Aymen et al.

RESEARCH

A New Alternative for Traffic HotspotLocalization in Wireless Networks Using O&MMetricsJaziri Aymen1,2*, Nasri Ridha1 and Chahed Tijani2

Abstract

In recent years, there has been an increasingawareness to traffic localization techniques drivenby the problematic of hotspot offloading solutions,the emergence of heterogeneous networks (HetNet)with small cells’ deployment and the greennetworks. The localization of traffic hotspots witha high accuracy is indeed of great interest to knowhow the congested zones can be offloaded, wheresmall cells should be deployed and how they can bemanaged for sleep mode concept. We propose, inthis paper, a new hotspot localization techniquebased on the direct exploitation of five KeyPerformance Indicators (KPIs) extracted from theOperation and Maintenance (O&M) database ofthe network. These KPIs are the Timing Advance(TA), the angle of arrival (AoA), the neighboringcell level, the load time and two mean throughputs:arithmetic (AMT) and harmonic (HMT). Thecombined use of these KPIs, projected over acoverage map, yields a promising localizationprecision and can be further optimized byexploiting commercial data on potential hotspots.This solution can be implemented in the networkat an appreciable low cost when compared withwidely used probing methods.

Keywords: Wireless cellular networks; Traffichotspot localization; O&M key performanceindicators; HetNets; Coverage map; Potentialtraffic hotspots; Optimization

*Correspondence: aymen.jaziri@orange.com1Orange Labs, 38/40 avenue General Leclerc, 92794 Issy-les-Moulineaux,

France2Telecom SudParis, 9 street Charles Fourier, 91011 Evry, France

Full list of author information is available at the end of the article

1 IntroductionLocalization of traffic hotspots is often one of thefirst steps in network planning and optimization, es-pecially in the context of newly proposed technologieswithin 3GPP standards such as Heterogeneous Net-works (HetNets) [2]. HetNets are composed of smallcells which are to be deployed, in addition to macrocells, in areas of capacity bottlenecks representing typ-ical hotspots. And so, the efficiency of the deployedsolution to absorb traffic in the congested zone obvi-ously depends on the accuracy of the localization ofthe traffic hotspot zones. Several traffic localizationtechniques have been proposed in 3GPP Long TermEvolution (LTE) systems. They are mostly based onprobing and include trace analysis and protocol decod-ing [3–5]. The most important information extractedfrom traces is the received power level and the tim-ing advance. Authors in [3] provided a test transmit-ter which plays the role of a neighboring cell at firstand then the role of a serving Base Station (BS). Testsare realized within the existing cells in the network inorder to assess the traffic density within the vicinityof the transmitter means. This solution needs to con-figure the test transmitter and realize measurementsin each area separately. This method may take a longtime to assess the traffic distribution in the entire zonecovered by a BS. In the same context, patent [4] dis-closed a method where the User Equipments (UEs)send periodically a report of radio measurements fromthe serving cell and the neighboring cells. A record-ing unit post is installed at the interface between theBS and the studied Base Station Controller (BSC),termed A-bis, to examine the messages exchanged onthis interface. Based on these measurements, the traf-fic distribution is calculated. It was shown that theprecision using this method does not fit with the smallcell dimensions. Another alternative method was pre-sented in patent [5] and treated traffic hotspot local-ization in GSM using statistical analysis of the timingadvance and neighboring cell measurements extractedfrom traces. The accuracy of this method is improvedas compared to the previous ones but it is still insuffi-cient because it only localizes the number of UEs and

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omit the data volume. Furthermore, proximity locationis another method of localization based on the detec-tion of close Wi-Fi Access Points (APs). This helps tocalculate the position of the UE knowing that of theAP [6,9]. RF-Fingerprinting [7,9], cell-ID (LTE Rel 8)and A-GPS (LTE Rel 9) [9] are also well known tech-niques for locating individual UEs with different accu-racy levels. However, these individual locating meth-ods are quite complex (in the generation of a spatialtraffic distribution) because it involves a large numberof UEs, from which information about hotspot trafficdistribution is extracted.In sum, the probing-based methods suffer from severalshortcomings: First, not all the traces are capturedby probes, some of them are lost. Second, they re-quire high capacity storage servers that can not be pro-vided by existing Operation and Maintenance (O&M)databases. Third, based on recorded traces, the posi-tion of each UE is calculated separately leading thusto a heavy localization process. Eventually, the toolsneeded for probing, storage and processing are costly.

Another family of localization methods makes useof Key Performance Indicators (KPIs) in order to in-fer the level of traffic in each area of the cell. In [8],authors proposed to divide each cell of the networkinto several subareas, and to calculate a traffic valuefor each subarea from O&M measurements in the net-work. The indicators used to compute traffic values arethe load of the cell, the call attempts and the numberof handovers. This method is very simple and doesnot require additional tests or equipment. However,it is limited in high data rate networks, such as 4Gsystems, because it only localizes traffic hotspots interms of number of UEs. The idea of [8] can be furtherimproved considering other KPIs. Besides, the trafficlocalization, in the way described in the paper, is basedon the definition of an optimization problem where thenumber of variables to find is the number of subareas.In this case, the method has a significant computa-tional cost mainly for a precision going to small celldimensions.

In order to improve the method used in [8] withfast computations, we propose, in this paper, a newmethod for hotspot localization based on the combineduse of several KPIs directly obtained from the O&M.These KPIs are: Timing Advance (TA), Angle of Ar-rival (AoA), neighboring cell level, load time and twomean throughputs: arithmetic (AMT) and harmonic(HMT). Simulation results show that the proposedmethod in this paper achieves acceptable localizationalong with sufficient precision and significant savingson the cost of localization, as compared to probing-based techniques.

