Influence of Positioning Error on X-Map Estimation

in LTE

Michaela Neuland, Thomas Kürner Technische Universität Braunschweig

Institut für Nachrichtentechnik Braunschweig, Germany

{neuland, kuerner}@ifn.ing.tu-bs.de

Mehdi Amirijoo Ericsson AB

Ericsson Research Linköping, Sweden

mehdi.amirijoo@ericsson.com

Abstract—Today, the network operators have to perform drive/walk tests when planning and optimizing their networks. These drive/walk tests are costly and time-consuming, cover only a limited part of the network, and capture only a snapshot in time. To overcome these difficulties, the mobile devices in the network can be used as probes by continuously reporting the observed service quality together with their positions. In this paper, we introduce the concept of X-map estimation, which is a function that processes the measurement data into a geographic map with overlay performance information, referred to as an X-map. By continuously processing measurements, the X-map estimation function approximates the spatial characteristics of the network performance and therefore reduces the costs of drive/walk tests. Additionally, X-maps can be used by Self-Organizing Networks (SON), especially in functionalities that address optimization of coverage, capacity, and quality. In this paper, we introduce two approaches for determining X-maps and we analyze the influence of the applied positioning method on the accuracy of the X-maps. We show that the accuracy of the positioning technique can have a significant impact on the accuracy of the X-map.

Keywords-X-map estimation; network monitoring; position error; drive test minimization; self-organizing network (SON)

I. INTRODUCTION

Today, network operators resort to planning tools to dimension and plan their networks according to a specific business strategy, formulated in terms of end user perception and quality. The output of the planning includes site locations, types of base stations and their configurations (e.g., output power and number of sectors). Current planning tools are based on digital maps with topographic and land use information (e.g., buildings, natural areas, and roads) as well as on a tuned path loss prediction model (e.g., Okumura-Hata) [1]. There is also a need to take into account capacity related factors, including the number of subscribers, user profiles, and the spectrum available.

The approach based on planning tools and predictions is, however, not fully accurate due to, e.g., (i) imperfections in the map data and building data, (ii) simplifications or approximations in the applied propagation model, (iii) changes in the environment, e.g., due to constructions/demolitions or seasonal effects (foliage changes), and (iv) changes in traffic distribution and user profiles. The above mentioned

shortcomings force operators to continuously optimize their networks using measurements and statistics, and to perform drive/walk tests which provide a picture of the end user perception in the field and enable the operator to identify and localize the causes for poor performance (e.g., incorrect tilts or handover settings). Drive/walk tests are, however, not ideal since only a limited (outdoor) part of the network can be analyzed due to access restrictions and the costs involved. Furthermore, only a snapshot in time of the conditions in the field is captured.

A viable method for overcoming these difficulties with drive/walk tests is to use the user equipments (UEs) in the network to report observed service quality along with the positions where the measurements are taken. The standardization of such UE reports is currently being carried out in 3GPP for the Long Term Evolution (LTE) system under the name Minimization of Drive Tests (MDT) [2]. The UE reports can be used by a so-called X-map estimation function which processes the UE reports in a geographical map with overlay performance information, referred to as an X-map where X can stand for different types of performance information. The X-map estimation function continuously monitors the network and estimates the spatial network performance, e.g., coverage and throughput. X-map estimation addresses the problems with prior approaches by probing a larger sample of UE locations, reducing the costs involved in drive/walk tests, and continuously tracking the network state as the network and its environment (e.g., topography) evolve. X-maps can aid operators in observing the performance of their networks. Furthermore, the information embedded in an X-map may be used by a Self-Organizing Network (SON) [3][4][5][6], especially in functionalities that address optimization of end user perception and quality.

