IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018 2205

Adaptive Mobility Load Balancing Algorithmfor LTE Small-Cell Networks

Md Mehedi Hasan , Sungoh Kwon , Senior Member, IEEE, and Jee-Hyeon Na, Member, IEEE

Abstract— Small cells were introduced to support high data-rate services and for dense deployment. Owing to user equipment(UE) mobility and small-cell coverage, the load across a small-cellnetwork recurrently becomes unbalanced. Such unbalanced loadsresult in performance degradation in throughput and handoversuccess and can even cause radio link failure. In this paper,we propose a mobility load balancing algorithm for small-cellnetworks by adapting network load status and considering loadestimation. To that end, the proposed algorithm adjusts handoverparameters depending on the overloaded cells and adjacent cells.Resource usage depends on signal qualities and traffic demandsof connected UEs in long-term evolution. Hence, we define aresource block-utilization ratio as a measurement of cell loadand employ an adaptive threshold to determine overloaded cells,according to the network load situation. Moreover, to avoid per-formance oscillation, the impact of moving loads on the networkis considered. Through system-level simulations, the performanceof the proposed algorithm is evaluated in various environments.Simulation results show that the proposed algorithm providesa more balanced load across networks (i.e., smaller standarddeviation across the cells) and higher network throughput thanprevious algorithms.

Index Terms— Small-cell network, mobility load-balancing,self-organizing network, measurement reporting, handover, cellindividual offset, load estimation.

I. INTRODUCTION

THE demand for mobile broadband services with higherdata rates and improved quality of service (QoS)

increases rapidly with the increases in smart devices andapplications for information and communications technolo-gies (ICT). The expected demand for wireless data in 2021 is49 exabytes, which is seven times the demand in 2016 [1].Consequently, mobile networks need to prepare for massivetraffic growth over the next decade.

Manuscript received December 8, 2016; revised May 26, 2017 andNovember 6, 2017; accepted December 29, 2017. Date of publicationJanuary 12, 2018; date of current version April 8, 2018. This work wassupported in part by the Basic Science Research Program through the NationalResearch Foundation of Korea, funded by the Ministry of Science, ICTand Future Planning, under Grant NRF-2015R1A1A1A05001069, and in partby the Institute for Information and communications Technology Promotion(IITP) Grant through the Korea Government (MSIT) (No. 2014-0-00282,Development of 5G Mobile Communication Technologies for Hyperconnectedsmart services). The associate editor coordinating the review of this paperand approving it for publication was B. Hamdaoui. (Corresponding author:Sungoh Kwon.)

M. M. Hasan and S. Kwon are with the School of ElectricalEngineering, University of Ulsan, Ulsan 44610, South Korea (e-mail:hasan3345@gmail.com; sungoh@ulsan.ac.kr).

J.-H. Na is with the Radio Access Network S/W Research Team, Electronicsand Telecommunications Research Institute, Daejeon 34129, South Korea(e-mail: jhna@etri.re.kr)

Color versions of one or more of the figures in this paper are availableonline at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TWC.2018.2789902

To support the data demand, as well as to increase networkcapacity, the small cell was introduced and will play an impor-tant role in the future fifth-generation (5G) network [2], [3].A small cell is a low-powered, low-cost radio-access node witha range that varies from ten to several hundred meters [4], [5].Although the small cell was initially designed to extend servicecoverage by serving shadow areas of macro coverage, densedeployment of small cells in a wireless network can signifi-cantly increase the capacity and throughput of the network [6].Hence, the small cell is now considered a prominent solution,and has become an integral part of future networks in orderto support the upcoming data demand.

The deployment of small cells is growing fast, especiallyin large shopping malls, stadiums, universities, multi-storeyresidential apartments, and offices [7]. Based on the policiesof service providers, the deployment of small cells can beplanned or unplanned [8]. Due to their low cost, subscribersmay have their own small cells and can deploy themanywhere, even to turn on and off at any time. Therefore,more or fewer small cells will be mostly randomly distributedthroughout the network.

Even if the deployment of small cells is planned, due totheir low service area, the small-cell network is vulnerable tothe mobility of user equipment units (UEs). Since the smallcell has low transmission power, only a few UEs can beserved by each small cell, and mobility of UEs leads to anunbalanced load across the network. Moreover, the preferencesof small cells during cell selection loads more traffic ontothem, which also causes an overloaded traffic situation. Suchunbalanced loads over the network results in performancedegradation in capacity and handover success rates. When UEstry to move onto overloaded small cells, even if neighboringcells remain underloaded, the deficit in resources results inhandover failures or poor QoS. As a result, some cells cannotsatisfy the QoS requirements, while other neighboring cellresources remain unused. Hence, appropriate configuration andmanagement of the network is required.

To overcome those unbalanced load issues, as well asto improve cellular network performance, the self-organizednetwork (SON) was proposed to configure and optimize thenetwork without human intervention [9]. Based on the SONalgorithm location, the SON is classified as centralized, dis-tributed, or a hybrid. In a centralized SON, all functionalityis located in a separate subsystem. On the other hand, fora distributed SON, all the functionality is located in basestations. A hybrid SON is a combination of centralized anddistributed SONs, where some of the functionality is locatedin base stations, and the rest of the functionality is located

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2206 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

in the central SON subsystem. A SON comes with severalfeatures, like mobility robustness optimization, mobility loadbalancing (MLB), RACH optimization, interference manage-ment, and so on [10]. MLB is responsible for load balancingin the network. The MLB algorithm for a SON optimizesthe handover parameters and achieves load balancing withoutnegatively affecting the user experience.