The main contributions of this paper are threefold.The first main contribution is the use of AoA, the cor-relation between the neighboring cells’ traffic loads andthe difference between the AMT and HMT, in addi-tion to the traditionally used TA and neighboring celllevel and the projection of the information extractedfrom these KPIs over the coverage map. The secondnovelty of this paper is the definition of a global met-ric combining all the five, previously-cited KPIs. Thismetric is further optimized using additional informa-tion about potential hotspot zones obtained from com-mercial data. Third, smoothing the estimated map isan additional step proposed in this paper in order tomake the estimated traffic distribution more precise.

The remainder of this paper is organized as follows.A general description of the process of traffic localiza-tion is provided and the key motivations behind theuse of the KPIs are given in Section II. Then, the threemain inputs of the hotspot localization algorithm aredetailed in section III. In Section IV, we present thehotspot localization algorithm and its optimization.Section V contains simulations and corresponding re-sults. Sections VI eventually concludes the paper.

2 Traffic localization method: Process andkey motivations for using KPIs

The proposed algorithm of hotspot localization is de-picted in Fig. 1. Five KPIs define the first main inputsof the algorithm: TA, AoA, neighboring cell level, cor-relation between traffic loads and the difference be-tween the AMT and HMT. Extracting informationfrom these 5 KPIs and projecting them over the cov-erage map, as will be explained later in Section 4, en-ables us to obtain the traffic level in each pixel[1] ofthe coverage map.

The 5 KPIs are properly chosen with respecting twoimportant criteria: locating hotspots in terms of num-ber of connected UEs and traffic volume. In fact, TA,AoA and neighboring cell level can be sufficient forcalculating the spatial distribution of connected UEs.However, in practice, a hotspot is measured by intro-ducing not only the high density of connected UEsbut also the traffic volume. Actually, this latter doesnot follow exactly the same evolution as the numberof connected UEs. Therefore, the traffic hotspot lo-calization is improved by adding directly data-volumerelated KPIs such as the correlation between the cells’traffic load and the difference between the AMT andHMT.

[1]The coverage map of the network is divided intosmall areas called pixels. The size of each pixel definesthe resolution of the coverage map (often between 25to 50 meters in practice).

Aymen et al. Page 3 of 12

Figure 1: General process of hotspot localization.

The importance of each KPI is measured and an im-portance factor is assigned to it in order to avoid theover-confidence in the localization of traffic hotspotsdue to the correlation between KPIs. Optimal impor-tance factors are found by solving a least square prob-lem minimizing the distance between the estimatedmap and the map of potential hotspots. The map ofpotential hotspots represents additional informationabout potential hotspot zones obtained from commer-cial data. Then, we define a global metric combining allthe five, previously-cited KPIs and using the optimalimportance factors. The localization of traffic hotspotzones can give significant weights to isolated areas, orpixels, as well as pixels in the edge of a hotspot. Andso, smoothing the estimated map is an additional stepproposed in this paper in order to make the estimatedtraffic distribution more accurate.

In this paper, we are mainly interested on the com-bination of the O&M KPIs, projecting them over thecoverage map and smoothing the estimated distribu-tion. However, the map of potential hotspots does notplay a crucial role in this method of hotspot local-ization. In fact, it is possible to manually tune theimportance factors until having a good accuracy of lo-calization. Whereas, a possible additional step to thelocalization algorithm is expected to provide better re-sults. This step is simply projecting O&M KPIs overan available map of potential hotspots.

It is possible to add other KPIs in the algorithm oftraffic localization. But, in such cases, it is essential

to properly combine the KPIs in order to avoid theover-confidence due to correlated KPIs.

3 INPUTS FOR TRAFFICLOCALIZATION

3.1 Coverage map: Definition and notation

A coverage map is a map of a bounded geographicalarea where the signal level from each cell is given ineach pixel. The coverage map is often taken from cover-age prediction tools or from real measurements such ascoverage provided by the Minimization of Drive Tests(MDT) techniques [10]. In this paper, the coveragemap is denoted by L and constitutes a bounded partof R2. Without loss of generality, we assume that Lhas the form of a square and is discretized into equallym2 pixels.

For 1 ≤ i, j ≤ m, we designate by Pi,j the coordi-nates of each pixel in L and we assume that the pixelP0,0 is located at the lower left corner of the map (theorigin of R2).

We denote by C = {C1, C2, ..., Cn} the set of thecells located in the coverage map L, where Ck is thegeographical area covered by cell k and n is the numberof cells. It is clear that C ⊆ L because some pixels arenot covered by any cell.

Let RSRPk,i,j be the Reference Signal ReceivedPower (or its equivalent the RSCP in WCDMA andRxlev in GSM networks) [11] from cell k in pixel Pi,j ,

Aymen et al. Page 4 of 12

Figure 2: Best server map in the covered area.

then the cell coverage Ck is given by

For 1 ≤ k ≤ n, Ck =

{Pi,j ∈ L such that RSRPk,i,j = max1≤l≤n

RSRPl,i,j}(1)

For every pixel Pi,j in L, we denote by c∗i,j and ci,j re-spectively the index of the first and second best servingcell.

c∗i,j = arg max1≤l≤n

RSRPl,i,j (2)

ci,j = arg max1≤l≤nl 6=c∗i,j

RSRPl,i,j (3)

Fig. 2 illustrates an example of the coverage map withthe best serving cell in each pixel. Colors are used todistinguish the area covered by each sector in the net-work. Each site has an identifier (BS01,BS02..) andsectors’ identifier in each site is either A or B or Csince we are working in a trisectorized network.