The accuracy of an X-map depends on a multitude of factors, e.g., the UE positioning technique applied, the UE measurement accuracy, the number of measurements taken, and the architecture. In particular, it is foreseen that the accuracy of the positioning technique has a significant impact on the overall X-map estimation accuracy which has already been shown for positioning methods used in the Universal Mobile Telecommunications System (UMTS) in [7][8][9]. In this paper, we report initial findings on the accuracy of the X-maps for LTE as a function of the positioning accuracy, which in turn depends on the positioning method applied. For this, we

978-1-4244-8331-0/11/$26.00 ©2011 IEEE

evaluate two approaches for creating X-maps which are already known in the literature (see [11], [12], [13], and [14]). However, their performances were investigated in a UMTS network which uses different positioning mechanisms compared to LTE. Further, the two approaches have previously been evaluated individually. We carry out a comparative assessment of the two approaches and analyze their differences in accuracy and “map coverage” (degree by which the area of interest is covered). Finally, we provide a comparison between the X-maps using the Global Positioning System (GPS) and the Observed Time Difference Of Arrival (OTDOA) method as positioning techniques.

The paper is organized as follows: In Section II, the two approaches for estimating X-maps are presented. In Section III, the realistic simulation scenario as well as the position error modeling are described. Results for different X-maps using the two approaches and different positioning methods are given in Section IV. Finally, Section V concludes the paper.

II. X-MAP ESTIMATION APPROACH

We consider two approaches to create X-maps with the help of measurement data, see Figure 1. A Location and Measurement Unit (LMU) collects the measurement data together with the corresponding position and time when the measurement was taken, and delivers this data to the X-map estimation function. In order to position the UEs in LTE, the Evolved Serving Mobile Location Center (E-SMLC) is introduced [10]. Depending on the environmental conditions and the UE/eNodeB positioning capabilities it decides which position method should be used and combines all the received results to determine one single location for each UE. Furthermore, this entity can provide additional information like the accuracy of the position estimate or the velocity of the UE. Thus, this entity provides all important positioning features which have to be fulfilled by the LMU and may be utilized for providing the necessary data for the X-map estimation function. In addition, various measurements available in the RAN, e.g., interference and load, are also collected by the RAN Measurement Unit (RMU) and utilized in order to better estimate the network state. The X-map estimation function uses this data from LMU and RMU for directly creating an X-map (approach 1) or for calibrating a propagation model (approach 2).

Approach 1 uses the UE measurement data together with the estimated position to update the corresponding bins (pixels) in the X-map where the UE is located, similar to [11] and [12]. Essentially, each time a UE report becomes available, the corresponding bin of the X-map is updated. Various update mechanisms are possible, e.g., replacing the current value in the bin or taking previous measurements into account by using, e.g., an exponential filter approach. In this paper, we form the average of the value in the X-map and the UE reports available at a definite time. Obviously, in areas where no UEs have been located (implying that no measurements have been taken), no information is available in the corresponding X-map. Note, the concept that has been standardized in 3GPP [2] also allows UEs that are out of coverage to log measurements (e.g., received signal power) and, as such, the X-map can also be defined for locations where the UEs have no coverage.

UE Location and Measurement Unit (LMU)

Bin UpdatePrediction

Data

UE/RAN Measurement, Time, Position

Measurement, Time, Location Data

RAN Measurement Unit (RMU)

UE1 UEn

Propagation Model Calibration

CalibratedParameters

Propagation Model

X-Map EstimationFunction

approach 1: approach 2:

X-Map

Figure 1. Overview of the X-map estimation approach

Approach 2 uses prediction data created by means of the Okumura-Hata propagation model (see, e.g., [1]). The propagation model is adapted to the environment of the corresponding eNodeB in the propagation model calibration process. A possible calibration method is described in [13]. In the current implementation, correction factors are calculated for the considered land use classes based on the collected measurement data, similar to the method introduced in [14]. The correction factors are updated periodically (in this paper, we assume a period of 150 s). By the calibration process, the accuracy of the propagation model can be improved. In contrast to approach 1, this approach has the benefit that an X-map is also available for locations where no UEs have been located. As such the “coverage” of the X-map is improved. However, the X-map derived based on approach 2 is less accurate than the X-map based on approach 1, as will be shown in Section IV.

