Geolocation based on Measurements Reports fordeployed UMTS Wireless Networks

Tiago Silva PereirinhaInstituto Superior Tecnico

University of LisbonLisbon, Portugal

tiago.pereirinha@ist.utl.pt

Antonio RodriguesInstituto de Telecomunicacoes

Instituto Superior TecnicoUniversity of Lisbon

Lisbon, Portugalar@lx.it.pt

Pedro VieiraArea Departamental de Engenharia de Electronica

e Telecomunicacoes e de ComputadoresInstituto Superior de Engenharia de Lisboa

Lisbon, Portugalpvieira@deetc.isel.pt

AbstractAlthough geolocation of mobile phones on UMTSnetworks is possible, it usually entails high implementationexpenditure. In deployed networks, for purposes like trafficanalysis and network maintenance, drive-tests can be taken,albeit with associated operational costs as well. In this thesis, analternative geolocation method using Measurement Report Mes-sages (MRM) has been studied. Using real measurements from asingle User Equipment in Lisbon, a data-abstract algorithm wasdeveloped, which takes as input MRM and Node B information,performs indispensable initial calculations, estimates the positionusing a non-linear recursive least squares trilateration method,and outputs the results in several types of data. The resultswere tested using the corresponding drive-tests collected GPScoordinates, and a median positioning error of 272 meters ofwas achieved, using 32.3% of the MRMs. Certain parameterswere found to regulate the positioning error-MRM usabilityrate tradeoff, and general results were shown to be affected byvarious factors, such as multipath degradation, low data avail-ability, non-ideal synchronization, and limited number of cellsavailable. Nevertheless, results were promising and a way waspaved for further improvement using integrated motion model-based Kalman filtering. The algorithm was also left theoreticallyprepared to receive and analyze LTE MRMs when they eventuallybecome available. The thesis concludes with an analysis on thepositive impact of the algorithm and an outlook on the future ofgeolocation technology implementation on deployed UMTS andLTE networks.

I. INTRODUCTION

Mobile communications, since their appearance and firststeps as an industry, have rapidly risen and currently hold asignificant importance at social and financial levels. Togetherwith the growth of cellular systems and technologies however,an effort is being made to consistently improve the quality andquantity of services provided to the end user. Regarding thattheme, one of the problems that has sparked a noteworthyamount of research and development is the finding of ways toprovide a precise location of the User Equipments (UE). Formobile operators, UE location is very useful and important, forreasons such as network design and maintenance, traffic mapgeneration, handover algorithm improvement and accurate ra-dio propagation modeling, through Key Performance Indicator(KPI) analysis. For this purpose, drive-tests are taken, in whichUE measurements and geographical data is acquired simul-taneously, for posterior analysis. However, drive-tests entail

considerable operational costs. To circumvent this drawback,Measurement Report Messages (MRMs) generated by the UEsand reported to the network, have been studied as a way ofgeolocating the handset.

The main objective is to study and develop a completedata-abstract platform, based on the OTDOA (Observed TimeDifference of Arrival) algorithm, to geographically locate UserEquipments on already deployed UMTS networks, using realMRMs collected in Lisbon. The point is to achieve a solutionthat delivers position estimates reliable enough to be used onnetwork analysis and maintenance, among other applications,in order to ultimately reduce the quantity of drive-tests beingperformed, successfully suppressing a considerable portion ofthe associated operational costs. The concept has already beenstudied, albeit within different scopes or using different typesand variety of data [1], [2]. Also, all of the solutions areproprietary, and therefore not directly available for study.

This article is structured as follows: Section II covers thealgorithm structure from input loading, parsing and positioningto output generation. Section III explains how the resultingposition estimates were assessed in terms of accuracy, fol-lowed by an analysis of the results yielded. Finally, SectionIV summarizes the main conclusions from this study and givessome pointers regarding future work.