Note that in practice even if each pixel has a bestserving cell, a UE located indeed in this pixel is ac-cepted in the network only if the signal level receivedfrom its serving cell is higher than a certain threshold,called Qrxlevmin in 3GPP LTE standard.

3.2 Used Key Performance Indicators (KPIs)3.2.1 KPI1: Timing AdvanceTiming Advance (TA) (in GSM, LTE and LTE-A) orthe propagation delay (in WCDMA) is a time offsetrealized by the BS between its own transmission andthe transmission received from the UE. According tothe calculated offset, the serving BS determines the

suitable TA for the UE [12]. Then, from this TA, theBS calculates the distance traveled by the radio signal.In practice, depending on the resolution (or granular-ity) of TA, a specific distance range where the UE islocated will be calculated. In practice, TA is used asa KPI for network supervision and analysis, with agranularity of 78.25 meters in LTE networks [12].

Figure 3: The division of the covered area basedon TA.

Based on this granularity, TA is discretized into 6intervals indexed by t of the form [78.25 × t, 78.25 ×(t+ 1)], with 0 ≤ t ≤ 4, and [391.25, +∞) for t = 5.As illustrated in Fig. 3, each cell is divided into sev-eral intervals according to the above defined ranges ofdistances. The TA KPI that we obtain from the O&Mdatabase is the distribution of the distance from theBS to the UEs in the cell. For example, 30% of theUEs are in the range of t = 0, 20% in the range oft = 1, 40% in the range of of t = 2, 10% in the rangeof of t = 3 and 0% in the range of t = 4 and t = 5.

For each cell k, we denote by τt(k) the value of theTA KPI that gives the percentage of UE in the TAinterval t.

3.2.2 KPI2: Angle of ArrivalAngle of Arrival (AoA) is defined as the estimatedangle of a UE with respect to a reference direction,typically the geographical North. The value of AoAis positive in an anticlockwise direction [11]. In gen-eral, any uplink signal from the UE can be used toestimate the AoA, but typically a known signal suchas the Sounding Reference Signals (SRS) or DeModu-lation Reference Signals (DMRS) would be used [13].The serving BS determines the direction of arrival bymeasuring the TA at individual elements of the an-tenna array and thus from these delays, the AoA iscalculated.

In order to construct the shape of the downlinkbeam, direction of arrival estimation already existsand is supported in WCDMA but it is not standard-ized [14]. Experiments in [19–21] showed that the accu-racy of AoA estimation depends mainly on the num-ber of antenna elements and also on the separationdistance between them. It varies from small devia-tions (around 2◦ to 5◦) to significant deviations (about30◦ = π

6 ) [19–21].

Aymen et al. Page 5 of 12

Figure 4: The division of the covered area basedon AoA.

Based on the possible AoA deviations provided in[19–21], we assume that the cell is divided into 3 zonesas in Fig. 4 relative to the angle between the UE, theBS and the geographical North. Likewise the TA, wedenote by t = −1, 0 or 1 the index of each AoA zone.

For each cell k, we denote by φt(k) the value of theAoA KPI that gives the percentage of UE in the AoAzone t. Therefore, the percentage of UEs connected tocell k and which are in the range of [−π6 , π

6 ] is denotedφ0(k). Then, φ1(k) represents the percentage of UEsconnected to cell k and which are in an angle of arrivallarger than π

6 . Also, φ−1(k) represents the percentageof UEs connected to cell k and that are in an angle ofarrival less than −π6 . In this sectorization, we considerthat the angle of arrival is equal to zero when pixelPi,j has the same angle as the azimuth[2] of its servingsector.

An example of distribution that we can get is 30%of UEs with range corresponding to t = −1, 40% forthe range of AoA t = 0 and 30% for t = 1.

3.2.3 KPI3: Neighboring cell levelIn different events of UEs connected to the network

such as handover process, every UE measures the sig-nals of the detected cells and sends a report of thesemeasurements to its serving cell. In GSM, the detectedneighboring cells list is reported periodically by theUE to the BS. However, in 3G and 4G networks, it isreported to the BS only when a special event is trig-gered. The counter in the O&M database represent-ing the neighboring cell level of a given neighboringcell is incremented whenever that cell is received (in ameasurement report) as an eligible candidate cell forhandover (Fig. 5). In order to get the KPI as a distri-bution, the neighboring cell level of each neighboringcell is calculated with respect to the total number ofreported neighboring cells.

For each neighboring cell l of the serving cell k, wedenote by ϑl(k) the relative number of times where cell

[2]The azimuth is the angle between the direction ofmaximum antenna radiation with respect to the geo-graphical North.

Figure 5: Updating the Neighbor cell level KPI inthe O&M database.

Table 1: Neighboring cell level for the cell BS1A in Fig.2.

Cell ID BS3A BS7B BS3C BS6C BS2C BS2A BS3B BS4ANeighboring level 0.2478 0.1371 0.1364 0.1298 0.1038 0.1007 0.0759 0.0685

l is reported as eligible candidate cell for handover ina measurement report to the serving cell k.

Normalizing with the total number of reportedneighboring cells makes ϑl(k) a density distributionKPI, i.e.

∑

l∈Sk

ϑl(k) = 1 (4)

Sk is the set of the neighboring cells of the cell k. Table1 shows an example of this KPI for the given servingcell BS1A (refer to Fig. 2).