Previous work researched the mobility load-balancing prob-lem from various aspects. A joint MLB and inter-cell interfer-ence coordination (ICIC) algorithm [11] solves MLB and ICICproblems together. Considering the link condition of targetcells, a rule-based cell individual offset (CIO) optimizationprocess was presented [12] to achieve a balanced network.Both of the algorithms considered nth-tier non-adjacent neigh-bors to overcome the load-balancing issue, with a fixed thresh-old to determine overloaded cells. Since the authors consideredonly the load status of the target cell (neglecting signal quality)to find appropriate targets, inappropriate target cell selectionwithout considering the service quality from the handover ofUEs utilizes more resources or drops the service. Moreover,due to the fixed threshold, the algorithms cannot performload balancing adaptive to varying network environments.In [13], a load balancing algorithm was proposed consideringsmall cells and multi-traffic UEs in the network. However,the algorithm also adopted a fixed threshold to determineoverloaded cells. An algorithm proposed in [14] uses thenetwork flow theory, which includes adjacent and non-adjacentoverloaded neighborhood cells in the balancing process. Thealgorithm depends highly on underloaded neighboring cells,and cannot ensure load balancing among neighborhood cellsin a peak-load situation, when most of the cells are consideredoverloaded. However, these previous efforts are not able toprovide effective load balancing under various load situations,such as underloaded neighborhood situations and highly over-loaded neighborhood situations. Hence, it is necessary to studya load-balancing algorithm that can adapt to various networkenvironments and avoid load ping-pongs.

This paper proposes an algorithm for MLB across smallcells by conditionally adjusting the handover parameters.Unlike the traditional fixed threshold approach, the algorithmuses an adaptive threshold to find overloaded cells. By intro-ducing maximum moveable load for the overloaded cells,the algorithm restricts the load released from the overloadedcells to the neighboring cells. In moving the load of theUEs, the algorithm estimates load status of the currentlyoverloaded cells and candidate target cells and decides UEhandovers in order to effectively distribute the load and avoidperformance oscillation. In order to shift the candidate UEs,the CIOs of the serving and target cells are adjusted based onmeasurements reported by UEs. Therefore, this approach isnot limited only to small-cell networks but is also applicableto macro cells and heterogeneous networks. The simulationresults show that the proposed algorithm can ensure balancedloads.

The remainder of the paper is organized as follows.Section II describes the system model and assumptions wemade. Section III overviews load balancing-related handoverparameters, while Section IV introduces a threshold adaptive

Fig. 1. Access network architecture with a centralized SON.

to the network load to decide overloaded cells, and a loadestimate to consider the impact of the load to be moved on theserving cell and its neighboring cells. The proposed algorithmis explained in Section V, followed by system evaluationresults in Section VI. Section VII concludes the paper.

II. SYSTEM MODEL AND PROBLEM FORMULATION

A. System Model

In this paper, we consider a set of small cells N , denoted asN = {1, · · · , N} over which the load balancing is carried out,and a centralized SON (cSON), as shown in Fig. 1. The smallcells operate in open access mode, and interconnect with eachother via X2 interface [15]. When UEs are handed over fromone small cell to another, the small cells exchange handover-related information through the X2 interface. The cSON isconnected to all the small cells via S1 interface [16]. Based oninformation gathered from small cells, the cSON periodicallyoptimizes and updates handover parameters of the small cellsto balance the load across the network.

In Long Term Evolution (LTE), the smallest unit of aphysical resource block (PRB) is defined as 180 kHz widein frequency and 0.5 ms long (1 slot) in time [17]. Thefrequency is divided into 12 sub-carriers of 15 kHz, and oneslot contains a group of seven orthogonal frequency-divisionmultiplexing (OFDM) symbols (normal cyclic prefix).1

A resource element is defined as one sub-carrier with onesymbol period. Hence, a resource block is composed of12 × 7 resource elements. In other words, a resource blockis a group of seven OFDM symbols; that is, 12 × 15 kHzwide in frequency. The scheduling time unit for allocatingresources is 1 ms (2 slots), referred to as the transmissiontime interval (TTI), so we consider a discrete system timeof 1 ms resolution throughout this paper. The total number ofavailable physical resource blocks of a cell is set according tothe system bandwidth [18]. For example, the total number ofPRBs is 100 for a system bandwidth of 20 MHz.

B. Measurement of Cell Load

To optimize the performance of the network through loadbalancing, it is important to find a proper measurement by

1The number of OFDM symbols depends on the cyclic prefix length, whichis configured by higher layer [17]. Under a normal cyclic prefix configuration,the number of symbols is seven per slot whereas the number is six under anextended cyclic prefix configuration.

HASAN et al.: ADAPTIVE MLB ALGORITHM FOR LTE SMALL-CELL NETWORKS 2207

which the load in a cell can be represented accurately. Theload can be the radio load, the transport network load, or eventhe processing load or the number of users served by a cell.To determine whether a cell is overloaded or not, in this paper,resource block utilization ratio (RBUR) calculated from thephysical resource blocks (PRBs) of the cells is considered.The RBUR is defined as the ratio of PRBs used by a cellto the total number of PRBs in the cell. For a given timeduration, T , the average RBUR of small cell i at time t isexpressed as

RBU Rti =

1

T · NP R B

∑

τ∈(t−T ,t)

RBτi , (1)

where RBτi and NP R B are the number of allocated resource

blocks and the total number of resource blocks in the cell attime τ , respectively.

