performance of lte self-optimizing networks uplink load balancing 2011

Performance of LTE Self-Optimizing NetworksUplink Load Balancing

Timo NihtilaMagister Solutions Ltd.,

Helsinki, FinlandEmail: [email protected]

Jussi TurkkaTampere University of Technology

Tampere, FinlandEmail: [email protected]

Ingo VieringNomor Research GmbH

Munich, GermanyEmail: [email protected]

Abstract—This paper presents a performance evaluation of anuplink load balancing algorithm for 3GPP Long-Term Evolution(LTE) Self-Optimizing/Organizing Networks (SON). The algo-rithm handles local overload situations in uplink by two differentstrategies: firstly passing load from overloaded cells to under-loaded cells and secondly by controlling UL interference throughUL power control (PC) parameter adjustment. The algorithm isdistributed so it works independently in each eNodeB (eNB).Every eNB includes a specific SON module which takes variousmeasurements from its own and the neighboring eNBs as an inputand gives handover orders and parameter change commands toits designated eNB as an output. In the paper we present adescription of the algorithm and evaluate its performance in alocally overloaded LTE network. The performance evaluationis done by the means of fully dynamic LTE system simulationtool comprising a detailed modeling of user equipment (UE)measurements, user mobility and handovers, traffic and radioresource management algorithms.

I. INTRODUCTION

LTE is a new high performance air interface for cellularnetworks. 3rd Generation Partnership Project (3GPP) finalizedLTE specification Release 9 in December 2009. Release 8included the first specifications of Self-Optimizing/OrganizingNetworks (SON) feature. SON is designed to significantlyimprove network management performance by automating theconfiguration, optimization and functionality of LTE, therebyincreasing efficiency as well as improving network perfor-mance and flexibility.

Release 9 included a load balancing (LB) SON solution toan eNodeB (eNB) [1]. With LB local overload situations canbe handled in a network by forcing users of an overloaded cellto perform a handover (HO) to a less loaded neighboring cell.

Mathematical framework of one proposed SON LB algo-rithm for downlink was presented in [2]. Its performance wasevaluated in [3] and was seen to result in clear gain in userhappiness. However, these papers focused only in downlink.Uplink was not considered in the algorithm. Due to the verydifferent natures of LTE UL and DL, the presented DL algo-rithm cannot be directly adopted in UL. This paper presentsthe algorithm adapted to work in UL. The performance of itis evaluated by fully dynamic system simulations.

II. ALGORITHM

The basic idea of the DL LB algorithm presented in [2],[3] is that selected cell edge user equipments (UEs) can bepassed from an overloaded but strong cell to a weaker butunderloaded cell and thus improve the user performance inboth cells. The assumption is that the underloaded cell canmore than compensate its weaker signal by allocating morePhysical Resource Blocks (PRBs) to the UE. The algorithmflow is basically as follows:

1) Collect measurements during a measurement period(SON period).

2) After the measurement period, detect an overload situa-tion in a cell.

3) Find the best LB HO candidate (UE-cell-pair).4) Execute LB HO.5) Optimize HO offset towards LB HO candidate cell.

In UL the basic functionality of the algorithm is the samebut it needs to be adapted considering the overload detectionand finding the best LB HO candidate parts. In addition,algorithm is added with an UL specific optimization feature,load adaptive UL power control (LAPC). LAPC has beenintroduced in [4].

In the following sections these adaptations to the algorithmare discussed.

A. Detecting the overload in UL

Overload in DL can be detected solely by calculating a”virtual load” ρ for each eNB, as introduced in [2], [3]. Virtualload of a cell c during a measurement period is the sumof virtual loads of the users it serves (which we will call”component loads” in the following):

ρc =Nc∑

u=1

GBRu

BRu∗ ARBu,c

SRBc, (1)

where GBRu is the guaranteed bitrate, BRu the realizedbitrate and ARBu,c the number of allocated PRBs of useru in cell c. SRBc is the total number of schedulable PRBs incell c during the measurement period and Nc is the numberof served UEs by cell c.