3.2.4 KPI4: Load time

The load time represents the percentage of time whenthe cell’s resources are fully occupied. This KPI is agauge counter (i.e. a percentage of time) already de-fined and provided by all O&M vendors. It is calcu-lated per hour and can be given for the busy hours,for the day or whether for the week or the month. Inthis paper, we denote by ρ(k) the load time of cell k.

3.2.5 KPI5: Arithmetic and Harmonic Meanthroughputs

The Arithmetic Mean Throughput (AMT) is the meanof the throughputs of all connected UEs. On the otherside, the Harmonic Mean Throughput (HMT) is the

Aymen et al. Page 6 of 12

average of the inverses of the throughputs[3]. The HMTdiffers from the AMT by the fact that it gives more im-portance to throughputs of UEs with bad radio con-ditions. Based on the formula of the cell throughputdefined in the O&M database by most equipment ven-dors, it is important to notice that the cell throughputhas almost the same value as the HMT.

The utility of these KPIs comes from the followingpossible scenarios: if HMT is high but always lowerthan AMT, one can infer that most of the users mightbe in bad radio conditions and if the arithmetic meanthroughput is high, most of the users should be closeto the serving cell. In other words, if most of the usersare in the cell edge, the HMT becomes more signifi-cant and the AMT is decreased and becomes close tothe HMT. However, if most of the users are in goodradio conditions, the difference between the HMT andthe AMT becomes significant. Moreover, in the caseof uniformly distributed traffic in the cell between theedge and the center, the difference between the arith-metic and the harmonic mean throughput is also largebut less than the case where UEs are mostly concen-trated in the center.

For each cell k, we denote by µa(k) and µh(k) arerespectively the AMT and the HMT KPIs taken fromO&M database.

3.3 Map of potential traffic hotspotsA map of potential traffic hotspots is an additionallayer obtained from commercial data so as to improvethe hotspot localization. In this case, the spatial traf-fic distribution is calculated with taking into consid-eration the a priori knowledge of the location of in-dustrial and commercial zones or the location of resi-dences. This map includes also informations about thelocation of rivers, forests etc.. Such a map represents areference map to define the contribution of each KPIin the estimation of the spatial traffic distribution likedescribed in subsection 4.2.

We define Q = (qi,j)1≤i,j≤m as the matrix represent-ing the potential hotspots as follows

qi,j =

{ωi,j if Pi,j is in a potential hotspot

0 otherwise(5)

ωi,j represents the importance of the potential hotspot.In fact, the weight assigned to each hotspot is evalu-ated according to the heaviness of the traffic that canbe carried inside it. For example, a residential zonecannot take the same weight as a very large commer-cial zone.[3]HMT is always lower than AMT according to themathematical definition of the harmonic mean and thearithmetic mean.

4 TRAFFIC LOCALIZATIONALGORITHM: DESIGN ANDOPTIMIZATION

4.1 Description of the algorithmBefore running the localization algorithm, the O&Mreports the statistics of each KPI corresponding to thecells in its controlled area. Then, the following stepsare performed.

4.1.1 Step 1: calculate the spatial distribution oftraffic weights according to TA.

For the design of the step based on TA, we attribute to

each pixel Pi,j a traffic weight q(1)i,j . To do this, each cell

in the coverage map is divided into 6 zones dependingon the distance from the BS as illustrated in Fig. 3.Then, each pixel takes a weight equal to the percentageof UEs in its range of TA.

q(1)i,j =

5∑

t=0

τt(c∗i,j)× χ1,t(Pi,j) (6)

where χ1,t(Pi,j) is the indicator function of TA zonet in cell c∗i,j ; χ1,t(Pi,j) takes the value 1 if the pixelbelongs to TA zone t of its serving cell and 0 otherwise.

4.1.2 Step 2: calculate the spatial distribution oftraffic weights according to AoA.

In this step, each cell in the coverage map is dividedinto 3 zones as illustrated in Fig. 4. So, each pixel in the

same range of AoA takes a traffic weight q(2)i,j equal to

the percentage of connected UEs in this range of AoA.

q(2)i,j =

5∑

t=0

φt(c∗i,j)× χ2,t(Pi,j) (7)

Likewise, χ2,t(Pi,j) is the indicator function of AoAzone t in cell c∗i,j ; χ2,t(Pi,j) is given by

χ2,t(Pi,j) =

{1 if Angle(Pi,j , c

∗i,j)− θ(c∗i,j) ∈ It

0 otherwise

(8)

with

It =

[−π6 , π6 ] if t=0

[π6 , π] if t=1

[−π, − π6 ] if t=-1

(9)

and Angle(Pi,j , c∗i,j)) is the angle between pixel Pi,j

and its serving cell with respect to the geographicalNorth of the coverage map. θ(c∗i,j) is the azimuth ofthe antenna of cell c∗i,j .

Aymen et al. Page 7 of 12

4.1.3 Step 3: calculate the distribution of trafficweights according to the neighboring cell level.

Figure 6: Exemple of attributing traffic weightsbased on Neighboring cell level.

Exploiting the neighboring cell level degree is moti-vated by the fact that when a neighboring cell is re-ported many times rather than others, the pixels hav-ing this cell as the second best serving cell containprobably most of the traffic.

