978-1-4799-5863-4/14/$31.00 ©2014 IEEE

Performance of SON for RSRP-based LTE/WLAN access network selection

István Z. Kovács*, Daniela Laselva*, Per-Henrik Michaelsen*, Yu Wang+, Relja Djapicɔ, Kathleen Spaeyx

*Nokia

Aalborg, Denmark +Ericsson Research Stockholm, Sweden

ɔTNO, Delft, Netherlands

x iMinds/University of Antwerp, Antwerp, Belgium

Abstract —Carrier-grade Wireless Local Area Network (WLAN) is becoming an important complementary system to cellular networks for Mobile Network Operators (MNOs). Network controlled access network selection between cellular and WLAN is an essential functionality to optimize network performance and user experience. Automated configuration and optimisation of the network selection mechanism is of utmost importance in the emerging complex heterogeneous networks. In this article, we present and evaluate a Self-Organizing Network (SON) scheme for optimizing autonomously the access network selection between the Long Term Evolution (LTE) and WLAN systems. The adopted access network selection mechanism uses the standard LTE Received Reference Signal Power (RSRP) measurements available at the User Equipment (UE) and a set of simple rules based on network-provided RSRP thresholds. The proposed SON mechanism is using the LTE cell load estimated at the evolved NodeB (eNB) to update the RSRP thresholds in order to achieve the best load balancing in the network. Simulation results in a realistic network highlight the benefits of the proposed SON mechanism and possible further improvements.

Keywords—LTE, self-organizing network, WLAN, network selection, network optimization.

I. INTRODUCTION Driven by the explosive growth in data traffic during the

past few years, the limited availability of new licensed spectrum, and the increasing number of mobile devices [1], [2] supporting both cellular and WLAN interfaces, MNOs are currently exploiting the offloading of mobile data traffic to WLAN networks. Today, the network selection between cellular and WLAN networks is manually controlled by the users, and typically set as “connect to WLAN if available”. However, to meet the high user expectations in terms of service availability and quality, and to efficiently exploit the available resources, MNOs are more and more enabling WLAN as an integrated and controlled part of their networks. Because of the challenges posed by the emerging heterogeneous cellular networks, with multiple cellular layers e.g., macro, micro, WLAN, it is crucial to develop intelligent access selection mechanisms between WLAN and the cellular layers.

Access network selection in heterogeneous wireless networks is a research topic extensively studied in academia. A common approach found in literature optimizes a multi-dimensional utility function in order to select the most suitable access network for a particular user, in a given location, requesting a particular service [4]. Various parameters such as load, throughput, Quality of Service (QoS), resource

utilization costs, battery lifetime or even specific user preferences might be used in the definition of the utility function [5], [6].

Access selection approaches proposed in [7] rely on IEEE 802.21 Media Independent Handover (MIH) standard [8]. The potential of this standard is however limited due to its restricted support from network/user equipment. 3GPP has specified the Access Network Discovery and Traffic Steering Function (ANDSF) framework as an advanced policy control to assist UE in network discovery and selection [9]. A UE supporting ANDSF can acquire the list of access networks available in the vicinity as well as operator preferences on network selection and traffic steering. The Wi-Fi Alliance Hotspot 2.0 specifications aim at improving user experience by means of seamless and secure Wi-Fi connectivity [10]. Hotspot 2.0 allows devices to acquire information on 3GPP cellular networks in order to ease network discovery and selection process. The main limitation of most of the currently deployed access network selection mechanisms is their static behaviour, i.e. the lack of adaption to dynamically changing traffic conditions, which makes them unsuitable to optimally exploit the available LTE and WLAN cell radio resources.

The contribution of this paper is twofold. First, based on an acceptable balance between algorithm complexity and performance, this paper adopts an access network selection (ANS) mechanism between LTE and WLAN which uses the standard LTE RSRP measurements available at the UE and a set of simple rules based on network-provided RSRP thresholds; second, it proposes and evaluates an automated, SON based, configuration and optimisation of the ANS mechanism with the goal of efficient resources utilization and fulfilment of the desired operator policy. The automation component is crucial in dynamic heterogeneous networks, characterized by different cell sizes and including diverse WLAN deployments, to achieve minimal operational effort and cost. The general idea behind using SON [3] for ANS is that a SON closed-loop control function autonomously tunes the network control parameters, which control the ANS mechanism according to the changes in the network and to the desired overall operator policy. The SON function continuously monitors a number of relevant Key Performance Indicators (KPIs), e.g. the LTE cell load, WLAN utilization, cell throughput, and evaluates on a regular basis the opportunity for adapting the network parameters.

