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Using of GSM and Wi-Fi Signals for IndoorPositioning Based on Fingerprinting Algorithms

Juraj MACHAJ, Peter BRIDA

Department of Telecommunications and Multimedia, Faculty of Electrical Engineering, University of Zilina,Univerzitna 1, 010 26 Zilina, Slovak Republic

juraj.machaj@fel.uniza.sk, peter.brida@fel.uniza.sk

DOI: 10.15598/aeee.v13i3.1241

Abstract. In the paper framework for indoor posi-tioning utilizing Wi-Fi and GSM signals is introduced.Nowadays, indoor positioning is a very attractive topicfor researchers, since accurate and reliable position-ing system can unlock new market to service providers.In this paper we will analyse the use of Wi-Fi and GSMsignals and their combination for the fingerprintingbased positioning in the indoor environment. Perfor-mance of positioning system in terms of accuracy wasanalysed using simulations. In the simulations the po-sition of the mobile device was estimated in three ways,when only GSM signals were used, when only Wi-Fisignals were utilized and when a combination of bothsignals was used. Three positioning algorithms fromthe Nearest Neighbour (NN) family were used in thesimulations. Simulations were performed in the simu-lation model created in MATLAB environment.

Keywords

GSM, indoor localization, positioning, simula-tion, Wi-Fi.

1. Introduction

In the recent years research in the area of po-sitioning systems becomes an interesting topic.This is caused by two factors-development of lo-cation based services [1], [2] and [3] and sig-nificantly increased use of smart devices. Mostof the smart devices are capable to estimate theposition of the mobile user in an outdoor environmentusing at least one of Global Navigation Satellite Sys-tems (GNSS), generally GPS. However GNSS systemsare not developed to perform well in an indoor envi-

ronment [4] and [5]. Thus many research teams workon indoor positioning using radio networks.

Most of research teams working on the topic ofindoor positioning using radio networks deals withWi-Fi signals [6]. Main advantage of this approach isthat Wi-Fi networks are used in almost every buildingand most of the smart devices are equipped with Wi-Fitransmitter. However using Wi-Fi signals for position-ing also have drawbacks. Positioning systems based onWi-Fi can be easily influenced by adding new AccessPoints (APs) into the area or by altering APs settingsor adding new APs with the same MAC address andSSID [7] etc.

In the recent paper [8], we focused on indoor posi-tioning system based on GSM signals since GSM signalshould be more stable and should not be so easily af-fected by attacks, when compared to Wi-Fi signals. Onthe other hand, GSM signals have lower difference be-tween minimum and maximum RSS (Received SignalStrength) values in the area compared to Wi-Fi, dueto the fact that GSM Base Transceiver Stations (BTS)are further away and the signal is less attenuated dueto use of lower frequency band. This may lead to thehigher localization errors.

In this paper we will investigate possibility to usecombination of Wi-Fi and GSM signals for localizationbased on fingerprinting framework. In the proposed so-lution the signals from GSM should improve position-ing accuracy of Wi-Fi based system by adding someextra information about the environment. Our goalis to develop modular positioning system which couldbe used in various environments and can outperformpreviously developed WiFiLOC [9] positioning system.

In the paper results from simulations performed tocompare basic Wi-Fi and GSM based positioning withthe system that utilizes signals from the both net-works will be presented. Achieved results will be com-pared based on statistical analysis of achieved local-

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ization error. Traditional deterministic fingerprint-ing algorithms will be used in simulations to estimatethe position of the mobile device using Wi-Fi, GSMand signals from both networks.

The rest of the paper will be organized as follows;in the Section 2. fingerprinting localization frame-work will be shown, simulation model and simulationscenario will be introduced in Section 3. , in Section4. achieved results will be shown and discussed andSection 5. will conclude the paper.

2. Fingerprinting LocalizationFramework

Fingerprinting localization framework is widely used inindoor positioning systems based on radio networks.Main advantage of the fingerprinting approach isthe fact that it seems to be immune to the multipathpropagation [10], which is significant in the indoor en-vironment.

Function of fingerprinting localization algorithmscan be divided into two phases [11], in the first phasecalibration measurements are performed over the lo-calization area and radio map is created. During thesecond phase position of mobile device is estimated,using one of localization algorithms and calibrationdata stored in the radio map.

2.1. Radio Map

Radio map is created during the calibration or off-linephase. Radio map is represented by the database ofReference Points (RPs) together with RSS measure-ments collected during the calibration process [12].