We attribute to each pixel a traffic weight q(3)i,j equal

to the value of the neighboring cell level of its secondbest serving cell like shown in Fig. 6.

q(3)i,j = ϑci,j (c∗i,j) (10)

4.1.4 Step 4: calculate the distribution of trafficweights according to cell loads.

In hotspot zones, the traffic evolution has almost thesame behavior in the neighboring cells due to the loadbalancing and forced handovers [15]. As a consequence,if a cell is congested and one of its neighbors is con-gested, it is likely that most of the traffic is located inthe edge between the two cells and if there is no corre-lation with any of the neighboring cells, then traffic ismost probably generated from users close to the serv-ing cell. Furthermore, if two cells are congested butthe heavy traffic is not located between them, the at-tributed traffic weight in the region between these twocells will be significant. The next step provides correc-tions to this step with comparing the arithmetic meanthroughput to the arithmetic mean throughput of thecell.

We define ψc∗i,j as the set of neighboring cells havingthe same behavior in terms of load time as the servingcell c∗i,j and are eligible for RSRP-based handover inPi,j . It is given by

ψc∗i,j = {k ∈ C such that |ρ(c∗i,j)− ρ(k)| < ε

and |RSRPc∗i,j ,i,j −RSRPk,i,j | < λ} (11)

where λ is a parameter used to identify the set of can-didate cells for a possible handover (by default, it isequal to the handover margin) and ε is used in orderto get only the cells having the same behavior as theserving cell (ε is set to 0.1 in the localization exerciseshown, next, in the simulation section).

The traffic weight q(4)i,j attributed to each pixel based

on the load KPI is then given as follows

q(4)i,j =

1n(ψc∗

i,j)

∑k∈ψc∗

i,j

ρ(k) if ρ(c∗i,j) > ρ

0 otherwise

(12)

n(ψ) means the number of elements in the set ψ, ρ is athreshold used to identify the very loaded serving cells(in practice the congestion threshold is set to 70%).Thus, only pixels, belonging to congested cells, get atraffic weights according to this KPI.

4.1.5 Step 5: calculate the distribution of trafficweights according to AMT versus HMT.

Attributed traffic weights are the difference betweenthe AMT of the cell and the HMT divided by a con-stant µ0 in order to have all the calculated weightsin the range [0, 1]. The constant µ0 is the maximumthroughput that a user can get. The weight in eachpixel depends on its position relative to the positionof its best serving cell and also on the value of thedifference between the two mean throughputs. So, theweights for pixels in the cell center take the value ofthe difference between the HMT and the AMT dividedby µ0. The rest of the pixels in the cell takes a traf-fic weight equal to the complementary of this value asgiven in (13).

The traffic weight q(5)i,j attributed to each pixel based

on the HMT and the AMT is then formulated as fol-lows:

q(5)i,j =

{µa(c∗i,j)−µh(c

∗i,j)

µ0if RSRPc∗i,j ,i,j

> RSRP0

1− µa(c∗i,j)−µh(c

∗i,j)

µ0if RSRPc∗i,j ,i,j

< RSRP0

(13)

where RSRP0 is a RSRP threshold used to differenti-ate between the cell edge versus cell center UEs.

Aymen et al. Page 8 of 12

4.1.6 Step 6: include all KPIs in one global metricand estimate the traffic hotspot zones.

After analyzing all the KPIs and extracting infor-mation about hotspots from them, this step identi-fies the way to transpose all the outputs from theprevious steps into a unique traffic weight Q =(qi,j)1≤i,j≤m. The simplest way to get the matrixQ is by making a weighted sum of the matrices

Q(s) = (q(s)i,j )1≤i,j≤m, 1 ≤ s ≤ 5, with an importance

factor x = (x1, x2, x3, x4, x5)T depending on the im-portance of the information extracted from each KPI.So, we have that

Q =

5∑

s=1

xsQ(s) (14)

The trivial value of x is ( 15 ,

15 ,

15 ,

15 ,

15 )T . In this case, x

is not optimal because some KPIs provides more pre-cision than others and some KPIs are correlated, assaid before. For these reasons, we define the optimalvalue of x as the result of an optimization problemwhich reduces the distance between the map of poten-tial hotspots and the matrix Q. This optimization isdetailed in next subsection 4.2.

4.1.7 Step 7: smoothing of the estimated spatialdistribution of the traffic.

The estimated spatial traffic distribution is further en-hanced with smoothing which consists in combiningthe values estimated in each pixel with the values esti-mated in the neighboring pixels. The main advantageof smoothing [16] is to delete isolated pixels with sig-nificant weights (wrong estimated hotspots). From an-other side, this step allows to ensure more the existinghotspots in the network once they are not eliminated.

It was shown in [17] that the spatial traffic dis-tribution follows a Lognormal distribution or a mix-ture of Lognormal distributions. Smoothing the esti-mation with a Lognormal smoother supposes the apri-ori knowledge of the direction of its shape relative tothe angle coordinate. Therefore, we choose the func-tion of the smoother to be a Gaussian distance decay(symmetric with respect to the angle coordinate).

q∗i,j =

∑Pi′,j′∈LG(Pi,j , Pi′,j′)qi′,j′∑Pi′,j′∈LG(Pi,j , Pi′,j′)

(15)

where

G(Pi,j , Pi′,j′) =1√2πh

e−|Pi,j−P

i′,j′ |2

2h (16)

with |Pi,j−Pi′,j′ | is the euclidean distance between thepixels Pi,j and Pi′,j′ and h is a parameter related to the

number of the neighboring pixels that are involved intothe smoothing of the value in pixel Pi,j (in simulations,we take h = 10−3m2). qi,j are the elements of theweight matrix obtained from the previous step and q∗i,jare the elements of the weight matrix estimated aftersmoothing.