A higher average RBUR for a small cell indicates that thecell has a higher load to serve and fewer available resources.If the RBUR of a cell reaches 1, the resources of the cell arecompletely depleted, and UEs moving to that cell will eitherbe dropped or will experience low throughput. In this paper,we did not consider any kind of call admission control (CAC).2

Hence, when a new UE arrives in an overloaded cell, it willbe accommodated by that cell, but the per-UE throughput inthe cell will be impacted. Therefore, a shift is necessary toreduce the load of the highly loaded cell by moving some ofthe UEs from it to a lightly loaded cell.

C. Problem Formulation

The objective of this paper is to balance the load acrossthe cells and improve network performance. To that end, ourproblem is to reduce the load variance among the cells bymoving edge UEs based on measurements. When the averageRBUR of a network of N small cells at time t during timeperiod T is

RBU RtNet =

1

N

∑

i∈NRBU R

ti ,

and the standard deviation of RBURs among the cells in thenetwork is

σ(RBU Rt) = 1

N

√∑

i∈N(RBU R

ti − RBU R

tNet )

2, (2)

then the problem can be expressed as

minimize σ(RBU Rt)

subject to 0 ≤ RBτi ≤ NP R B , ∀i ∈ N , ∀τ ∈ (t − T, t).

(3)

In order to shift the load from an overloaded cell to anunder-loaded cell and balance the network, we adaptivelyadjust handover parameters, and hand UEs over by consid-ering the impact of the shifting load on the network. In the

2A CAC in a cell ensures the QoS of existing users by rejecting usersincoming to the cell due to instantaneous cell load in a dynamic environment.The sacrifice of the handover users degrades the overall network performanceeven if the problem can be solved by load balancing.

following sections, we explain the handover parameters andthe estimated shifting load, and we propose a load-balancingalgorithm. For notational simplicity, we drop time t from thefollowing section, unless otherwise stated.

III. MOBILITY CONTROL PARAMETERS

To perform various functionalities, such as handover, mobil-ity management, capacity and coverage optimization, auto-matic neighbor relations, and so on, each cell requires themeasurements of signal qualities, reference signal receivedpower (RSRP), or reference signal received quality (RSRQ)from the neighboring cells. To that end, a network letsUEs report signal quality to their serving cells either peri-odically or as event-based reports. In periodic measurementreporting, UEs are required to periodically report the measuredsignal quality to the serving cell. This kind of measurementreporting increases signaling overhead. To avoid high signalingoverhead problems, LTE provides a set of measurement reportmechanisms to be performed by UEs [19]. Each of thosepredefined measurement report types is called an event. Themeasurement report includes the signal quality informationfrom the serving cell and the neighboring cell. In this paper,event-driven measurement reporting is considered in order toexecute handovers and also to get edge UEs’ information.

A. Events in LTE

LTE specifies eight events for measurement reporting:six events (A1, A2, A3, A4, A5, and A6) for intra-LTEmeasurements, and two events (B1 and B2) for inter-RATmeasurements [20]. As we are considering intra-LTE loadbalancing, we forgo inter-RAT measurement events.

UEs report intra-LTE event-based measurements to theserving cell when the criteria for a particular event hasbeen satisfied for a pre-specified time, called time to trig-ger (TTT) [21]. The triggering quantity and the reportedmeasurement for the events can be either RSRP or RSRQ.All the intra-LTE events (except A3 and A6) are triggeredwhen compared with predefined thresholds. However, eventsA3 and A6 are triggered based on real-time signal qualitycomparisons between cells. Among the threshold-based events,a UE triggers event A1 when the signal quality of the servingcell becomes better than a pre-defined threshold. Event A2 istriggered when the signal quality of the serving cell becomesworse than a given threshold. When the signal quality of aneighboring cell becomes better than a defined threshold, eventA4 is triggered. However, event A5 is triggered by a UE whenthe serving cell becomes worse than a defined threshold anda neighboring cell becomes better than another threshold.

On the other hand, event A3 is defined as a triggering eventwhen a neighboring cell is offset better than the serving cell;under carrier aggregation, event A6 is triggered by a UE whenthe signal quality of a neighboring cell is offset better thanthe secondary cell. The primary cells are the cells selectedby the UEs during the initial connection establishment. Thesecondary cells can be added or removed by the primary cellsdepending on the service demands of the UEs while carrier

2208 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

aggregation is considered. In this paper, A3 and A4 event mea-surements are used to trigger a handover and select candidateUEs for handover, respectively and RSRP is assumed reportingsignal quality for measurements.

B. Load Shifting Using A3 Event-Based Handover

Event A3 is triggered, and the UEs report measurementresults to the serving cell when the signal of a neighboring cellis offset better than that of the serving cell. Since the triggeringlogic of event A3 depends on relative signal quality of aneighboring cell, event A3 is the most suitable, even among theintra-LTE events, for finding the best neighboring cell. Hence,event A3 is widely used for triggering handovers in wirelessnetwork [22], [23]. The small cell decides to trigger a handoverif the A3 triggering criteria remain satisfied for longer thanthe TTT. The condition for an event A3 measurement reportis as follows:

Mn + O f n + Ocn − H yst > Mp + O f p + Ocp + O f f,

(4)

where Mn and Mp denote the average RSRP values; O f nand O f p are the frequency-specific offsets; Ocn and Ocpare the cell individual offsets for the target and serving cells,respectively; H yst is the hysteresis parameter; and O f f is theA3 event offset between the primary and target neighboringcells. In this paper, we forgo O f n and O f p because they areused for inter-frequency handover.