Component load is basically a calculational portion of theeNB scheduling resources, namely PRBs, that the user needs

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

to be allocated in order to be satisfied1. Thus, as mentionedin [2], virtual load can exceed 1, which is an indication of anoverload.

In DL it is assumed that all available PRB resources canbe used all the time by the scheduler. This works fine sinceunhappy users are basically always unhappy due to lack ofPRB capacity. In UL the situation is different: in additionto PRB capacity limitation, users can be power or controlchannel (i.e. max. number of scheduled users, MSU) limited.MSU limitation means that all free PRB resources are nottotally schedulable all the time i.e. sum of the latter part of theproduct in (1) is < 1 even though the resources are shared withmaximum number of UEs in every TTI. If some of the UEs arepower limited and unable to allocate the available bandwidthto be happy, then there are unhappy users at the same timewith unused PRB resources. As UL user unhappiness dueto power/MSU limitation does not increase virtual load, ULvirtual load can be < 1 and still there are unhappy users.

As a consequence, virtual load alone is not a sufficienttrigger for LB in UL. We need to distinguish the reasonsfor user unhappiness. In the following we will describe howadditional limitations due to power and control channels canbe considered in our algorithms. The theory behind thoselimitations has been introduced in [5]. In the following we willexplain how this can be practically handled in our simulations(and later on in reality).

1) Power limitation: User is not transmitting enough to behappy but due to it being in poor channel conditions (e.g. celledge) it cannot increase its transmission power and data rate.Assuming the user is connected to the best cell, a LB HOcannot make this user happy.

To detect power limitation, it is estimated what would bethe throughput of an unhappy user if the maximum numberof RBs supported by the PC of a user was used during themeasurement interval. If this virtual ”PC limited throughput”is still smaller than the GBR of the user, the user is consideredpower limited.

More specifically, ”PC limited throughput” during a mea-surement period is updated as follows. If a user is scheduledn RBs, PC limited virtual data is increased by

m/n ∗ B, (2)

where m is the current maximum number of RBs supported bythe PC of the user and B is the amount of correctly receiveddata.

If user is not scheduled, PC limited virtual data is increasedby

m/n′ ∗ B′ ∗ (1 − BLER), (3)

1It is generally assumed that a user satisfaction can be defined. This is truefor constant bit rate (CBR) users for whom some portion of the target bitrate can be set as the critical satisfaction limit, i.e. guaranteed bitrate (e.g.98 % of the CBR). If the bitrate of the user is below GBR, user is consideredunsatisfied.

where n′ is the number of RBs and B′ is the amount ofcorrectly received data in previous successful transmission.BLER is the UL block error rate target. The idea behind thisis that part of the virtual data is assumed to be lost due toerrors.

In the end of the measurement period, power limitation forthe user is assumed if both the actual throughput AND ”PClimited throughput” per measurement period are below GBR.

2) MSU limitation: If there are a lot of users in thenetwork willing to transmit data, the UL scheduler can runout of control channels, namely Physical Downlink ControlChannels (PDCCH), which are used to inform the users ofupcoming scheduling grants. If PDCCH resources run out,part of the users wanting to transmit are not scheduled atall. Most importantly, there can be unhappy users due to lackof PDCCHs at the same time there are a lot of free PRBs.In this case serious conflict between virtual load and actualload arises: because there are free PRB resources, virtual loadindicates there is no overload.

To determine MSU limitation, it is estimated what would bethe throughput of an unhappy user if the user was scheduledalso when it was not scheduled due to MSU limitation. If thisvirtual ”MSU unlimited throughput” is at least the GBR ofthe user, the user is considered MSU limited.

More specifically, ”MSU unlimited throughput” for a useris updated as follows. If user is scheduled, MSU unlimitedvirtual data is increased by B. If user is not scheduled dueto MSU limitation, MSU unlimited virtual data is increasedby B′ ∗ (1 − BLER). In the end of the SON period, MSUlimitation is assumed if the actual throughput < GBR AND”MSU unlimited throughput” >= GBR.

It should be noted that users ”PC limited throughput” is al-ways greater or equal than ”MSU unlimited throughput” sinceit assumes there is no MSU limit and maximum resources areavailable but ”MSU unlimited throughput” assumes only noMSU limit. Thus, if user is considered MSU limited, it is notconsidered to be power limited.