4.2 Optimization of the localization algorithmIt is possible to manually set the importance factorsbut the relative performance of the localization processmay be incompetent comparing to existing solutions.For instance, KPIs holding small information shouldnot be well considered in the aggregated traffic weightQ because they bias the results. In order to improvethe accuracy of the hotspot localization algorithm, theimportance factors, as defined in step 6, need to beoptimized so that the contribution of each KPI in theweighted sum is justified.

Finding the optimal vector x is the result of an op-timization problem that reduces the distance betweenthe available map of the potential hotspots Q and theestimated traffic map Q found in Step 6 of the lo-calization algorithm. The optimization is performedonly one time for an area that we well know its poten-tial hotspots. The obtained optimal importance fac-tor might be used later for any similar environment(urban, suburban, rural...) without any knowledge ofits potential hotspots because the contribution of eachKPI mostly depends on the type of environment. Forinstance, TA and AoA are more efficient and have morecontribution in rural areas, however in urban areas,measuring the TA or the AoA presents more errorsdue to obstacles resulting thus to more reflexions anddiffractions.The optimization of x can be formulated as follows:

minimizex∈R5

∑mi,j=1(qi,j − qi,j)2 =

∑mi,j=1

(∑5s=1 xsq

(s)i,j − qi,j

)2

xs ≥ 0, s = 1..5

(17)

In order to put the optimization problem in a knownand solvable form, we consider

AT =

q(1)1,1 ... q

(1)1,m ... q

(1)2,1 ... q

(1)m,m

. .. . .. . .. .

q(5)1,1 ... q

(5)1,m ... q

(5)2,1 ... q

(5)m,m

b = [q1,1...q1,m...q2,1...qm,m]T

Clearly, A is a matrix of m2 lines and 5 columns andb is a vector of length m2.

Aymen et al. Page 9 of 12

Using matrix notation, the optimization problem ofequation (17) becomes a minimization of the distance

between the vectors Ax and b in the space Rm2

:

{minimize

x∈R5‖Ax− b‖2

xs ≥ 0 , 1 ≤ s ≤ 5(18)

where ‖.‖2 is the standard 2-norm (the Euclidian

norm) in the space Rm2

.

This formulation represents a least square optimiza-tion problem and can be solved using the Gauss New-ton method [18]. This method is fast and provides ac-curate solution. As said before, the optimal importancefactors of this problem is not specific to limited numberof possible scenarios. It can indeed be changed from ascenario to another but with small variations since theimportance of each KPI remains more or less the sameand is slightly independent from the taken scenarios.The optimal vector x would lead to a more precisehotspot localization that we look forward to in the ob-jective of the paper.

5 NUMERICAL RESULTS5.1 Parameters’ settings

In order to validate and evaluate the proposed algo-rithm, we use a LTE simulator that allows dynamicusers’ arrivals and departures after being served. Ateach time step of the simulator, equal to 1s, the num-ber of call attempts are generated according to a Pois-son distribution with intensity λ = 200 UEs/s. UE po-sitions are generated depending on the traffic weightin each pixel of the coverage map. UEs are acceptedin the network only when there are available resourcesand when their RSRP is higher than the thresholdQrxlevmin (set to −115dBm in this work). We supposethat each UE has a file of size 1Mbit to download. EachUE quits the network after downloading the entire file.Moreover, we consider that a part of UEs (20%) aremoving during their transmission with a mobility of8.33km/h. This means that the simulation supportsalso handover events with a margin set to 6dB. UEsare scheduled according to the round robin model. Atthe end of the simulation, which lasts 1 hour, all KPIs(including the previously cited KPIs) are calculatedand stored in a file.

The simulated network is shown in Fig. 2 and com-posed of 23 tri-sectorized eNB (evolved Node-B) cov-ering an area of 3×3Km2. eNB positions and physicalcharacteristics are taken from a real network. Each sec-tor in each eNB is equipped with a directive antennahaving a beamwidth equal to 65o. Moreover, each sec-tor has an available capacity equivalent to 20Mhz of

spectral bandwidth operating in the band 2.6Ghz. Be-cause of the network heterogeneity, the list of param-eters (e.g., eNB transmitter noise figure, cable loss,maximum transmit power...), involved in this simula-tion depends on each site. Hence, it is worthless to citeall these parameters.

The coverage map (RSRP from each cell in eachpixel), provided by a planning tool, has a resolutionequal to 25 meters. According to this map, the firstand the second best serving cell are identified in eachpixel.

Before starting the simulation, we attribute the traf-fic weight in each pixel of the coverage map based ona mixture of Log-normal distributions [17]. As shownin Fig.7, each peak of a Log-normal density representsa traffic hotspot.

For the potential traffic map, we attribute arbi-trary weights in some pixels according to our knowl-edge about some hotspot zones in the chosen map.The exploitation of this map improves the precision ofhotspot localization but recall again that it is not akey element in the proposed algorithm.

The validation of the algorithm consists in findingthe traffic weight in each pixel (following the steps ofthe algorithm) and comparing it with the original traf-fic weights generated at the beginning of the simula-tion.

5.2 ResultsRunning the hotspot localization algorithm and per-forming the optimization, as described in Subsection4.2, we get the importance factor

x = [0.418 0.2689 0.2281 0.0358 0.0491]T

The validation of the obtained importance factors isnot the objective of the present paper. However, wewere able to test other values set for importance fac-tors and the relative results are clearly improved afterthe optimization step. So, these optimal importancefactors are used in the performance evaluation for thedifferent reasons cited before.