The remaining of the paper is organised as follows. The system modelling approach and assumptions are illustrated in

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DOI: 10.1109/ISWCS.2014.6933378

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Section II. The performance evaluation ofANS is presented in Section III. The presented and discussed in Section IV. The with the conclusions and recommendations in

II. SYSTEM MODELLING FOR SON E

A. RSRP-based Network Access Selection mThe ANS mechanism adopted in this p

standardized 3GPP LTE RSRP measuravailable at the UE and the RSRP threshold iHere, we propose two different thresholdThr_High and RSRP Thr_Low, to be used inevaluated at each UE to determine whether to WLAN when a new active session starts: 1) UE served on WLAN if RSRP > RSR

RSS>RSS_min, otherwise UE served on LTE.in case of co-located LTE small-cells (micropoint and with this approach an UE is served UE RSS is above certain level but without thexplict WLAN RSS threshold.

2) UE served on WLAN if RSRP < RSRSS>RSS_min, otherwise UE served on LTE.in case of LTE macro cells without a co-lopoint.

The RSS_min value denotes the WLAN Signal Strength (RSS) minimum connectivity

B. Proposed SON Algorithm The RSRP-based SON algorithm we pro

is a single control parameter mechanism, whcell load1 as input KPI to adjust the RSRP thcell. By using standardized RSRP measurneed for additional standardization support.the algorithm is shown in Figure 1. A target cell load (green range in Figure 1) is prconfiguration parameters, called CellLoaCellLoad Thr_Low.

At the start, the two RSRP thresholds (Hinitialized to their default values. During thSON function, the LTE cell load is periodicafiltered. At each SON step, the high LTE checked, and if met, the corresponding RSRor Low) is adjusted with the target to direcWLAN to avoid LTE congestion. SpThr_High is decreased in micro cells and Rincreased in macro cells. If the high LTE celnot met, the low LTE cell load condition imet, the corresponding RSRP threshold (adjusted with the target to direct new sessadjustments of the RSRP thresholds are madload is in the target range. After every updthresholds, their new values are assumed to bUEs that are in the coverage area of the LTE

1 In this paper the LTE cell load is defined as the requirresource blocks to serve the existing users when providithroughput of 10 Mbps.

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f the RSRP-based SON results are paper is finalized

n Section V.

EVALUATION

mechanism paper exploits the rements that are introduced in [11].

ds, labelled RSRP n two simple rules to connect or not

RP Thr_High and This rule is applied

o) and WLAN acess on WLAN when the

he need to specify an

SRP Thr_Low and This rule is applied

ocated WLAN acess

Radio Received y level.

opose in this paper hich uses the LTE

hreshold values per res, we avoid the The flowchart of range for the LTE redefined by two d Thr_High and

High and Low) are e execution of the ally calculated and load condition is

RP threshold (High ct new sessions to pecifically, RSRP RSRP Thr_Low is ll load condition is is checked, and if (High or Low) is sions to LTE. No de if the LTE cell date of the RSRP be signalled to the cell, and to be

red fraction of physical ing a minimum user

Figure 1: Flow chart of the based LTE/WLAN ac

used in the NAS mechanism inIII.A.

The targeted heterogeneoassumed higher traffic distribmakes possible to design ourprinciple of separation of timeThr_Low values in all macro cerate compared to the RSRP Thrto allow convergence of the Rtwo consecutive updates of thmacro cells. Thus, the overall S

C. Evaluation Scenario and MThe evaluation scenario is c

traffic shopping street area inwhich is covered by three netwThe deployment includes 28 coWLAN APs at lamp post heimacro sites. Stationary outddatagram protocol (UDP) typePoisson distributed call arrivaThe simulated WLAN access interference from one additiona

Figure 2: Investigated site spnetwork deployment la

SON mechanism for RSRP-

ccess network selection. n the UE as described in Section

ous network deployment and bution around the small cells, r SON algorithm based on the e scales. Accordingly, the RSRP ells should be updated at slower r_High values in the micro cells

RSRP Thr_High values between e RSRP Thr_Low values in the

SON becomes stable.

Modelling characterized as an outdoor high n the city centre of Hannover

work layers as shown in Figure 2. o-sited outdoor LTE micros and ght (5m) in addition to 4 LTE

door users are assumed. User e traffic with fixed file size and l (downlink only) is simulated. corresponds to operation, under

al ‘external’ WLAN network.

pecific dense urban scenario: ayout and user locations.