Finally, overload detection in UL is modified so that if ULvirtual load > 1 OR number of MSU limited users > 0, cell isassumed to be overloaded and LB HO procedure is triggered.

Note that this limitation is also there in DL, but it is muchless critical there since even a small number of scheduled userscan occupy the full bandwidth (no power limitation problem).

B. Consideration of power limitation in finding best LB HOcandidate

As LB HO function cannot do anything to improve theperformance of power limited users, in LB point of view,imposing users to situations where they may become powerlimited should be avoided at all costs. Thus, when search-ing for the best LB HO candidate, algorithm identifies andexcludes 1) power limited users and 2) users that probablybecome power limited if forced to hand over to a weaker cell.

C. Load adaptive UL power control

LB HO procedure passes users to worse cells in terms ofsignal quality, inherently increasing the probability of power

limitation in the network.The idea of LAPC, proposed in [4], is to minimize UL

interference by letting users transmit with lower power in lowload situations. In theory, minimizing UL interference hasa decreasing impact on the probability of power limitation,which makes it an essential addition to the algorithm alongsideLB HO function. However note that lower transmit powermeans smaller SINRs, so the users need to use a largerbandwidth to reach the same CBR. So indeed less interferenceper PRB is produced, but on more PRBs.

LAPC dynamically adjusts P0 of a cell according to its load.P0 is a parameter of UL PC algorithm which determines theTx power of each UE [6]. In general, P0 is the target receivelevel of the UE and by altering it, all UE transmission powersare adjusted simultaneously.

In our study, we use virtual load as an input for the LAPC.P0 for cell c is calculated by

P0,c = P initial0,c + 10 log10(ρc), (4)

where P initial0,c is an initial P0 of cell c.

III. SIMULATION SCENARIO AND ASSUMPTIONS

The used simulator consists of a fully dynamic systemlevel modelling utilizing exponential SINR link to systemlevel mapping [7]. Both E-UTRAN downlink and uplink aresimulated with a TTI (1 ms) resolution. Simulator has detailedmodeling of radio resource management (RRM9, mobility andhandovers as well as traffic models [8].

The simulation scenario is presented in Fig. 1. It consistsof 57 macro cells with ISD of 500 meters. Innermost 21 cellsof them are active (white cells) and outermost of them areinterferers (grey cells). A UE can establish a connection onlyto the active cells and the interferer cells only cause artificialinterference.

Fig. 1. Simulation scenario.

A local overload is created in the network by creating partof the users into a hotspot in the scenario (initial position ofthe hotspot is indicated by the dotted hexagon in the figure).The hotspot size matches the theoretical service area of asingle cell. Initially the hotspot is between cells 12 and 13.During the simulation the hotspot moves towards cell 44 in125 seconds. In case of a longer simulation time than this,the hotspot movement pattern is repeated from the start. Themovement of hotspot does not dictate the movement of theUEs; it only determines the creation positions of the users. Themovement of an individual user created during the simulationis random. Furthermore, hotspot does not limit the mobility ofthe UEs so a UE created inside the hotspot can move out ofit.

In Fig. 1 an example distribution of created user positionsduring a simulation are indicated with dots. The color ofthe dot indicates the creation time of the user: the greenerthe color, the earlier in the simulation the user is created.However, in order to gain statistical confidence to the results,each simulation was repeated 10 times with different usermobility random seeds. Thus exact user creation positions andmovements were different in each simulation. All the presentedresults are average values of 10 simulations, if not mentionedotherwise.

TABLE IPARAMETERS IN DL SIMULATIONS.

User bitrate 64 kbps 256 kbps 640 kbpsAvg background load [users/cell] 21.7 7.5 2.7Avg hotspot load [users] 259 90 31Simulation time 125 s 250 s 625 sInter-site distance 500 mDefault HO margin 3 dBTime to trigger 200 msMeasurement period 1 sGBR 98 % of user bitrateMSU per TTI 12UL scheduler ATB [9]Initial P0 -52 dBmPC slope α 0.6

Table I presents the most essential simulation parametersused in the simulations. The LB measurement period was rel-atively short (1 second). This was selected in order to increasethe speed of the simulations. In real life the measurementperiod before deciding to perform load balancing would surelybe longer than this.