In Fig. 7, the original distribution of the traffic gen-erated at the beginning of the simulation is drawn. InFig. 8a, the estimated traffic distribution is depictedbased on the proposed algorithm without including thesmoothing process. Fig. 8b represents the spatial dis-tribution of the traffic after smoothing the calculateddistribution.

Regarding the identification of the hotspot zones, weobserve that most of the hotspot zones are found in theentire network map.

In Table 2, the coordinates of the generated hotspotsand those estimated in step 6 and in step 7 are ex-tracted from the calculated matrices generating the

Aymen et al. Page 10 of 12

Table 2: Coordinates of generated and estimated hotspots holding highest traffic weights (x,y) in meters.Generated hotspot (1100,960) (760,940) (20,980) (-1020,-640) (1040,280) (-200,-120) (-960,740) (220,100) (960,-300)

Estimated hotspot in step 6 (1140,940) (740,940) (40,980) (-1040,-660) (1200,420) (-240,-180) (-940,780) (320,140) (940,-280)

Estimated hotspot in step 7 (1100,960) (760,900) (40,980) (-1020,-660) (1100,340) (-180,-120) (-920,760) (260,100) (960,-320)

Distance between the original andthe estimated hotspot in step 6

44.72 20 20 28.28 212.6 72.11 44.72 107.7 28.28

Distance between the original andthe estimated hotspot in step 7

0 40 20 20 84.85 20 44.72 40 20

Figure 7: Original traffic distribution.

figures above. From Table 2, we find out that the exe-cution of the proposed algorithm until the step 6 pin-points the hotspots generated in the network with aprecision of 59.84 meters (see Table 2, the mean of thecalculated distances between the original and the esti-mated hotspots in step 6). However, the spatial traf-fic distribution is quite uniform inside these hotspots.Then, the shape of the estimated traffic distribution isimproved after smoothing and the precision becomesabout 31 meters (see Table 2, the mean of the calcu-lated distances between the original and the estimatedhotspots in step 7).

Fig. 9 shows the CDF of traffic weights attributedto pixels based on the proposed algorithm. To localizethe hotspot zones, we are interested only in the regionsurrounded with blue ellipse in Fig. 9 which representthe significant weights.

Fig. 9 indicates that using all the 5 KPIs in the net-work gives better estimation of traffic distribution ascompared to the case when using only some of them. Infact, significant weights have a small density when us-ing only one KPI which means that the traffic distribu-tion is more flat and is uniform inside the hotspot zone.This density is increased and becomes near the exactdistribution when all the KPIs are used and when allthe steps of the proposed algorithm are performed.

From Fig. 9, we note that the most useful KPIs arethe TA, AoA and the neighbor cell level. The meanthroughput and the load time have also an impact onthe estimation and improve further the localization.

(a) Estimated traffic distribution in step 6.

(b) Estimated traffic distribution in step 7.

Figure 8: Spatial traffic distribution.

In addition, smoothing the estimated traffic distribu-tion reduces the difference between the original spatialdistribution of the traffic and the estimated one.

Among the plotted curves in Fig. 9, the curve repre-senting the combination between TA and neighboringcell level allows us to compare approximately the solu-tion proposed in [5] to the present proposed algorithm.So, the new proposed algorithm perform better andprovides promising results comparing to the solutiondisclosed in [5].

We compare in Table 3 the percentage of detectedhotspots that have the highest weights to the originalhighest hotspots in the network.

In Table 3, the analysis is based on the fact thatweights are sorted in a decreasing way. We determinethe coordinates of the pixels which hold 0.5% (1, 2, 5,10, 20, and 50 respectively) of the traffic in the network(using the real distribution). Then, we calculate thesum of estimated weights in these pixels.

Aymen et al. Page 11 of 12

2 4 6 8 10 12 14

x 10−4

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

x

F(x

)

Empirical CDF

Real weightsTAAoANeighbor cell levelTA and AoATA and Neighbor cell levelTA, AoA and Neighbor cell levelTA, AoA, Neighbor cell level and LoadTA, AoA, Neighbor cell level and Mean throughputAll KPIsAll KPIs after smoothing

2 KPIsOnly 1 KPI

Real weights

3&4 KPIs

All KPIsAll KPIs

aftersmoothing

Figure 9: CDF of real and estimated trafficweights.

Table 3: Percentage of detected hotspots.Real % of first Estimated % with Estimated % with Estimated % after

hotspots only TA all KPIs smoothing0.5 0.12 0.30 0.4221 0.26 0.48 0.582 0.46 1.09 1.135 1.12 2.17 2.56

10 2.29 4.34 4.820 4.87 33.45 9.750 14.22 74.34 27.32

The purpose of the evaluation in Table 3 is to seethe importance of using all the KPIs and the im-pact of smoothing on the localization of most signifi-cant hotspots. From another side, the column relatedto performances of using only TA stands to the rea-son that, in literature, several traffic localization tech-niques are based on TA. So, this allows a commoncomparison of these techniques to the proposed one.In this context, Table 3 shows that the hotspot local-ization is clearly improved when it is based on the 5KPIs comparing to the use of only TA.

From Table 3, we also observe that the first hotspotsthat have the highest weights are reasonably estimatedand this estimation is further enhanced with the step ofsmoothing. However, when we increase the percentageof the first significant hotspots (from 0.5% to 10%), theestimation shows less efficiency and this is due to thefact that some zones close to the hotspots could takea significant weight without carrying heavy traffic.