III. SIMULATION ENVIRONMENT

A. Scenario

An urban area of 1.5 km x 1.5 km in Germany is chosen for the simulations. This scenario includes 20 mobile users which move along the streets. The user traces have been derived with the help of the traffic simulator SUMO (Simulation of Urban MObility) [15]. For the LTE network considered in this scenario, the UMTS layout of a German network operator is used with the same site locations, sector orientations, and antenna tilts. For these sites, realistic path loss predictions at 2.6 GHz are available. These predictions are used as a reference for determining the accuracy of the resulting X-maps. For the area of the scenario, clutter data is derived from the freely available information of the OpenStreetMap project [16]. Five different clutter classes are distinguished, namely, buildings, natural areas, waterways, streets, and railways. Furthermore, satellite orbits for a specific date and time are available for modeling the positioning error of GPS. Further details about the scenario can be found in [17].

The 30 strongest cells for each user position are determined based on the provided predictions. The user positions and the corresponding path loss information are updated every 200 ms. The whole simulation time is 1000 s.

B. Position Error Modeling

For LTE three different localization methods are foreseen [10]. The first location function is the network-assisted version of Global Navigation Satellite Systems (GNSS), e.g., GPS or Galileo. Different GNSSs can be used individually or in combination with other GNSSs. The network assists the UE GNSS receiver by providing assistance data (e.g., visible satellite lists, clock corrections, reference positions) to reduce the UE GNSS start-up and acquisition times, to increase the UE GNSS sensitivity, and to allow the UE to consume less handset power than with stand-alone GNSS. With GNSS the highest accuracy can be achieved which meets the FCC requirements [18].

The second location function is OTDOA which utilizes the differences of time measurements of at least three eNodeBs for calculating the UE position. In order to increase the hearability of the eNodeBs a Positioning Reference Signal (PRS) with a frequency reuse of six in combination with low-interference subframes (LIS) is introduced [19].

The last localization method, the enhanced cell ID positioning method, uses information about the serving cell and additional radio resource measurements like the Reference Signal Received Power (RSRP) or the Reference Signal Received Quality (RSRQ). Since this positioning method has a poor accuracy, we consider only GNSS and OTDOA as positioning techniques in the following.

In this paper, we do not focus on modeling the different positioning methods, but on modeling the position error which is then added to the true positions, thus, obtaining the estimated positions. The modeled position error depends on the geometry between transmitter and receiver, on the number of measured signals, and on the standard deviation of the errors of the measurements used for the positioning. For this, the approach described in [20] is used for GPS and enhanced for OTDOA. According to this paper, the minimum mean square error (MMSE) of an estimated position can be calculated as

{ }( ) { } [ ] [ ]

{ })(

)()()()ˆ(min 2,21,11

θθθ

θθI

ΙIICov

DettrtrMMSE

+=== −

(1)

where Cov is the covariance matrix of the estimate θ of the unknown position and I is the Fisher information matrix. The entries of I for GPS can be found in [20]. For OTDOA, the entries of I are calculated as follows:

[ ] ( )

[ ] [ ] ( )[ ] ( )

=

=

=

∗−∗=

∗−∗==

∗−∗=

N

kBSkBSk

N

kBSBSkBSkBSk

N

kBSkBSk

yy

yxyx

xx

ucuc

uucuuc

ucuc

2

21,1

2,2,2

21,1,1,,1,22,1

2

21,1

2,1,1

)(

)()(

)(

θ

θθ

θ

I

II

I

(2)

where without loss of generality, eNodeB 1 is chosen as the reference eNodeB for calculating the time differences. The variable N is the number of time measurements, uBS,k is a unit vector originating at the UE and directed towards the k-th eNodeB, and ck is calculated according to

2

,0

1

∗=

kOTDOAk c

cσ

(3)

where c0 is the speed of light and σOTDOA,k is the standard deviation of the measurement errors which are modeled as a Gaussian random variable with zero mean. According to [21], σOTDOA,k is chosen as 39 ns.