By changing the values of Ocn, Ocp, and O f f , the han-dover decision area can be shifted, and the handover canbe intentionally delayed or hastened according to the loadsituation of the serving and target cells. If the value forOcn in a cell for a particular neighbor is increased, or thevalue of Ocp is decreased, then the cell range is reducedvirtually, and UEs of the cell can be handed over to theneighboring cell to reduce the load. If the value for Ocn isdecreased or the value of Ocp is increased, the cell will attractUEs from the neighboring cell to increase the cell load. Forexample, in Fig. 2 (a), Cell B is overloaded with four UEsand its neighboring cells, A and C, have a light load with twoUEs. There are edge UEs (UEs 3 and 4) at the borders ofcells A and B, but no UEs are at the borders of cells B and C.Hence, by decreasing Ocn for Cell A, UE 3 hands over fromCell B to Cell A, and the load of the network is balanced.Although changing Ocp can delay or hasten the handover,it has an impact on the cell range of all the neighboringcells. Hence, for the sake of load-shifting with a particularneighboring cell, the parameter Ocn is more suitable foradjustment.

After a handover based on an A3 event, UEs move toneighboring cells and load at the serving cell is lessened.However, the system does not have information on edge UEsprior to handover for load balancing. To balance the load,the system needs information about UEs that are on theoutskirts of the cell and that can be moved. Frequent periodicmeasurement can provide accurate UE distribution but bringsredundant overhead, which deteriorates network performance.Hence, we utilize A4 events to gather edge-UEs’ information.

Fig. 2. The basic working procedure of the MLB algorithm: (a) optimizationof A3 event parameters to achieve load balancing, and (b) collecting candidateUEs and desired target cell information by setting the A4 threshold.

C. Finding Edge UEs to Shift Using A4 Event

The SON achieves load balancing by transferring outskirts’UEs in overloaded cells to relatively low-loaded neighboringcells. To that end, the SON requires information about edgeUEs in the overloaded cells. Since event A4 is triggered whenthe RSRP of a neighboring cell becomes better than a providedthreshold, it is the appropriate for collecting outskirts’ UEsinformation from a cell. Using event A4, all the small cellsin the network collect the outskirts’ UEs’ information andshare it with the SON. The criterion for triggering event A4 isexpressed as,

Mn + O f n + Ocn − H yst > T hresh, (5)

where T hresh is event A4’s threshold. UEs that satisfy theconditions in (5) will report measurements, including signalstrength (or quality) for the serving cell and for the neighbor-ing cell. Hence, by setting a reasonable T hresh and selectingonly those UEs that report measurements, a small cell canobtain edge-UEs’ information and list candidate UEs to bemoved for load balancing.

For example, in Fig. 2 (b), UE 3 is outside the A4 eventboundary of Cell B and reports measurements. When CellB is overloaded, with the measurement report from UE 3,Cell B can hand UE 3 over to Cell A by reducing Ocn forCell A. Hence, each small cell i makes a set, Ei , of edgeUEs based on A4 event reports. The centralized SON gathersedge-UEs’ information E from all the small cells, which isE = {E1, · · · , EN }.

IV. ADAPTIVE THRESHOLD AND LOAD ESTIMATION

To balance load over the network, we need a thresholdto decide whether cells are overloaded or not, and we needtransfer-load estimates to ensure system stability.

A. Adaptive Threshold

Previous work introduced a fixed threshold to detect over-loaded cells [11], [12], [22], [24]. However, the fixed thresh-old is not applicable to all scenarios. For example, if the

HASAN et al.: ADAPTIVE MLB ALGORITHM FOR LTE SMALL-CELL NETWORKS 2209

MLB has a low threshold and all cells have more load thanthe threshold, the previous MLB algorithms do not work.Although all the cells exceed the threshold, the varianceof loads among the cells may be large enough for loadbalancing. If the threshold is too high, the algorithm willhardly ever find an overloaded cell for load balancing. The loadsituation of a network also changes over time and space. As aresult, a fixed threshold to identify an overloaded cell is notreasonable. Therefore, to identify overloaded cells for differentload situations, an adaptive threshold, T h Adapt , is introduced,which is defined as

T h Adapt = max{

RBU RNet , T hI nit}, (6)

where T hI nit is a pre-defined acceptable load threshold, whichdetermines whether the network requires load balancing or not.If at least one cell’s load status exceeds T hI nit , the algorithmwill initiate load balancing. The value of T hI nit should bekept minimum in order to proceed to load balancing.

B. UE Load Estimation

Required resource blocks (RBs) to serve edge UEs beforeand after handover are different, since UEs experience dif-ferent signal qualities (RSRP or RSRQ) from different cells.In order to balance loads, the algorithm determines the loadof the edge UEs to be moved from the overloaded cell andthe load to serve the edge UEs at the target neighboring cellsbefore performing the handover.

