) is a Gaussian

random variable, having zero mean and a standard deviation

(

y). Based on the executed measurement trials, the definedmodel is the most accurate for A at a level of 0.35 for urban

and 0.47 for suburban/rural macro cellular propagation

environments. In turn, range error deviations represented by

variate y correspond to observations with y defined as 2.2

dB and 2.5 dB for urban and suburban/rural environments,

correspondingly. Fig. 2a illustrates an example of the

generated range error in a function of the distance for two

considered environments. The correspondence of the

modeled error to the observations from performed

measurements is presented in Figs. 2b and 2c (urban) and

Fig. 2d (suburban/rural). In illustrations, the UE-NodeB

distance in urban environment is limited to 450 m due to

cell sizes in the measured area. In turn, range errors withmodeled representations for suburban/rural scenario are

illustrated for distances up to 1 km.

5. ConclusionsTwo entirely mobile-based positioning techniques for

UMTS have been proposed. Introduced location methods

are based on RSCP and DS measurements and do not

involve any changes in the network. Therefore, the

technique once implemented in a user terminal can be

exploited in a wide range of networks. Performance

assessment has been carried out by extensive field

measurements in various environments. Reported results

illustrate the accuracy of the range estimation, whichnaturally influences the final accuracy of the position

estimation.

Performed analysis indicates that the RSCP is correlated

to some extent with the DS. Thus, the proposed Hybrid 2

method improves the accuracy of the pure RSCP-based

positioning in the most of the measured environments.

However, the obtained results yields that the observable DS

is not essentially dependent on the distance from the serving

NodeB. Therefore, the Hybrid 1 method is not applicable.

Practically, only in the measured micro cellular urban

environment in locations relatively near the serving Node B

(mean UE-NodeB distance 144 m), the Hybrid 1 improves

the accuracy by 50%. In other analyzed scenarios theHybrid 1 does not improve the accuracy. In turn, conducted

measurements illustrate that in urban macro cellular

environments, the range error is reduced from 108 m for

RSCP-based positioning to 94 m when the Hybrid 2

algorithm is exploited. The improvement of the Hybrid 2 is

expected to be more significant in urban macro cells if the

average distance to the measured NodeB is larger. Namely

in the analyzed scenario with 330 m mean UE-NodeB

distance, the average range estimation error for the Hybrid

2 is 107 m, which is 20% lower in comparison to the

positioning based on RSCP measurements. From the other

hand, in the suburban/rural environment proposed

techniques do not improve the accuracy of the signal

strength based positioning.

In the measured scenarios, range error in RSCP-basedestimation has been observed to increase with the distance

for the locations above 250 m from the measured NodeB.

Slightly different behavior of the range estimation error in

urban and suburban/rural environments has been described

in the distance-dependent model with appropriate

parameterization.

Acknowledgment

Authors would like to thank Elisa Networks Oyj for

enabling the measurement campaign, Nemo Technologies

Ltd for providing a measurement tool, and European

Communications Engineering (ECE) Ltd for helpful

comments.

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