Before smoothing, pixels within the same region ofa hotspot take the same weight. However, weights inthe center of a hotspot are increased after smooth-ing and weights of pixels in the edge of a hotspot arereduced. Hence, when the percentage of the first sig-nificant hotspots is low, only pixels in the center of ahotspot are evaluated and the sum of weights beforesmoothing is less than the sum after smoothing. In

contrast, when the percentage of the first significanthotspots is high, pixels in the edge of the hotspots arealso included in the evaluation. As a result, the sumof weights before smoothing is larger than the sum af-ter smoothing since weights of pixels in the edge ofhotspots are more reduced after smoothing.

6 ConclusionWe have proposed in this work a modular and op-timized algorithm that consists in combining sev-eral KPIs along with coverage and potential traffichotspots map. Results showed acceptable localizationerror, in cases of moderate and heavy traffic. There-fore, the use of O&M KPIs projected over a cover-age map can be efficient for hotspot localization asthey yield promising results at low operational costs.This method is a good solution that can be used bothto identify areas where a small cell must be deployedand to perform appropriate configurations to lessen thecongestion rate in hotspot zones.

In the future works, we intend to study the impactof imperfect hotspot localization on the network per-formance. This can be done by analyzing the extrainterference that would result from a bad positioningof the small cell and the degraded throughput experi-enced by the involved UEs in the network.

Author details1Orange Labs, 38/40 avenue General Leclerc, 92794 Issy-les-Moulineaux,

France. 2Telecom SudParis, 9 street Charles Fourier, 91011 Evry, France.

References1. A. Jaziri, R. Nasri and T. Chahed, Traffic Hotspot localization in 3G

and 4G wireless networks using OMC metric, Proc. IEEE PIMRC,

p.246-250, September 2014.

2. A. Damnjanovic, J. Montojo, Y. Wei, T. Ji, T. Luo, M. Vajapeyam, T.

Yoo, O. Song, and D. Malladi, A survey on 3GPP Heterogeneous

Networks, IEEE Wireless Communications, vol. 18, no 3, p. 10-21.

June 2011.

3. T. Hurley and S. Martin-Leon, Traffic location in mobile cellular

telecommunications systems, EP1115262 A1, July 2001.

4. O. Ho-A-Chuck, Process for analysing traffic location in a cellular

radio communication network, WO 1997039598 A1, October 1997.

5. T. Vaara and R. Aalto, Traffic hot spot locating method, CA 2267801

A1, April 1998.

6. A. Athalye, V. Savic, M. Bolic and P. M. Djuric, Novel Semi-passive

RFID System for Indoor Localization, IEEE Sensors Journal, vol. 13,

no 2, p. 528-537, 2013.

7. L. M. Ni, Y. Liu, Y. C. Lau and A. P. Patil, LANDMARC: indoor

location sensing using active RFID, Wireless networks, vol. 10, no 6, p.

701-710, 2004.

8. C. S. Randriamasy, Geographic representation of traffic load in a

mobile radio communication network, EP 1168866 A1, January 2002.

9. A. Roxin, J. Gaber, M. Wack, A. Nait Sidi Moh, Survey of Wireless

Geolocation Techniques, IEEE Globecom Workshops, 2007.

10. J. Johansson, W. A. Hapsari, S. Kelley and G. Bodog, Minimization of

Drive Tests in 3GPP Release 11, IEEE Communications Magazine ,

Vol 50, no 11, p. 36-43, November 2012.

11. 3GPP TS 36.214, Evolved Universal Terrestrial Radio Access

(E-UTRA); Physical layer; Measurements, version 10.1.0, Release 10,

April 2011.

12. 3GPP TS 36.213, Evolved Universal Terrestrial Radio Access

(E-UTRA); Physical Layer Procedures, v8.6.0, Release 8, March 2009.

Aymen et al. Page 12 of 12

13. S. Sesia, I. Toufik and M. Baker, LTE - The UMTS Long Term

Evolution: From Theory to Practice − > 19.4.3 p. 432, July 2011.

14. P. Singla and J. Saxena, Beam forming Algorithm for Smart Antenna

in WCDMA Network, International Journal of Engineering and

Innovative Technology (IJEIT) Volume 3, Issue 5, November 2013.

15. R. Nasri and Z. Altman, Handover Adaptation for Dynamic Load

Balancing in 3GPP Long Term Evolution Systems, Proc. of

International Conference on Advancnes in Mobile Computing and

Multimedia (MoMM), December 2007.

16. M. P. Wand and M. C. Jones, Kernel smoothing, Vol. 60. Crc Press,

1994.

17. D. Lee, S. Zhou, X. Zhong, et al. Spatial modeling of the traffic

density in cellular networks, IEEE Wireless Communications, vol. 21,

no 1, p. 80-88, 2014.

18. S. Boyd, and L. Vandenberghe, Convex Optimization, Cambridge

University Press, 2004, Chapter 6.

19. L. Zimmermann, A. Goetz, G. Fischer and R. Weigel, GSM mobile

phone localization using time difference of arrival and angle of arrival

estimation, Proc. IEEE Systems, Signals and Devices (SSD), p. 1-7,

2012.

20. S. Reynaud, M. Mouhamadou, K. Fakih et al., Outdoor to Indoor

channel characterization by simulations and measurements for

optimising WiMAX Relay Network Deployment, IEEE Vehicular

Technology Conference, VTC, p. 1-5, Spring 2009.

21. A. Kangas and T. Wigren, Angle of arrival localization in LTE using

MIMO pre-coder index feedback, IEEE Communications Letters, vol.

17, no 8, p. 1584-1587, 2013.