From the Shannon formula [25], channel capacity C isdefined as C = B · log(1 + θ), where B is the trans-mission bandwidth and θ is the signal-to-interference-plus-noise ratio (SINR). The load-balancing algorithm performshandovers of UEs that is located in the edge areas of cellsand that experience low SINRs of receiving signal quality.In the low SINR regime,3 by Taylor series expansion [26],the channel capacity can be approximated as

C � B · θ, (7)

For a given SINR from small cell i and required capacity Creq ,the required number of RBs for UE e is inversely proportionalto the SINR, which can be defined as

θ = Ps

Pi + Pn,

where Ps is the power of the signal, Pi is the averageinterference caused as a result of collisions between resourceblocks that are utilized by multiple cells simultaneously [27],[28] and Pn denotes the background noise.

Signal power Ps is measured over 12 sub-carriers in theentire bandwidth over NP R B resource blocks, and is thennormalized to a single sub-carrier bandwidth [29], [30].As RS RP is the average power of resource elements thatcarry cell-specific RS over the entire bandwidth, Ps can be

3In wireless LTE networks, the SINR at the cell edge area remains low, andthe approximation of (7) is assumed. In the simulation section, we providethe radio environment maps of our simulation environments in Fig. 3, whichshow that the SINR at the edge area follow the assumption.

modeled using the RS RP and per-antenna sub-carrier activityfactor x as

Ps = x · 12 · NP R B · RS RP.

For a full-load transmission when x = 1, SINR can beexpressed as

θ =(

12 · NP R B

Pi + Pn

)RS RP.

With identical interference and noise, SINR is linearly pro-portional to RSRP at the edge area [31]. Since handoverstake place at the edge of the cells, the UEs at the cell edgeexperience almost identical interference and noise.

Hence, for a given number of RBs (NP R B ), let us denotethe average load to serve edge UEs e ∈ Ei of small cell ias ρ(i,e). Then, the estimated load, ρ( j,e) to serve the edgeUEs of small cell i at neighboring cell j can be expressed by

ρ( j,e) ≈ ρ(i,e)Ms(i,e)

Mn( j,e), (8)

where Ms(i,e) and Mn( j,e) are the measured RSRPs of servingsmall cell i and neighboring small cell j , respectively. Basedon edge-UEs and their load information gathered from smallcells, the SON can periodically compute the average load ofedge UEs, which is expressed as

ρedge = 1

‖E‖∑

i∈N

∑

e∈Ei

ρ(i,e), (9)

where ‖E‖ is the total number of UEs in set E .

V. PROPOSED ALGORITHM

This section introduces a mobility load-balancing algorithmfor LTE small-cell networks, which adapts to the network loadstatus and considers the impact of the UEs loads to be movedon the existing network load in order to avoid ping-pongs dueto the moved load. The proposed algorithm is composed oftwo parts: information gathering and load balancing.

A. Information Gathering

In the information-gathering routine, the SON gathers infor-mation on the UEs that are available to move and that arelocated at the borders of the small cells. To that end, the SONadjusts T hresh in Equation (5) for all small cells (based onA3 event measurement reports of the neighboring cells) andgathers information on UEs in the edge areas of their servingcells and on the neighboring cells of the UEs that are best forhandover.

Based on the A3-event measurement reports during a period,the algorithm establishes a set of UEs, U = {U1, · · · ,UN },where Ui is the set of UEs that report measurements to cell i .To adjust a threshold for A4 of each cell, the algorithmaverages the serving cell RSRPs reported by UEs at each cell,which is expressed as follows, for small cell i :

Msi = 1

‖Ui‖∑

u∈Ui

Ms(i,u), ∀i ∈ N . (10)

2210 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

After averaging the RSRPs at which A3 events are triggeredin a serving cell, the A4 threshold of small cell i is set to theaverage of Msi of the neighboring cells, which is expressedas

T hreshi = 1

‖Bi‖∑

u∈Bi

Msi , (11)

where Bi is a set of neighboring cells of cell i , which arereported by UEs in Ui . Hence, the A4 thresholds are adaptivelyadjusted to the updates of A3-event parameters.

After setting adaptive thresholds for A4 events T hreshi

∀i ∈ N , the SON then collects measurement reports under theA4 event. UEs reporting an A4 event are considered candidatesfor which the load can be shifted to a neighboring cell tomitigate the load of the serving cell. The SON establishes adatabase based on measurement reports triggered by A4 eventsfor each cell: UEs, RSRPs (Mss and Mns) of serving cellsand neighboring cells, and from the neighboring cells. LetEi = {e(1)

i , e(2)i , · · · , e

(‖E i‖)i } denote the set of edge UEs that

report A4 event measurements to cell i and which are sortedin decreasing order of RSRP (Mn) of the neighboring cell.A set Ti = {τ (1)

i , τ(2)i , · · · , τ (‖E i‖)

i } is denoted as the set ofneighboring cells reported under A4 event by the edge UEsto serving cell i .

For example, edge UE e(2)i ∈ Ei reports the RSRP of

a neighboring cell, τ(2)i , to serving cell i , and the RSRP

of e(n)i is greater than or equal to the RSRP of e(m)

i whenn < m. The routine for gathering information is summarizedin Algorithm 1.