II. ALGORITHM STRUCTUREA. Input data

The input data necessary to calculate, within an acceptableerror margin, the estimates of the physical position of theUE is the Node B information and the MRMs. The Node Brepository, henceforth the cell dump, must contain the neces-sary data, which are the names, identification numbers (CellIDs), Primary Scrambling Codes (PSC), Primary CommonPilot Channel (P-CPICH) powers, geographic coordinates andantenna heights off all the cells of the network. As for theMRMs, each one must contain the event that triggered thereport, the Cell IDs of the cells in the active set, the PSCs ofthe cells in the active and monitored sets, and for each one ofthose radio links, the Ec/N0, Received Signal Code Power(RSCP), frame offset (OFF) and chip offset (Tm) parameters[3].

Although additional data can be used in order to furtheroptimize the algorithm, said data may not be provided, so onlythe utmost essential elements needed to calculate the positionare going to be taken into account at this level.

After all the data is successfully loaded and parsed, the stepsthat follow are repeated for each MRM, and each one generatesa set of coordinates for the position of the UE, assuming thatsaid MRM complies with all of the following requirements oftrilateration methods, which are used twice over the course ofthe algorithm:

a) The MRM provides measurements from at least threedifferent cells.

b) All three (or more, if available) of those cells must befrom different Node Bs (henceforth, sites), i.e., they mustnot share the same location.

c) All three (or more, if available) of those cells must be notbe located in a way that makes them co-linear in space.

B. Initial Calculations

Prior to the actual position estimation method, somecalculations and verifications are essential, mainly forunequivocally identifying sites and cells, eliminatingredundant measurements and calculating Observed TimeDifferences (OTDs) and a first estimate for the position.

1) Cell identification: Since of the cells in the monitoredset only the PSC is known, and since PSCs are only ranged[0, 512] (i.e., the PSC is not enough to uniquely identify thecell), to determine which cells are in fact communicating withthe UE, it is necessary to ascertain their Cell IDs. So, thecoordinates of the strongest (in terms of Ec/N0) cell in theactive set are used as reference, and for a PSC n being tested,all the cells in the cell dump with that PSC are checked, andthe cell with a PSC n which is closest to the reference isassumed to be the one linked to the UE. This proximity-basedapproach is effective provided the network design, in termsof PSC assignment, is acceptable. The distance between thereference and the tested cells is calculated using the Haversineformula.

2) Elimination of redundant measurements and Siteidentification: As mentioned before, since the positioningmethod is based on trilateration, measurements made by theUE to cells located in the same place are redundant, as twoequations between the same two locations and the UE arenot linearly independent. Therefore, all links within an MRMmust be tested, and for each redundant link pair is found, theone with the lowest measured RSCP is eliminated. At thispoint, a site repository is also created, to assist the algorithmin posterior phases.

3) First position estimation: The non-linear iterative RLSmethod used for position requires that an initial estimate forthe position is provided. Using the strongest cell position [1] orthe centroid of the triangle formed by the strongest three cellsare possible options, but since the accuracy of this estimate

is important to the convergence of the RLS method, the firstestimate is calculated using a more complex method, via amodified Okumura-Hata model and geometric trilateration.

The Okumura-Hata model [4] uses empirical equations toestimate the pathloss, on which one of the variables thedistance, among others. Thus, by applying a different equation,which uses the cells P-CPICH transmission power Tx and theMRMs reported RSCP to estimate the pathloss PL [5]

PL[dB] = Tx[dBm] RSCP[dBm] (1)

and by rearranging the Okumura-Hata equations, the dis-tance di from a cell i to the UE can be calculated by

di = 10

PL 69.55 26.16 log10 f + 13.82 log10 hB + CLC44.9 6.55 log10 hB

(2)where f is the transmission frequency, hB is the cells

antenna height, and CLC is the model-specific parameter forlarge cities.

Then, using the three strongest cells coordinates,(x, y)A,B,C , converted to the UTM system [6], and the threecalculated distances dA,B,C , the UTM coordinates for the firstposition estimation (x, y)UE can be obtained using geometrictrilateration:

(xUE xA)2 + (yUE yA)2 d2A = 0(xUE xB)2 + (yUE yB)2 d2B = 0(xUE xC)2 + (yUE yC)2 d2C = 0

(3)

The coordinates are obtained after rearranging the systemof equations and solving it by applying Cramers rule.