Three different user target bitrates were simulated: 64, 256and 640 kbps bitrates. As higher bitrate user consumes higheramount of PRBs, the hotspot UE creation probabilities fordifferent bitrates were set so that there is approximately equalRB load in the hotspot at any given time. The amount of usersin the background (area surrounding the hotspot) is set so thatthe RB load is much smaller than in the hotspot, creatingenough optimization possibilities for the LB algorithm. So all3 cases are defined to be slightly above the capacity limit.

In our simulations maximum number of scheduled usersper transmission time interval (TTI) is set to fixed value12. In reality, MSU changes from TTI to TTI since it is

determined by how much scheduled users consume controlchannel elements. However, 12 users has been empirically seento be a good average value (i.e. 12 PDDCHs are available foruplink).

IV. SIMULATION RESULTS

Simulations were run with following optimization strategies:• a reference case (no LB nor LAPC used)• LAPC only (no LB)• LB + LAPC

Fourth case (LB only) is omitted because it is not reasonable.LB forcefully handovers users to worse cells, increasing theprobability of power limitation. As LB function is helplessagainst it, using only LB in UL is not beneficial.

Fig. 2. Example of virtual load of cell 29 with 256 kbps.

Fig. 2 presents an example virtual load of one cell (29) froma 256 kbps simulation. Load first increases and then decreasesas the user hotspot travels through cell 29. It is notable thateven when the hotspot is right on top of the cell, UL virtualload is always below 1 in the reference case. The situationis the same also in other cells. Still, there are a considerableamount of unhappy users in the network. The unhappinesscomes mainly from the fact that there are a lot of MSU limitedusers. Hence, majority of LB HOs are triggered by the MSUlimitation.

Interestingly virtual load is not notably or at all decreasedwhen LB is enabled. On the contrary, virtual load is actuallyhigher in some occasions. This can be explained by thefollowing chain of events: LB procedure handovers some usersfrom the MSU limited cell to neighbors. As a result, somescheduling slots are freed in the overloaded cell, which canthen schedule users that were previously not scheduled due toMSU limitation. Those users can be allocated more PRBs thanthe users that were handed over. This increases total resourceconsumption and eventually also virtual load. This event chainis probable since LB HOs fall usually to the most ”distant”users, which are most likely to be power limited and henceconsume very few PRBs.

Fig. 3. Example of P0 of cell 29 with 256 kbps.

Corresponding P0 behavior of cell 29 is shown in Fig. 3.Naturally in reference case P0 is constant but when LAPC isenabled, considerably lower P0 values are realized.

Fig. 4. Percentage of unhappy users.

The percentage of unhappy users with all simulated trafficsis presented in Fig. 4. The lower (red) part of a bar indicatesthe portion of unhappy users due to power limitation. Recallthat the number of users is higher for lower bit rates in orderto have the same degree of overload irrespective of the bitrate.

It can be seen that LB brings the highest benefit with lowbitrate but has a smaller effect to performance with highbitrates. LAPC on the other hand cannot increase the happinessof low bitrate users much but gives significant gain with highbitrates.

Low bitrate users have a smaller probability to be in a powerlimitation than high bitrate users. This is clearly visible in thelevel of power limitation from reference result bars in Fig. 4.LAPC seems to be the best weapon to tackle power limitation.On the other hand, LB procedure increases power limitation

rate slightly. This is as expected as LB HO users are forcedto worse cells. Nevertheless, the overall user unhappinessdecreases still as LB is used in conjunction with LAPC.

TABLE IINUMERICAL RESULTS.

Case Ref LAPC LAPC+LBAvg Std Avg Std Avg Std

64 kbpsHOs total [-] 1038 40.94 1034 36.17 1067 63.62Ping-pongs [-] 128 13.18 126 11.14 119 14.60LB HOs [-] - - - - 173 30.74LB HOs due to

- - - - 126 13.69MSU limit [-]No of calls 3884 8.47 3911 6.95 3933 7.98





Fig. 5. LB effect to HOs in percentage of all HOs.