Algorithm 1 Information Gathering1: Get A3 measurement reports2: Compute the average Msi in (10) for i ∈ N3: Compute T hreshi in (11) for i ∈ N4: Set T hreshi as A4 threshold for Cell i5: Get A4 measurement reports6: Update candidate UE Ei information of Cell i

B. Load Balancing

In the load-balancing routine, the SON periodically per-forms load balancing by moving UEs from highly loadedcells to lightly loaded neighboring cells based on the averageRBURs and estimated load to move. First, the centralizedSON receives RBUR information from all the small cells andcalculates the average RBUR of edge UEs, ρedge, as (9). Next,the SON sorts all the small cells in decreasing order of RBUR.

After sorting RBURs, the SON algorithm compares themaximum RBUR, RBU Rmax , of the list with predefinedthreshold T hI nit in order to determine if the network needsload balancing or not. If the maximum RBUR of a small cellis greater than the threshold, the network is considered to beoverloaded, and the SON algorithm performs load balancing.

As mentioned in Section IV-A, according to the load overthe network, loads to move to other cells are different. Hence,we set threshold T h Adapt adaptive to network load status,

as in (6), in order to determine relatively overloaded cellsin the network. If the average load across the network,RBU RNet , is greater than the prefixed threshold, T hI nit ,the adaptive threshold is set to the network average load.Otherwise, the threshold is set to T hI nit . The algorithmestablishes the set O of overloaded cells such that RBU Ro ≥T h Adapt for o ∈ O and O ⊂ N . Therefore, to balance thenetwork load, the algorithm sequentially takes the overloadedcells from O in the order of cell load, and lessens the loadto underloaded neighboring cells by moving UEs from theoverloaded cells.

During the load-balancing procedure, a moved load maymake a underloaded cell overloaded, which induces ping-pongs among multiple cells, which results in an infinite loop.To avoid such ping-pongs, we define a lower bound for cellloads RBU Rlow, which is defined as

RBU Rlow = RBU Rnet − 1

2ρedge.

Hence, after balancing the load, the overloaded cells do nothave an average load lower than the lower bound.

For an overloaded cell in O, the algorithm performs thefollowing procedure. First, the algorithm computes the max-imum load of overloaded cell o ∈ O to move, referred to asthe moveable load, which is expressed as

ρo = RBU Ro − RBU Rlow.

In other words, the moveable load is the excessive loadof the cell with respect to the lower bound. If there is noload restriction for movement, an overloaded cell may shiftan unconscionable amount of the load to become an under-utilized underloaded cell, and a neighboring cell becomesoverloaded. Moreover, the algorithm may enter an infinite loopdue to ping-pongs.

After computing the moveable load (ρo) of the overloadedcell, o ∈ O, and initializing estimated loads of cells ( RBU Ri s)to RBU Ri s for all i ∈ N , the algorithm then finds the possiblecandidate UEs in Ei of that particular overloaded small cell.To that end, for each of the edge UEs, e(n)

o ∈ Eo, the algorithmcomputes the load contribution in the present serving cell,o ∈ O, as ρ

(o,e(n)o )

and estimates the load ρ(τ

(n)o ,e(n)

o )in the

neighboring cell, τo, as in (8).The algorithm then checks the conditions below:

ρo > ρ(o,e(n)

o )(12)

RBU Rnet > RBU Rτ

(n)o+ ρ

(τ(n)o ,e(n)

o )(13)

RBU Ro − ρ(o,e(n)

o )> RBU R

τ(n)o+ ρ

(τ(n)o ,e(n)

o )(14)

Here, condition (12) restricts release of the load from over-loaded cell o so that cell o cannot become an underutilizedlow-loaded cell. If condition (12) is ignored, the overloadedcell will release the extra load to become underloaded, and theneighbor will become overloaded, which leads the procedureinto an infinite loop of load balancing. By comparing theestimated after-handover load with the average network load,conditions (13) and (14) ensure that the target neighboringcell will not become overloaded and the load status of theoverloaded cell remains higher than the target neighboring cell.

HASAN et al.: ADAPTIVE MLB ALGORITHM FOR LTE SMALL-CELL NETWORKS 2211

TABLE I

NOTATIONS USED IN THE PROPOSED MLB ALGORITHM

If conditions (13) and (14) are ignored the target cell willreceive extra load from the overloaded neighbors and willbecome overloaded.

If the three conditions are satisfied, UEs e(n)o is accepted for

handover to the neighboring cell, and related cell individualoffsets are updated as follows:

C I O = M(o,e(n)

o )− M

(o,τ(n)o )+ H ysto +� (15)

Ocn(o,τ

(n)o )= C I O (16)

Ocn(τ

(n)o ,o)

= −C I O (17)

where M(o,e(n)

o )is the RSRP of overloaded cell o serving

UE e(n)o , M

(o,τ(n)o )

is the RSRP of target cell τ(n)o measured

by UE e(n)o , H ysto is a hysteresis parameter of cell o, and

� is an increment step specified by LTE [19]. The algorithmsymmetrically updates the cell individual offsets of both theserving and target cells at the same time in (16) and (17) inorder to maintain the level of ping-pongs.

Finally, the algorithm updates the load information of theserving and target cells as follows:

RBU Ro = RBU Ro − ρ(o,e(n)

o )

RBU Rτ

(n)o= RBU R

τ(n)o+ ρ

(τ(n)o ,e(n)

o )

ρo = ρo − ρ(o,e(n)

o )

and goes for the next candidate UE of the overloaded smallcell.

The above process continues for all the overloaded cells inthe network according to the list. The notations used in theproposed MLB algorithm are summarized in Table I, and theprocess for load balancing is summarized in Algorithm 2.