The visible satellites for each user position are determined with the help of a ray tracer and the satellite orbits of the scenario. A satellite is assumed to be visible if the direct path between the satellite and the UE exists, i.e., reflections are not considered and, as such, the coverage of GPS positioning will be somewhat pessimistic in the following results. An eNodeB is assumed to be detectable by the UE if the RSRP is greater than or equal to -124 dBm and if the SINR is greater than or equal to -6 dB [22]. The PRS with a frequency reuse of six and the LIS [19] which are introduced in order to increase the number of detectable eNodeBs are modeled by excluding the six strongest cells from the interference calculation. This is considered for all presented OTDOA results

In addition to the presented positioning methods, the combination of GPS and OTDOA is used in the following way. First, UE positioning is attempted using GPS and the Fisher information matrix provided by [20]. If GPS cannot provide a valid position estimate, i.e., if less than four satellites are visible, then OTDOA is used as positioning method and (2) is applied. Thus, the number of available position estimates can be increased as shown in Section IV.A.

IV. RESULTS

In this section, we first present the mean error and standard deviation for the introduced positioning methods (see Section A) since we assume that the accuracy of the applied positioning method will have an impact on the accuracy of the X-maps. This is followed by an analysis of the accuracy of the X-maps created with the two approaches, described in Section II, in combination with using different positioning techniques (see Section B).

A. Position Error Modeling

Using OTDOA as positioning method provides valid position estimates for 66.4 % of all user positions in the scenario, whereas GPS provides valid position estimates for 77.1 % of all user positions as can be seen in TABLE I. . This number can be increased to 90.6 % of all user positions if a combination of GPS and OTDOA is applied. The mean position errors and the standard deviations of GPS and the combination of GPS and OTDOA, respectively, are very similar and in the range of the pixel size of an X-map which is 10 m x 10 m. The mean position error of OTDOA is 16.3 m and, thus, approximately twice the mean position error of GPS. The standard deviation of the position error using OTDOA is 31.4 m.

Note, that the values provided here are determined based on the direct path to the satellite orbits for a specific date and time for the considered scenario. Using a different scenario and

different satellite orbits might change the results in TABLE I. , as it is indicated in [23].

TABLE I. RESULTS FOR THE POSITION ERROR MODELING

GPS OTDOA GPS+OTDOA

valid positions 77.1 % 66.4 % 90.6 %

mean error 8.3 m 16.3 m 8.8 m

standard deviation 6.7 m 31.4 m 6.7 m

B. X-Map Estimation

In Figure 2. , two examples of X-maps for a specific site of the scenario which has an azimuth of 240° can be seen. On the left side, approach 1 is used to create the X-map, whereas the X-map on the right side is based on approach 2. For both X-maps, the real positions provided by the realistic scenario are applied.

Figure 2. X-maps based on approach 1 (left) and based on approach 2 (right) using the real positions

In order to determine the accuracy of the X-maps, every pixel with an available RSRP value is compared to the provided prediction of the corresponding eNodeB (i.e., the real value). This means that in case of approach 2 all pixels in the considered area are used for determining the accuracy (including more pixels than used for the calibration process). The advantage of approach 1 is that the resulting X-map reflects the real situation in the network since the measurement data is used directly for creating the X-map. However, the disadvantage is that the X-map provides RSRP values for only those pixels which are covered by the UEs (in Figure 2. , dark red means that no RSRP value is available). In contrast, every pixel in the area has an RSRP value when using approach 2, i.e., the coverage of the X-map is greater compared to approach 1. The accuracy, however, is not as good as for the X-map based on approach 1 with a mean error (mean) of 2.1 dB and a standard deviation (std) of 6.6 dB as shown in TABLE II. . The resulting root mean square error (rms) using approach 2 is 7.0 dB.

TABLE II. RESULTS FOR DIFFERENT X-MAPS USING THE REAL POSITIONS

approach 1 approach 2

mean std rms mean std rms

0.0 0.2 0.2 2.1 6.6 7.0

The accuracy for approach 2 is in the range of what can be achieved with calibrated propagation models for small macro and micro cells. In, e.g., [1], about 7 dB up to 9 dB is mentioned as the average standard deviation of the prediction error of the presented models. The absolute value of the mean errors typically ranges from about 0 dB up to 6 dB. For the analyses, building data and measurement data at 947 MHz were available for an area in downtown Munich [1].