In Fig. 5 the LB effect to HOs is presented. The resultsare relative to total number of HOs in LAPC only case.MSU limitation causes the majority of LB HOs in eachcase. The increase in total number of HOs is moderate withother bit rates than 256 kbps. This anomaly can be explainedby the high MSU limitation rate in 256 kbps case, whichcauses a high number of LB HOs. Ping-pong HO rate is notimpacted severely by the LB procedure which indicates thatthe measures taken in order to mitigate the probability of ping-pongs due to LB are effective.

In all cases selected optimization strategies (LB and LAPC)offer gain. The amount of gain and the most effective strategy

is dependent on the UL bit rate. With low and moderate userbit rates LB function improves performance more than mereLAPC. On the other hand, with high user bitrates the effectof LB is insignificant in comparison to LAPC, which is apowerful method to decrease power limitation in the network.

V. CONCLUSIONS

In this paper we present a simple description and a per-formance evaluation of a distributed UL SON algorithm. Al-gorithm optimizes the performance of users involved in localoverload situations by two main strategies: Load balancing andload adaptive power control. LB strategy was mostly effectivewith low bit rate UL services. High bit rate UL service usershave a high probability to suffer from power limitation. LAPCwas seen to benefit high bit rate users the most.

In theory, as LB strategy forces users to connect to worsecells, it increases the power limitation problem of high bitrate users. This effect was confirmed by the simulations. Theresults indicate that LB strategy is not necessary with highbit rate UL services and without concurrent power controlparameter adaptation LB strategy might even hurt the ULperformance. On the other hand, LAPC was seen to be moreor less beneficial with all tested bit rates.

Finally we have seen that, in contrast to downlink, PDCCHlimitation can be a further reason to trigger UL load balancingprocedures, even if radio resources are unused.

VI. ACKNOWLEDGEMENTS

The authors would like to thank colleagues from Nokia andNokia Siemens Networks for their support with the algorithmand the simulator.

REFERENCES

[1] Telecommunication management; Self-Organizing Networks (SON) PolicyNetwork Resource Model (NRM) Integration Reference Point (IRP):Information Service (IS), 3GPP, Technical Specification Group Servicesand System Aspects TS32.522, Rev. v9.0.0, March 2010.

[2] I. Viering et al., “A mathematical perspective of self-optimizing wirelessnetworks,” in Proceedings of the IEEE International Conference onCommunications, Dresden, Germany, June 2009.

[3] A. Lobinger et al., “Load balancing in downlink LTE self-optimizing net-works,” in Proceedings of the 71st IEEE Vehicular Technology Conference(VTC 2010-Spring), Taipei, Taiwan, May 2010.

[4] R. Mullner et al., “Enhancing uplink performance in UTRAN LTEnetworks by load adaptive power control,” European Transactions onTelecommunications, 2010, DOI:10.1002/ett.1426.

[5] I. Viering et al., “Efficient uplink modeling for dynamic system-levelsimulations,” EURASIP Journal on Wireless Communications and Net-working, February 2010.

[6] Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layerprocedures (Release 9), 3GPP, Technical Specification Group RadioAccess Network TS36.213, Rev. v9.2.0, June 2010.

[7] K. Brueninghaus et al., “Link performance models for system levelsimulations of broadband radio access systems,” in Proceedings of thePersonal, Indoor and Mobile Radio Communications (PIMRC05), vol. 4,Berlin, Germany, September 2005, pp. 2306–2311.

[8] P. Kela et al., “Dynamic packet scheduling performance in UTRA longterm evolution downlink,” in Proceedings of the International Symposiumon Wireless Pervasive Computing (ISWPC’08), Santorini, Greece, May2008.

[9] F. D. Calabrese et al., “Adaptive transmission bandwidth based packetscheduling for LTE uplink,” in Proceedings of the 68th IEEE VehicularTechnology Conference (VTC 2008-Fall), Calgary, Canada, September2008.

performance of lte self-optimizing networks uplink load balancing 2011

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