LTE Macro LTE Micro & WLAN User location

Deployment: LTE macro layer: 20MHz@1.8GHz, 2x2MIMO, 46dBm LTE micro layer: 20MHz@1.8GHz, 2x2MIMO, 33dBm 6dB range extension WLAN 802.11n: 20MHz@2.4GHz, SISO, 20dBm

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Parameter Description

P_SON Periodicity of SON algorithmexecution

P_LTE Periodicity of LTE load calcula

Į LTE load (L) filtering factor,

CellLoad Thr_Low/High LTE cell load thresholds Low/H

Min. RSS level Minimum RSS level for WLAconnectivity

RSRP Thr_High Step

Step size of RSPP Threshold Hadjustments (up or down)

RSRP Thr_High Initial

Initial value of the RSRP threshold High

RSRP Thr_Low Min / Max

Minimum and maximum valuethe RSRP Threshold Low

RSRP Thr_High Min / Max

Minimum and maximum valuethe RSRP Threshold High

Traffic model DL UDP with fixed file size aPoisson distributed call arriva

Table 1: Adopted simulation and modelli

Table 1 summarizes the simulation simulation tool has been developed using the

III. PERFORMANCE WITH FIXED RSRPThe first step in our study was to analyz

the proposed ANS algorithm using global the two RSRP thresholds, i.e. the same vacells. The main targets of this exercise werimpact of the RSRP thresholds on the systemto select a reference scenario, and ii) to idrange for the RSRP threshold parameters.

The first performance metric analyzed wuser sessions offloaded to WLAN, see Figurshow that the system behaves as expected wRSRP thresholds, i.e. the offloading rate is inRSRP Thr_High values and/ or for highevalues. The scenario which emerges from tobvious reference case is the one where thselection is maximized, up to 90%, indicatedthe RSRP–based selection conditions are prand a UE connects to WLAN when available-92 dBm, see Table 1). As a second performa3 shows the user session throughput performathe RSRP thresholds.

Analyzing the results in Figure 3, we canglobal settings which maximize the WLAN not maximize the user session throughputs (aand vice versa. Alternatively, it is also temconclusion that by utilizing global settings of-90 dBm for all macro cells and RSRP Thr_for all micro cells it is possible to maximiz5%-ile, user session throughput. While thwould indeed yield an acceptable good ovedue to the non-uniform spatial traffic (seindividual LTE cells are likely under/ overperformance is suboptimal. When the Senabled, the network selection can be better traffic load conditions in each cell, as following section.

L)1(L)1()(L nn meas∗+−∗−= αα

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Setting m 1 sec

ation 0.5 sec in 0.7

High 60%/ 80% or 70% / 90%

AN -92 dBm

High {1, 2, 5, 10} dB

-70 dBm

s of -100 dBm / -50 dBm

s of -110 dBm / -40 dBm

and al

Dsize = 40Mbit Ȝ = 16 calls/s

ing parameters.

parameters. The e Matlab platform.

P THRESHOLDS e the behaviour of configurations for

alue applied to all re: i) to verify the m performance and dentify the correct

was the ratio of the re 3. These results when changing the ncreased for lower r RSRP Thr_Low these results as an he WLAN access

d in Figure 3, when ractically disabled e (with RSS above ance metric Figure ance when varying

n conclude that the offloading rate do average or 5%-ile)

mpting to draw the f RSRP Thr_Low = _High = -80 dBm

ze the average and his latter settings erall performance, ee Figure 2), the rloaded, thus their ON algorithm is tuned to the local presented in the

Figure 3: User session WLAand 5%-ile throughput vs.

IV. SON ALGOR

A. Performance results with opConsidering the deploymen

there are only a few analyzedtraffic conditions, we use fixvalues for all macro cells. Thisthe overall SON design presentThr_Low values are assumed coin order to allow for the RSRP all micro cells.

We start our SON anaconfiguration parameters, whicperformance (see Table 1):

i) LTE Cell load Threii) RSRP Threshold Hiii) RSRP Threshold LThe obtained results corresp

Thr_Low-High of 60%-80% a

)(nsured

”Connect to WLAN when available”

AN offloading ratio, average RSRP Threshold High/Low.

RITHM EVALUATION

ptimized settings nt scenario, see Figure 2, where d macro cells and with similar

xed and global RSRP Thr_Low s approach is also motivated by ted in Section II.B, i.e. the RSRP onstant for sufficiently long time Thr_High values to converge in

alysis by varying three key ch influence the overall system

eshold Low/High, High Step Size, and Low

ponding to the setting CellLoad are summarized in Figure 4 in

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terms of average and 5%-ile user session relative to the reference scenario “Connectavailable” presented in Section III.