4) OTD calculation: Finally, the SFN-SFN Observed TimeDifference [3], in chips, for a connection between the UE andcell i, reported by the k-th MRM, using the frame offset OFF,which has a range of [0, 1, . . . , 255], and the chip offset Tm,which has a range of [0, 1, . . . , 38399], can be calculated with

OTDk(i) = 38400 OFF(i) + Tm(i) (4)

C. Positioning Cycle

Using the original measurements, cell information and addi-tional parameters defined on the sections above, the algorithmenters an iterative cycle, which produces, as a result, thefinal estimated coordinates of the UE position. The cycle iscomposed by three main steps: Estimate propagation delays of the connections between

the cells and the UE (using newly estimated coordinates), Calculate Relative Time Differences (RTDs), based on

OTDs and Propagation delays, Run a non-linear RLS algorithm, which generates new

coordinates.This iterative process is repeated a pre-defined number, K,

of times, or until the coordinates stabilize. Stabilization, inthis context, is assumed when coordinates do not change for

5 iterations of the cycle. The first iteration will use the firstposition estimate calculated above, and the ones after willuse the estimates produced by the previous one.

1) Propagation delay and RTD calculation: The first step iscalculating the propagation delays for every link of the MRM.In the k-th MRM, the propagation delay k(i), between theUE and a cell i, is given by

k(i) =1

c

(xc(i) xue)2 + (yc(i) yue)2 (5)

where c is the speed of light, and (x, y)c(i) and (x, y)ueare the coordinates of the positions of cell i and the UE,respectively.

Then, using the OTDs and the propagation delays, RTDsare calculated. On an MRM k, and RTD sample, between twocells belonging respectively to sites i and j, can be obtainedas follows:

RTDk(i, j) = OTDk(i)OTDk(j) (k(j) k(i)) (6)

RTDs, like OTDs, are in chips, which makes the conversionof k from seconds to chips necessary. In UMTS, the chip rateis 3.84 Mchips/s. Ergo, one chip in seconds corresponds tothe inverse of that figure, which is 0.26 s. Also, it has to betaken into account that RTDs have a range of [0, 1, . . . , 256384001]. So, incorporating those notions, the expression forRTD calculation becomes

RTDk(i, j) =(OTDk(i, j)

k(j, i)

0.26 106)

mod (25638400)(7)

Each MRM k analyzed measuring sites i and j will producean RTD sample, RTDk(i, j). These samples are saved in athree-dimensional matrix called RTD array. The first twodimensions of the matrix are the sites. Each position of thematrix is an array containing all the samples already calculatedfor the two sites. The matrix, conceptually, is strictly uppertriangular.

The RTD values used on the next step will not be thesamples calculated in this step for this k-th MRM, but actuallya modified median of all the samples already calculated,in MRMs [1, . . . , k], between the two sites in question. Asimple median is not suitable because there might be error-contamined measurements amongst the data. The solution isto attenuate any outliers that might appear on the distributionof values [1]. So, instead of calculating the median of the arrayRTD(i, j), the final value RTDk(i, j) between sites i and jis obtained as follows:

RTDk(i, j) = sgn(c(i, j)

)|c(i, j)|.p + RTD(i, j)

mod(256 38400)(8)

where

d(i, j) = RTD(i, j) RTD(i, j) (9)

and

c(i, j) = (d(i, j) + 128 38400) mod (256 38400)128 38400

(10)

RTD(i, j) is the median of RTD(i, j), and p is theexponent that defines the ratio of outliers eliminated. The dotbefore the parameters denotes element-wise exponentiation.RTDk(i, j) are calculated for every cell pair involved in

the usable measurements of the MRM.

2) Non-linear RLS trilateration: The final step takes allthe parameters above and uses them to perform a trilateration,producing the coordinates for the physical position of the UE.Said trilateration is executed as a non-linear RLS optimizationproblem, which follows an algorithm called Trust-Region-Reflective (TRR). This algorithm is a subspace trust-regionmethod and is based on the interior-reflective Newton methoddescribed in [7], [8]. Each iteration involves the approxi-mate solution of a large linear system using the method ofpreconditioned conjugate gradients. A system of equationsis constructed, and takes an initial estimate of the positionthen performs a number of iterations, each time applying acorrection to the previous value, reducing the sum of squaredresiduals of the different equations:

minx,yf(x, y)22 = min

x,y

(f1(x, y)