Firstly area where position of mobile device will beestimated is divided into small cells. Each cell is repre-sented by one reference point. At each reference pointRSS measurements are performed and collected dataare then stored in the database, as follows:

Pj = (Nj , pj , αji, θj) j = 1, 2, ...,M, (1)

where Pj represents fingerprint at j-th reference point,M is number of reference points, Nj represent iden-tification of the reference point, pj represents posi-tion of reference point and consists of its coordinates,αji is the vector of the RSS measured at j-th refer-ence point from i-th transmitter, and θj can obtainany additional information needed during the positionestimation process.

2.2. Deterministic Algorithms

In the deterministic algorithms, the state P is assumedto be a non-random vector. This means that the RSSmeasurements at each reference points are not random,but depends on the position of reference point. Mainobjective of the localization algorithm is to computethe estimate x̂, which is combination of reference pointcoordinates pj , stored in the radio map [13], given by:

x̂ =

M∑j=1

ωj · pj

M∑i=1

ωi

, (2)

where ωi and ωj represents nonnegative weights, whichare computed as inversed value of Euclidean distancebetween RSS vector measured during positioning andRSS vector stored in the database for i-th referencepoint [14].

The estimator Eq. (2), which keeps the K highestweights and sets the others to zero is called the WKNN(Weighted K-Nearest Neighbor Method) [15]. WKNNwith K highest weights ωi = 1 is called the KNN(K-Nearest Neighbor) method [16]. The simplestmethod, whereK = 1, is called the NN (Nearest Neigh-bor) method [17].

KNN and the WKNN can perform better than theNN method, particularly with parameter values K = 3and K = 4 [18]. On the other hand NN algorithms canoutperform KNN and WKNN algorithms when radiomap density is high.

3. Simulation Model andScenario

This section will provide detailed information aboutsimulation model created in MATLAB environmentand simulation scenario, which was used to analyzefingerprinting positioning utilizing GSM and Wi-Fi sig-nals.

3.1. Simulation Model

Simulation model used for testing of GSM-based fin-gerprinting localization was developed as modificationof previously introduced simulation model for indoorpositioning using Wi-Fi signals [19].

In the model the signal propagation is modeled us-ing Multi Wall and Floor (MWF) propagation model[20]. The MWF propagation model is based on the as-sumption that attenuation of the same type of wall is

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decreased with the number of signal transitions thruthe wall of given type. In the model the mean attenua-tion value LMWF in distance between transmitter andreceiver d can be calculated using:

LMWF = L0 + 10n log

(d

d0

)+

I∑i=1

Kwi∑k=1

Lwik +

J∑j=1

Kfj∑k=1

Lfjk,(3)

where L0 is path loss at distance d0 = 1 m in dB (givenby Friis transmission equation), n is power decay index,I is number of walls types, Kwi is number of traversedwalls of category i, Lwik is attenuation due to wall typei and k-th traversed wall in dB, J stands for numberof floor types, Kfj is number of traversed floors of cat-egory j and Lfjk represents attenuation due to floortype j and k-th traversed floor in dB. Parameters ofMWF model were chosen based on analysis providedin [20].

The second propagation model was implemented forGSM signals and BTS positions were defined in thesimulation model. In the simulation model we assumed7 BTS in the area. We assumed ideal signal propaga-tion for each BTS, thus area covered by each BTS isassumed to be hexagonal. Radius of each hexagon is1 km thus the distance between 2 BTS is 2 km. Posi-tions of BTS in simulation model are depicted in theFig. 1.

Fig. 1: Positions of GSM BTS in the simulation model.

We have implemented AMATA radio propagationmodel to evaluate path loss of GSM signals. AM-ATA propagation model is modification of ITU prop-agation model and allows calculating of signal propa-gation losses in indoor environment [21]. Propagationloss L is given as:

L = 20 log10 f + 10n0 log10 d+ Lout +Xa + LF , (4)

where f represents frequency of the GSM signal inMHz, n0 is path loss exponent after isolation of theinternal wall effect, d is distance from the transmitterin m, Lout is attenuation loss factor of outer wall and

LF represents (multi)floor attenuation. Xa is the multiinternal wall attenuation loss factor, which is given by:

Xa = 0.0075H4 − 0.18H3 + 1.1H2 + 2.9H, (5)

where H is the number of separated walls of standardbrick type. Equation above applies for up to 11 indoorwalls in the measurement process. It is also importantto notice that signal attenuations after penetrating of8 walls become relatively weak to measure.