In all the analysed cases for the SOparameters, a gain of 30% to 50% in avethroughput has been obtained. However, thefrom Figure 4 is that the RSRP Thr_Low andHigh Step Size values have a significant impuser throughput, and can even cause a loss inbetter understand these results we show in session distribution across the different netwo

For the case with RSRP Thr_Low = -70dBtrend visible in Figure 5 indicating that forThreshold High Step Size the number of usmicro layer decreases due to increasing Wtriggered by the high load in the micro Threshold High Step Size values up to 2dBconvergence to the optimal RSRP Thr_algorithm in the micro cells cannot triggeselections hence the performance in thesuboptimal. For RSRP Threshold High Stepand above, the number of WLAN selections5%-ile user throughput performance of improves sufficiently to result in an overall The mechanism described above is visiblThr_Low = -80dBm. In this scenario the users moved to WLAN is less and they arconditions towards the serving WLAN degrade less the overall WLAN layer performthe RSRP Thr_Low = -70dBm case. performance of the micro and WLAN layhigh to compensate for the lower performacells (due to its higher UE population whedecreases) and this yields an overall 5%-ile tthe order of 150%. For RSRP Thr_Low = -9network performance does not depend on thHigh Step Size values up to 5dB. Beyond ththere is a small performance degradation, coloaded micro cells, where the users to be cannot be selected with sufficient accuracyCellLoad Thr_Low-High of 70%-90% showpresented for the case CellLoad Thr_Low-Hig

Based on these results, henceforth we parameter settings that provide a good taverage and 5%-ile UE throughput: CellLoadCellLoad Thr_High = 80% and RSRP Thr_The global RSRP Thr_Low = -80 dBm vaequalize the average numbers of user sessmacro and micro network layers.

Figure 6 shows the corresponding results in terms of average cell load and average RScell indices see Figure 2). Therefore, as expbetween the average RSRP Thr_High value SON algorithm for a given micro cell and thload can be observed. That is, the higher thee.g., in cell index 2, 4, 7, the lower the RSRvice-versa. As explained earlier, the RSRPapplied to macro cells and it is disabled withdBm. An important observation is thaopportunities of macro cell index 3 are only p

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throughput gains t to WLAN when

ON configuration erage user session e main observation d RSRP Threshold pact on the 5%-ile n performance. To Figure 5 the user

ork layers. Bm there is a clear r increasing RSRP sers served by the WLAN selections

cells. For RSRP B, due to the slow _High, the SON er enough WLAN e micro cells is p Size values 5dB s is higher and the

the micro layer gain up to 140%.

le also for RSRP number of macro re in better signal APs, hence they

mance compared to The combined

yers is sufficiently ance in the macro en RSRP Thr_Low throughput gain in

90dBm, the overall e RSRP Threshold his step size value oming from highly moved to WLAN y. The results for

w similar trends as gh of 60%-80%. use the following trade-off between d Thr_Low = 60%, _High Step = 5dB. alue is selected to sions between the

on a per cell basis SRP Thr_High (for pected, correlation determined by the

he experienced cell e average cell load RP Thr_High, and

P_Thr_High is not h the setting of -40 at the offloading partial as this cell

Figure 4: User session thr“Connect to WLAN when a

different SON pa

Figure 5: User session distrlayers for different SO

covers a substantially large WLAN deployment, hence its algorithm most of the micro ceset CellLoad Thr_Low-High potentially, even higher offeredthe network.

Figure 7 shows an examplloaded micro cell under theevolution of the cell load Thr_High adjustments are illachieves the desired tuning, Thr_High is increased when CellLoad Thr_Low and it is deCellLoad Thr_High. The Figalgorithm is able to react quiWith the selected SON settings1) and RSRP Thr_High StepSiin the adjustment of the RSRPRange (between -40 dBm Range / RSRP Thr_High StepThis value can also set the minithe RSRP Thr_Low threshold.

B. Impact of traffic model paraWe have shown in Section

parameters on the achieved assumption of a given offeparameters. In real systems the traffic types, with various packe

roughput gains relative to available” reference case for arameter settings.

ribution across the network ON parameter settings.

area with no micro cells nor high load. Thanks to the SON

ells have average load below the range, which indicates that,

d traffic can be accommodated in

e of the time trace for a highly e selected SON settings. The and the corresponding RSRP lustrated. The SON algorithm

i.e. the value of the RSRP the averaged load falls below

ecreased when the load exceeds gure 7 illustrates also that the ckly to large load fluctuations. s for P_SON = 1 sec (see Table ize = 5 dB, the maximum delay P Thr_High value across its full and -110 dBm) is equal to

epSize * P_SON = 14 seconds. imum possible update period for

ameters IV.A the impact of SON set-up system performance under the

ered traffic and traffic model offered load is a sum of various

et sizes. Thus, in this section we

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Figure 6: Per cell results of average cell Threshold High for selected SON param