VI. PERFORMANCE EVALUATION

A. Simulation Environments

To evaluate the performance of the proposed algorithm,we performed system-level simulations with three differentnetwork scenarios using ns-3 [32]. We considered a homo-geneous network with small cells only. There are 10 smallcells in each of the network scenarios. In the first and second

Algorithm 2 Load Balancing1: Get RBU R information from all small cells N2: Calculate ρedge of edge UEs3: Sort N according to RBUR and find RBU Rmax

4: if RBU Rmax > T hI nit then5: RBU RNet ← mean(RBU Ri) where i ∈ N6: T h Adapt ← max(RBU RNet , T hI nit )7: while RBU Ri ≥ T h Adapt do � i = {1, 2, · · · N}8: O includes small cell i � O ⊂ N9: end while

10: for all o ∈ O do11: ρo = RBU Ro − RBU Rnet + 1

2ρedge

12: Get candidate Eo � Algorithm I13: RBU Ro ← RBU Ro

14: for all e(‖Eo‖)o do

15: Compute ρ(o,e(n)

o )16: Estimate ρ

(τ(n)o ,e(n)

o )17: if (12), (13), and (14) are satisfied then18: C I O ← M

(o,e(n)o )− M(o,τ n

o ) + H ysto +�

19: Ocn(o,τ no ) ← C I O

20: Ocn(τ no ,o)←−C I O

21: RBU Ro ← RBU Ro − ρ(o,e(n)

o )

22: RBU Rτ

(n)o← RBU R

τ(n)o+ ρ

(τ(n)o ,e(n)

o )23: ρo ← ρo − ρ

(o,e(n)o )

24: end if25: end for26: end for27: end if

network scenarios, we considered a uniform deployment ofsmall cells, while considering irregularly deployed small cellsin the third scenario obtained from real network information,4

as shown in Fig. 3.We considered Scenario 1 to diagnose the impact of the

proposed MLB algorithm on the network from differentaspects (Subsections VI-C, D, E, and F). However, to findthe effect of the mobility models and the velocity of userson MLB algorithms, we simulated Scenario 2 with differentuser mobility models and various speeds (Subsection VI-G).Scenario 3 was considered to analyze the performance of thealgorithm in an irregularly deployed small cell environmentwith pre-defined streets and a parking area layout (Subsec-tion VI-H).

Regarding an initial UE distribution in Scenario 1, 50%of the mobile UEs were randomly distributed over the wholearea, and the rest were fixed and uniformly distributed overthe border areas of the small cells. The randomly distributedUEs followed the circular way (CW) mobility model [34],where UEs move in a circular path with a 10 m radius and aspeed of 3.6 km/h. In Scenario 2, all the UEs were initiallyrandomly distributed across the same network coverage inScenario 1. For the sake of mobility in Scenario 2, 70%of the UEs were modeled with random way point (RWP)

4The cell layout of Scenario 3 was modeled based on real 4G small celldeployment information on the campus of University of Ulsan, Korea. Thecell layout and coverage information were collected from Sensorly [33].

2212 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

Fig. 3. Radio environment map: (a) uniformly deployed small-cell network(b) irregularly deployed small-cell network.

TABLE II

MOBILITY MODELS FOR THE USERS IN DIFFERENT SCENARIOS

mobility, and the rest were modeled as random walk (RW)UEs. However, UEs in Scenario 3 were randomly distributedinside the predefined areas, which were modeled as streets andparking areas following the real data [33], and the mobilityof those UEs was modeled as RWP and RW inside that area.Table II shows the mobility models we considered for differentnetwork deployments.

The bandwidth for each small cell was set to 20 MHz, whichcorresponds to 100 PRBs, and transmission power was setto 24 dBm. The path loss was modeled using a non-line ofsight (NLOS) propagation loss [35]. We adopted a channelQoS-aware (CQA) scheduler to allocate the available resourceblocks among the UEs, because it is more suitable for real-timeapplications [36]. For an acceptable load threshold, we set theinitial value, T hI nit , to 0.75, which means that the algorithminitiates load balancing if at least one cell exceeds 75% of

TABLE III

SIMULATION PARAMETERS

its RBUR. Once load balancing is triggered, the algorithmcalculates adaptive threshold T h Adapt with (6), based on thenetwork load status. Each simulation was performed for aduration equivalent to 10 minutes of real-time network oper-ation, and the algorithm optimized the network every minute,based on measurements reported from the small cells duringthe period. More specific parameters are given in Table III.

B. Performance Evaluation Metrics

For performance evaluation, we considered two aspects:load distribution across the network, and throughput. To mea-sure load distribution, standard deviation was considered,as defined in (2). Even if the load is balanced across thenetwork by MLB algorithms, throughput can be sacrificedfrom picking up the wrong UEs. If an unsuitable UE is forcedto move to a neighboring cell by setting a high CIO value,the UE will experience very low SINR from the new servingcell. Consequently, the new serving cell may fail to maintainthe data rate required by the UE. If a trade-off exists betweenload balancing and throughput, the algorithm deviates fromthe interests of network carriers. Hence, we also consideredthe impact of load balancing on throughput.