In the simulation model random variables are addedto the computed propagation losses to simulate signalfluctuations presented in the real world environment.These fluctuations were simulated by the random vari-able with lognormal distribution with 0 mean and stan-dard deviation of 5 dB. We decided to use lognormaldistribution based on previous results, in which highcorrelation between real world and simulation resultswas proved.

3.2. Simulation Scenario

Simulation scenario was proposed to evaluate impactof different wireless technologies and their combinationon the performance of the fingerprinting based posi-tioning system from the accuracy point of view. Radiomap was created by generating of 20 RSS samples fromeach transmitter in the communication range at eachreference point. Before the fingerprints were stored inthe radio map, RSS samples were averaged in order toreduce fluctuations of RSS signals.

In the simulations we assumed only 2D environment,e.g. one floor building, therefore attenuations causedby floors in propagation models Eq. (3) and Eq. (4)can be neglected.

Simulations were performed with 10000 independentposition estimation trials. In each trial, position of mo-bile device was randomly (uniform distribution) chosenfrom the localization area. Localization area with ref-erence points separated by 2 m can be seen in Fig. 2.

Fig. 2: Localization area.

In the Fig. 2, the blue lines represent walls of thebuilding and black dots represent positions of the ref-erence points in the area. Positions of APs used insimulations are represented by red squares. The area

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was covered by signals from 9 Wi-Fi APs placed insidethe building and by signals from 7 GSM base stationsplaced outside the building (as can be seen in Fig. 1and Fig. 2).

All RSS measurements were affected by random vari-able with lognormal distribution to simulate real worldfluctuation of RSS, which can be caused by shadowingand multipath propagation. In the simulation model20 samples of RSS from all transmitters (Wi-Fi andGSM) was measured during each positioning and aver-age RSS value was computed to reduce impact of RSSfluctuation.

During each trial, position was estimated using allthree algorithms described in the previous section -NN, KNN and WKNN. Position was estimated forthree cases, in the first case only Wi-Fi measurementswere used, in the second case only GSM measurementswere used and in the third one both measurements wereused to estimate position of the mobile device. TheKNN and WKNN algorithms K was set to 3 and 4 re-spectively, in case that measurements form Wi-Fi andcombination of Wi-Fi and GSM was used for the posi-tion estimation. In the case when only GSM measure-ments were utilized K was set to 7 for both KNN andWKNN algorithms. Based on previous results thesesettings should provide the most accurate results [8]and [22].

4. Simulation Results

In this section results achieved in simulations will beshown and discussed. Firstly, comparison of impact ofused radio signals on algorithm performance was inves-tigated. Results achieved for NN algorithm are shownin Fig. 3. In the figure the CDF of achieved localizationerror is depicted.

Fig. 3: Localization error of NN algorithm.

From the figure it can be seen that the highest ac-curacy was achieved by the combination of Wi-Fi andGSM signals. It can also be seen that GSM based lo-calization achieved similar results to the other cases in

50 % of position estimates, however the other 50 % ofposition estimates was affected by extremely high error.In the Fig. 4, the CDF of positioning errors achievedby KNN algorithm is shown.

Fig. 4: Localization error of KNN algorithm.

From the figure it can be seen that, similarly to NNalgorithm, combination of Wi-Fi and GSM signals im-proved accuracy of the position estimates of KNN al-gorithm. From the figure it is clear that GSM basedlocalization has significantly lower accuracy in 60 %of estimates. CDF of localization error achieved byWKNN algorithm is depicted in Fig. 5.

Fig. 5: Localization error of WKNN algorithm.

From the figure above, it is clear that, similarly toNN and KNN algorithms, achieved errors were signifi-cantly lower when Wi-Fi signals were used. Moreover,it can be seen that accuracy of WKNN algorithm wasimproved by use of both Wi-Fi and GSM signals in theprocess of position estimation.

From the Fig. 4 and Fig. 5 it can be seen that po-sitioning accuracy of WKNN algorithm is almost thesame compared to KNN algorithm. This is given bysmall differences in weights used in WKNN algorithm,which were calculated from Euclidean distance betweenRSS samples. Therefore, there cannot be high differ-ence in the performance of these algorithms.

Moreover, from the presented figures it can beseen that positioning accuracy achieved by KNN and

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WKNN algorithms is significantly higher when com-pared to the one achieved using NN algorithm. This isgiven by the fact that NN algorithm gives additionallocalization error, since it estimates position only asa position reference point with the highest weight.On the other hand, in both KNN and WKNN algo-rithms the positions of reference points are averaged,thus position can be estimated anywhere in the area.