Figure 7: Time trace example for a highly under the selected SON parameter

analyse the system behaviour under constraffic load, by increasing the call arrival decreasing the file size (Dsize / N), while keeaverage packet inter-arrival time, where N =shows the resulting average and 5%-ile user sgains relative to N=1 reference case; we useconfiguration parameter settings from Sincreasing N, the instantaneous traffic loainterval is increased, thus all cells experiencetimes with high load and this leads to a degraThe 5%-ile performance of the WLAN laydegraded because the WLAN utilisation inAP’s load is not controlled by the SON algori

Figure 8: User session throughput gaireference traffic scenario (N=1) vs. tr

1 2 3 4 5 6 1 2 3 4 5 6 7 8 9 10 11 12 1

10

20

30

40

50

60

70

80

90

100

Cel

l loa

d (%

)

LTE Macro Co-located LTE Micro

0 20 40 60

10

20

30

40

50

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Cel

l loa

d (%

)

Time [s]

CellLoa

CellLoad T

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load and RSRP meter settings.

loaded micro cell

r settings.

stant total offered rate (Ȝ * N) and

eping constant the = 1, 2, 4. Figure 8 session throughput

e the selected SON ection IV.A. By

ad per scheduling e longer periods of aded performance.

yer is significantly ncreases while the ithm.

ins relative to affic scaling.

V. CONCLUSION

We have presented and Network (SON) solution for access network selection betw(LTE) and WLAN systems. selection mechanism (ANS) Received Reference Signal available at the User Equipmenbased on network-provided algorithm updates autonomousbased on the estimated cell loaload range, thus achieving an each cell. The evaluation resuSON parameter configuration, gcan be achieved in the avethroughputs, compared to the “reference case, respectively. performance gain numbers aretype and an increase in thesignificant loss of performanallows direct control of the load

For further improved perforscenarios a refined SON desevaluation including typical mechanisms will be further neeunderstanding of the benefits of

ACKNOW

The research leading to thewithin the FP7 SEMAFOUR pfrom the European Union Se(FP7/2007-2013) under grant ag

REFER

[1] 4G Americas Whitepaper, "MeetSpectrum, Technology and Policy

[2] Ericsson Mobility Report “On the2013, http://www.ericsson.com/m

[3] S. Hämäläinen, H. Sanneck, Organizing Networks (SON): NOperational Efficiency, John Wil

[4] Lj. Jorguseski, Remco Litjens, “Access Systems”, 14th IEEE Vehicular Technology in the Ben

[5] J. Ha et al, “Dynamic Load baWireless Network EnvironmenCommunications and Information

[6] S. Lee, et al., “Vertical HandoOptimized Performance in HeteTrans. Vehic. Tech., Vol. 58, No.

[7] Sueng Jae Bae et al, “Handover 802.21 in Heterogeneous NInternational Conference on Infor

[8] IEEE 802.21-2008, "IEEE StanNetworks: Media Independent Ha

[9] 3GPP TS 24.302 V11.7.0, “Acc(EPC) via non-3GPP access netw

[10] Wi-Fi Alliance Hotspot 2.0 Tefi.org/knowledge-center/publishe

[11] 3GPP TS 36.304 “User EquipmeR2-141846, April 2014.

13 14 15 16 17 18o / Wi-Fi AP

-110

-100

-90

-80

-70

-60

-50

-40

RS

RP

Thr

(dB

m)

80 100-110

-100

-90

-80

-70

-60

-50

-40

RS

RP

Thr

Hig

h (d

Bm

)

ad Thr_Low

Thr_High

N AND FUTURE WORK evaluated a Self-Organizing optimizing autonomously the

ween the Long Term Evolution The adopted access network

uses the standard 3GPP LTE Power (RSRP) measurements

nt (UE) and a set of simple rules RSRP thresholds. The SON

sly the RSRP thresholds values ad level and a pre-defined target optimal rate of WLAN ANS in ults show that with an optimal gains in order of 50% and 150% rage and 5%-ile user session

“connect to WLAN if available” We also show that these

e dependent on the actual traffic e traffic burstiness results in

nce unless the SON algorithm d on the WLAN layer. rmance in real-life mixed traffic sign is required. A combined mobility and traffic steering

eded in order to gain a complete f the proposed SON algorithm.

WLEDGMENT ese results has been carried out project and has received funding eventh Framework Programme greement n° 316384.

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