Figure 3. shows X-maps based on approach 1 using (a) GPS, (b) OTDOA, and (c) a combination of GPS and OTDOA as positioning method. The “bulk” of measurement data roughly in the middle of the X-map using OTDOA as positioning method are due to high position errors as a result of the hearability problem since these positions are near the serving eNodeB. However, using OTDOA as positioning method increases the number of valid position estimates in the right area of the corresponding X-maps. This area is characterized by dense urban canyons where the probability of at least four visible satellites is low.

As can be seen in TABLE III. , the standard deviation and the rms of the X-map based on approach 1 are increased by about 2 dB using GPS or the combination of GPS and OTDOA as positioning method instead of the real positions. They are increased even more when OTDOA is used as positioning method (standard deviation and rms of 4.6 dB). For X-maps based on approach 2, the standard deviations are very similar for the different positioning methods compared to the one using real positions (6.6 dB and 6.7 dB, respectively). However, the mean error is about 2 dB higher when using OTDOA as positioning method. Thus, the rms value is increased by about 1 dB, too.

TABLE III. RESULTS FOR X-MAPS USING DIFFERENT POSITIONING METHODS

approach 1 approach 2

mean std rms mean std rms

GPS 0.1 2.3 2.3 2.6 6.6 7.1

OTDOA 0.0 4.6 4.6 4.6 6.7 8.1

GPS + OTDOA 0.0 2.3 2.3 2.9 6.7 7.3

Using a more inaccurate positioning method like OTDOA decreases the accuracy of both approaches. However, the impact of the positioning error on the accuracy of the X-maps is higher for approach 1 compared to approach 2. The reason for this is that the measurement data is directly used for creating the X-map and, thus, the errors are directly transferred to the X-map. In contrast, approach 2 collects the measurement data over the whole considered area and over a specific time interval for determining the correction factors. Thus, the probability is high that the errors compensate each other and consequently they have a smaller impact on the accuracy of the resulting X-map.

V. CONCLUSION

In this paper, two approaches for creating geographic maps with overlay performance information, referred to as X-maps,

have been presented and the influence of the positioning error on the accuracy of the resulting X-map has been analyzed. Approach 1 uses the measurement data together with an estimated position directly for updating the bins of an X-map. This has the advantage that this approach is very accurate. However, the information in the X-map is limited to areas where measurement data is available. Furthermore, this approach is affected to a higher degree by the positioning accuracy of the applied positioning method compared to approach 2 which uses a calibrated propagation model for calculating the X-map. Using a calibrated propagation model as basis for the X-map has the advantage that information about the network performance of interest can be provided for every pixel in the considered area. The disadvantage is that this approach is less accurate compared to approach 1. However, the mean errors and standard deviations of the analyzed X-maps based on approach 2 are in the range of what can be achieved with today’s deterministic or semi-deterministic propagation models (see, e.g., [1]).

As a next step, we want to consider other factors which have an influence on the accuracy of the X-map, namely, the UE measurement accuracy and the number of measurements taken. Furthermore, we want to have a look at X-maps determined with the help of real measurement data.

ACKNOWLEDGMENT

The presented work was carried out within the FP7 SOCRATES project [24], which is partially funded by the Commission of the European Union.

REFERENCES [1] COST 231 Final Report, available at http://www.lx.it.pt/cost231/.

[2] 3GPP TR 36.805, “Study on Minimization of drive-tests in Next Generation Networks”, Version 9.0.0, December 2009.

[3] 3GPP TR 36.902, “Self-configuring and self-optimizing network use cases and solutions”, Version 9.0.0, September 2009.

[4] 3GPP TS 32.522, “Self-Organizing Networks (SON) Policy Network Resource Model (NRM) Integration Reference Point (IRP); Information Service (IS) (Release 9)”, Version 9.0.0, March 2010.