In the Tab. 1 comparison of statistical measuresof achieved localization errors for all three algorithmswith utilization of Wi-Fi and GSM signals combinationis shown. From the previous results it is clear that com-bination of Wi-Fi and GSM signals allows to achievehigher accuracy of position estimates, thus results forseparate Wi-Fi and GSM based localizations are notshown. From the results in the table it can be seen

Tab. 1: Localization error of algorithms when both Wi-Fi andGSM signals were used.

Localization error [M]Mean Median STD 95 %

NN 3.3 2.81 2.27 8.23KNN 2.18 2.01 1.22 4.64

WKNN 2.21 2.02 1.26 4.79

that KNN and WKNN algorithms achieved very simi-lar results, and are able to significantly outperform NNalgorithm. It can be seen that average localization er-ror was for more than 1m lower when KNN andWKNNalgorithms were used. When we take a look at 95 %error, the error achieved by NN algorithm is two timeshigher compared to KNN and WKNN algorithms.

For better analysis of the achieved results, CDF oflocalization error achieved by the three algorithms isdepicted at Fig. 6.

Fig. 6: Comparison of algorithms when both Wi-Fi and GSMsignals were used.

From the results in the figure it can be seen thatKNN and WKNN algorithms performed in the sameway. The only difference between these two algorithmsis only in the 10 % of highest localization errors, whereKNN algorithm outperformed WKNN algorithm,by reducing error of highest outliers. This may be given

by the fact that the reference point with highest weighthas relatively high error. This fact can be observed onthe performance of NN algorithm, since this algorithmuses only reference point with the highest weight.

5. Conclusion and FutureWork

In the paper indoor fingerprinting localization systemutilizing combination of Wi-Fi and GSM signals was in-troduced and tested using simulations. Proposed local-ization system uses deterministic algorithms NN, KNNand WKNN to estimate position of mobile device inthe localization area.

The impact of the radio signals used to estimate posi-tion of mobile device, on the localization accuracy wastested in simulations. Simulation model was created inMATLAB environment, as modification of previouslyused simulation model for Wi-Fi based positioning [19].

From the achieved results it is clear that combinationof Wi-Fi and GSM signals can outperform positioningusing only one type of these radio signals. It is im-portant to note that GSM based positioning does notachieve performance of Wi-Fi based positioning. How-ever from the results it is clear that additional RSSsamples from GSM network can help to improve posi-tioning accuracy of Wi-Fi based positioning.

From the analysis of results achieved by different al-gorithms in combination with both Wi-Fi and GSMsignals it can be stated that NN algorithm was outper-formed by both KNN and WKNN algorithms. Meanlocalization error achieved by NN was 50 % highercompared to KNN and WKNN algorithms. In addi-tion 95 % error of NN algorithm was who times highercompared to KNN and WKNN algorithms. This ispartially given by the nature of the used algorithms.

In the future we will implement proposed local-ization framework into the modular localization sys-tem [23] and test its performance in the real worldenvironment. Real world experiments will be per-formed in order to analyse conditions where standalone GSM or Wi-Fi positioning can be outper-formed by use of signals from both systems. Mod-ular positioning system should perform in all kindsof environment-indoor, outdoor and dense urban,and always choose the most appropriate localizationmodule.

Acknowledgment

This work was partially supported by the grantLPP-0126-09 of Slovak Research and Development

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Agency, by the Slovak VEGA grant agency, ProjectNo. 1/0394/13 and by Broker centre of air transportfor transfer of technology and knowledge into transportand transport infrastructure IMS 26220220156.

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About Authors

Juraj MACHAJ was born in Piestany, Slovakia,in 1985. He received the M.Sc. degree in telecom-munications and Ph.D. degree in mobile radiocommunications from the University of Zilina, Slo-vakia, in 2009 and 2012 respectively. His doctoralwork was in the area of the indoor mobile posi-tioning using radio networks. He currently works asa researcher at Department of Telecommunications andMultimedia at FEE, University of Zilina. His researchinterests include wireless networks, positioning us-ing wireless networks and intelligent transport systems.

Peter BRIDA was born in Bojnice, Slovakia,in 1979. He received the M.Sc. degree in telecom-munications and the Ph.D. degree in mobile radiocommunications from the University of Zilina, Slo-vakia, in 2002 and 2006, respectively. His doctoralwork was in the area of the mobile positioning. He iscurrently as Associate Professor at the Department ofTelecommunications and Multimedia at FEE(Facultyof Electrical Engineering), University of Zilina.His research interests include wireless positioning inheterogeneous wireless networks, satellite navigationsystems and intelligent transport systems.

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