2+f2(x, y)2+. . .+fn(x, y)

2)

(11)f(x, y) is a system of n equations, ranged from f1(x, y)

to fn(x, y). The common unknown to all the equations, is theposition of the UE, (x, y)ue. Each equation uses measurementsfrom an MRM k reporting data from two cells/sites i and j:

fk,i,j(x, y) = rk,i,j dk,i,j(x, y) (12)

where

rk,i,j = c78(OTD(i)OTD(j)RTDk(i, j)

)(13)

and

dk,i,j(x, y) =

(xc(i) xue)2 + (yc(i) yue)2 (xc(j) xue)2 + (yc(j) yue)2

(14)

So, an MRM k containing n valid measurements fromthe UE to cells/sites (1, 2, . . . , n), ranked in order of signalstrength, will produce (n 1) equations:

fk(x, y) =

fk,1,2(x, y)fk,1,3(x, y)

...fk,1,n(x, y)

(15)Naturally, if the MRM reports 4 or more valid measure-

ments, the system is overdetermined. The reason why only

(n 1) equations are generated from n measurements is toattenuate multipath propagation effects - only RTDs involvingthe cell with strongest reported RSCP are used, even thoughRTDs for every site pair on the MRM are calculated.

Besides a new position estimation, supplementary informa-tion data is produced: the squared norm of the residual, RN ,

RN =

ni=1

f1(x, y)2 + f2(x, y)

2 + . . .+ fi(x, y)2 (16)

and the exit flag of the RLS/TRR algorithm.

D. Validity Criteria

After the end of the cycle, the algorithm is in possession ofthe final estimate for the UE position. However, not all of thepositions will be valid, since for a given MRM the RLS/TRRalgorithm might not converge to a stable mathematical so-lution, or the measurement data might be corrupted. So, thealgorithm will attribute a flag of validity to the MRM, brandingit valid or not valid according to the following verifications: A negative RLS/TRR exit flag means that the the method

was unsuccessful. In that case, it can be assumed that theresulting position estimation is compromised, and thus isnot valid.

If the positioning cycle reaches K iterations withoutstabilizing, it means that the cycle has been consistentlyproducing a different position for every iteration. Conse-quently, it has to be assumed that the position, whicheverit is, might not be valid.

Each MRM k analyzed measuring sites i and j willproduce an RTD sample, RTDk(i, j), which is thensaved in the three-dimensional matrix RTD array. Then,an RTD value, RTDk(i, j) is extracted from that arrayfor use in the RLS/TRR algorithm. RTD values betweenthe same sites, ideally, should be the equal for everyMRM, regardless of other factors. However, there mightbe MRMs that have error-contaminated values. So, thefinal validity verification is testing how much the k-th MRM RTD samples diverge from the RTD values.Given that difference RTD,

RTD = |RTDk(i, j)RTDk(i, j)| (17)

the UE position produced for that MRM is consideredinvalid if the following inequality is true, for a pre-definedmaximum error tolerance value RTD:

RTD RTD (18)

E. Output

The very final phase of the algorithm is generating output.Besides the UE position coordinates, the algorithm providesother information that can be used for post-processing analysisof the estimates and of the performance of the algorithm itself.The outputs generated are: text logs of every operation performed,

the raw data of the MRMs, including all values andstructures used to produce the positions estimates,

a CSV table, in which each row corresponds to a measure-ment, and contains the latitude and longitude coordinatesof the UE position, the active set size, and the RSCP andEc/N0 of the strongest measurements,

two Google Earth KML files, for visual reference of thedata, containing all the points yielded by the algorithm.In one file, the pins are color-coded according to the sizeof the active set, and on the other, according to the theirEc/N0 values.

III. ASSESSMENT AND RESULTS

A. Assessment method

To test the accuracy of the estimates, they were testedagainst GPS measurements acquired as the MRMs wererecorded, i.e., during a drive-test. For every MRM whichyielded a location, the algorithm uses its timestamp to findthe accompanying GPS position for comparison. However,due to delays and clock drifts, there might not be an exactmatch between the MRM timestamp and a GPS one. Thus, aweighted linear interpolation is used to find the best value forthe coordinates, and only then is the positioning error betweenthe estimates and the real positions, using the Haversineformula.