[5] L.C. Schmelz, J.L. van den Berg, R. Litjens, A. Eisenblätter, M. Amirijoo, O. Linnell, C. Blondia, T. Kürner, N. Scully, and J. Oszmianski, “Self-configuration, -optimisation and -healing in wireless networks”, Wireless World Research Forum Meeting 20, Ottawa, Canada, April 22-24, 2008.

[6] J.L. van den Berg, R. Litjens, A. Eisenblätter, M. Amirijoo, O. Linnell, C. Blondia, T. Kürner, N. Scully, J. Oszmianski, and L.C. Schmelz,

“Self-organisation in future mobile communication networks”, ICT - Mobile Summit 2008, Stockholm, Sweden, June 10-12, 2008.

[7] S. Ahonen, J. Latheenmaki, H. Leitinen, and S. Horsmanheimo, “Usage of mobile location techniques for UMTS network planning in urban environment”, IST Mobile & Wireless Telecommunications Summit, 2002.

[8] S. Horsmanheimo, H. Jormakka, and J. Lähteenmäki, “Location-Aided Planning in Mobile Network - Trial Results”, Wireless Personal Communications, Vol. 30, pp. 207-216, Kluwer Academic Publishers, 2004.

[9] S. Sharma and A Nix, “Dynamic W-CDMA network planning using mobile location”, Proc. IEEE 56th Vehicular Technology Conference (VTC 2002-Fall), Vol. 2, No. 2, pp. 1182-1186, 2002.

[10] 3GPP TS36.305, “Universal Terrestrial Radio Access (E-UTRA); Stage 2 functional specification of User Equipment (UE) positioning in E-UTRAN (Release 9)”, Version 9.2.0, March 2010.

[11] A. Catovic and J. F. M. M. K. Dills, “Apparatus and method for generating performance measurements in wireless networks”, Patent US 2009/0310501, December 2009.

[12] C. Brunner and D. Flore, “Generation of Pathloss and Interference Maps as SON Enabler in Deployed UMTS Networks”, Proc. IEEE 69th Vehicular Technology Conference (VTC 2009-Spring), pp. 1-5, 2009.

[13] A. Erik and S. Holm, “Tuning of Empirical Radio Propagation Models Effect of Location Accuracy”, Wireless Personal Communications, Vol. 30, pp. 267-281, Kluwer Academic Publishers, 2004.

[14] J. G. Clancy, “Clutter database enhancement methodology”, Patent US 6,580,911 B1, June 2003.

[15] http://sumo.sourceforge.net.

[16] http://www.openstreetmap.org.

[17] “Review of use cases and framework II”, SOCRATES Deliverable D2.6, December 2009, available at http://www.fp7-socrates.eu.

[18] “FCC 99-245: Third report and order”, Federal Communications Commission, http://www.fcc.gov, Technical Report, October 1999.

[19] J. Medbo, I. Siomina, A. Kangas, and J. Furuskog, “Propagation channel impact on LTE positioning accuracy: A study based on real measurements of observed time difference of arrival”, Proc. IEEE 20th Int. Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC ‘09), pp. 2213-2217, September 13-16, 2009.

[20] C. Fritsche and A. Klein, “Cramér-Rao Lower Bounds for Hybrid Localization of Mobile Terminals”, Proc. 5th Workshop on Positioning, Navigation and Communication (WPNC ‘08), pp. 157-164, March 2008.

[21] 3GPP R1-092832, “Simulation results for LTE positioning accuracy”, June 2009.

[22] 3GPP TS36.133, “Evolved Universal Terrestrial Radio Access (E-UTRA); Requirements for support of radio resource management (Release 9)”, Version 9.3.0, March 2010.

[23] M. Modsching, R. Kramer, and K. ten Hagen, “Field trial on GPS Accuracy in a medium size city: The influence of built-up”, Proc. 3rd Workshop on Positioning, Navigation and Communication (WPNC ‘06), pp. 209-218, March 2006.

[24] SOCRATES web page, http://www.fp7-socrates.eu.

(a) (b) (c)

Figure 3. X-maps based on approach 1 using (a) GPS, (b) OTDOA, and (c) a combination of GPS and OTDOA as positioning method