After all the MRMs have gone through this comparison,many important indicators can be calculated and generated,including error median values and cumulative distributionfunction graphs.

B. Results analysis

Only MRMs with three or more usable measurements wereconsidered, since it is not possible to perform trilateration withonly one or two beacons. Table I explicits how many MRMswere analyzed and, of those, how many produced positionsconsidered valid as per the criteria of Section II-D. It canbe easily observed that the fact that only three or more cellsMRMs are usable quickly eliminates a very large numberof MRMs, which is a significant weakness of any OTDOA-based algorithm. From the remaining MRMs, even though allof them produce position estimates, an additional amount arerendered invalid by the validity criteria, but it is important tonote that there is an implied trade-off between the number ofvalid estimates and the widening or narrowing of the validitycriteria - narrower criteria yield less estimates, but, in theory,closer to the actual position of the UE.

Figure 1 shows the Cumulative Distribution Function of thepositioning error. The error correction median obtained was272.62 meters, which falls within the accuracy ranges for aOTDOA solution in urban environments, [50 300] meters(specific range for urban environments) [9]. It can also beseen that, under the same conditions (no RTD values knowna priori and no implemented Kalman Filter), the algorithmperforms similarly to that of [1].

Even though the results are acceptable for the input dataprovided (only cell and MRM data as opposed to cell, MRM,

TABLE IMRM ANALYZABILITY AND VALIDITY CRITERIA-COMPLIANT VALUES

AND PERCENTAGES

Total MRMs 1123MRMs w/ 2 measurements 630MRMs w/ 3 measurements 493Analyzable MRMs [%] 43.900Valid estimate MRMs 159Invalid estimate 334Valid MRMs [%] 32.252

Fig. 1. Cumulative Distribution Function of the positioning error

U-RNTI, RTT and elevation data), the fact that it does not yieldpositioning estimates with even smaller positioning errors canbe attributed to a number of factors:

The data was collected in Lisbon, which is a city withirregular terrain, with little to no spatial planning andheterogeneous architecture, all traits which exacerbate theeffects of multipath.

OTDOA-type algorithms perform worse on urban envi-ronments [9].

Due to trilateration performance requirements, two-or-less-cell-MRMs are of no use in this context, whichrenders more than half of the reports useless straight fromthe onset.

Finding the cells by PSC proximity analysis is not infal-lible - there is always the possibility of the closest cellwith a given PSC not being the cell on the measurementwith that PSC. That probability grows proportionally tothe density of cells in the area.

All calculations throughout the algorithm are performedon 2D, as no elevation data was provided.

Many calculations are made on the course of the algo-rithm, most of which involve approximations.

The method used in the assessment, might introduceerrors when generating the error measurements.

OTD (and, consequently, RTDs) have resolutions of onechip, or rather, 78 meters, which is a considerable dis-tance.

Since MRMs are based on handover events, due totheir nature there will be bursts of MRMs reporting thesame cells. If those measurements all generate wrongpositions, there will be a big impact on general algorithmperformance.

The final algorithm generated all outputs successfully. Fig-ure 2 provides a screenshot of Google Earth loaded with oneof the output KML files.

Fig. 2. Screenshot of Google Earth UE running the Ec/N0 KML outputfile.

IV. CONCLUSION

The developed algorithm proves to be a very promisingstarting point to a fully usable post-deployment low-costlocation solution, leaving room for improvement in order toultimately achieve a tool that eliminates almost completelythe need to perform most drive-tests. Key additions to beconsidered include:

Using elevation data would be useful to modify thealgorithm to work in 3D.

The development, and subsequent integration, of a motionmodel based-Kalman filter, which would be used tofurther improve the estimated positions by using a locallylinear prediction model of successive positions [10], [11],to provide a better control over non-convergent positionestimates generated by the algorithm.

Given a powerful enough machine, the algorithm can beeasily modified to run various traces at once, and in real-time.

If LTE MRMs are provided, they can be used to studymodifications to the algorithm in order to make it LTE-compatible.

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