To validate the performance of the proposed algorithm,we compared it with two previous algorithms: the joint MLBand ICIC algorithm [11], and a traditional, fixed-thresholdMLB approach [24]. We set the threshold to determine over-loaded cells at 95%, and to perform handover at 85% for thejoint MLB and ICIC algorithm [11]. There was no informationabout an optimal threshold for the fixed-threshold MLB [24].Therefore, we conducted several simulations with differentthreshold values and found an optimal threshold value of 85%,such that the algorithm achieved the best performance at thatvalue. Hence, we set the threshold value at 85% for the fixed-threshold MLB algorithm. For the sake of simplicity in thefigures, we denote the proposed MLB algorithm as ProposedMLB, the joint MLB and ICIC algorithm as ICIC + MLB,the traditional fixed-threshold MLB algorithm as Fixed MLB,and simulations without an MLB algorithm as No MLB.

HASAN et al.: ADAPTIVE MLB ALGORITHM FOR LTE SMALL-CELL NETWORKS 2213

Fig. 4. RBUR status of the network with 80 UEs and 1 Mbps connectivityper UE: (a) without an MLB algorithm (b) with the proposed MLB algorithm.

C. Impact of MLB on Load Distribution

To identify the impact of the MLB algorithms on loaddistribution across the cells, we simulated a uniformlydeployed small-cell network with CW-mobility UEs and staticUEs (Scenario 1 in Table II). The scenario was simulated with80 UEs, and data traffic for each of the UEs was set at aguaranteed bit rate (GBR) of 1 Mbps. Fig. 4 expresses the loaddistribution in terms of RBUR for the network scenario thatdoes not adopt the MLB algorithm, as well as for the scenariowith our proposed MLB algorithm. It is clear that, as timegoes on, the load among the small cells becomes more evenlydistributed when the proposed MLB algorithm is adopted.

Fig. 4a shows that after 10 minutes of network opera-tion without considering an MLB algorithm in the network,cells 5 and 6 are extremely overloaded with an RBUR of 0.98.However, cells 3 and 8, which are neighbors of cells 6 and 5,respectively, remained underutilized at an RBUR of 0.72.The difference between the maximum and minimum RBURsof the network is 0.26. On the other hand, when the pro-posed MLB algorithm is implemented, as can be seen fromFig. 4b, the overloaded small cells released some of their loadto the underloaded neighboring small cells. As a consequence,the load across the small cells in the network became morebalanced. The figure shows that the RBUR of cell 8 reachedthe maximum 0.96 after 10 minutes, and cell 5 ended up at anRBUR of 0.86, the minimum among the small cells. Hence,the gap between the maximum and minimum RBUR of thenetwork was reduced from 0.26 to 0.10, which means thevariance in the load among the cells in the network is low,and the network is well balanced.

Fig. 5 shows the performance of three different MLBapproaches in terms of standard deviation of RBUR among the

Fig. 5. Standard deviation of RBUR among cells in the network.

Fig. 6. Impact of load balancing on resource utilization and throughput inthe network with 80 UEs at 1 Mbps data rate per UE: (a) Resource utilization(b) Average cell throughput.

small cells in the network. The standard deviation of RBURswith the proposed adaptive MLB algorithm in the networkbecomes smaller, compared with the other MLB algorithms.

D. Impact of MLB on Resource Utilization and Throughput

While load balancing, the overall resource utilization of thenetwork may be affected. Fig. 6a shows the average resourceutilization with different MLB algorithms for the networkscenario considered in Subsection VI-C. Resource utilizationin the network increased while adopting the MLB algorithms.

In the network without an MLB algorithm, the overloadedsmall cells are unable to allocate the required resources tothe UEs due to a resource deficit; meanwhile, the low-loadedsmall cells remain underutilized, as shown in Fig. 4a. As aresult, resources in the network remain underutilized, butthe UEs are denied from the required resource allocation.On the other hand, when an MLB algorithm is adopted,UEs are distributed from the overloaded small cells to the

2214 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

Fig. 7. Performance of different MLB algorithms under different traffic loadsin the network after 10 minutes: (a) Standard deviation of RBUR among cells(b) Average cell throughput in the network.

low-loaded small cells, and release resources in the overloadedcells. Therefore, the small cells can fully allocate the requiredresources to the UEs, which leads to increased overall resourceutilization. Moreover, while load balancing, the intentionallyshifted UEs may experience relatively low signal quality fromthe new serving cells; therefore, the new serving cells mayrequire more resources to serve those shifted UEs, as estimatedin (8). In Fig. 6a, resource utilization is higher with ourproposed algorithm.

The proposed algorithm increases network throughput aswell. Fig. 6b shows the average cell throughput with dif-ferent MLB algorithms for the considered network scenario.Since the overloaded cells are unable to allocate the requiredresources to the UEs, the required data rate for the UEs cannotbe provided in those cells. Therefore, when the network hasno MLB algorithm, UE throughput in the overloaded cellsis limited by the resource deficit. However, MLB algorithmsevenly distribute the load across the cells and enable the cellsto allocate the required resources to the UEs, which leads toincreased network throughput. In the figure, the network hasa higher average cell throughput when adopting our proposedMLB algorithm.

E. Impact of Offered Load Variation

To study the effect of traffic load variation, we simulated thesame scenario in the previous subsection with different trafficload per UE, but kept the total number of UEs unchanged.The traffic loads per UE was set at 128 kbps, 256 kbps,512 kbps, and 1 Mbps. Fig. 7 shows the performance of theMLB algorithms under the four different traffic loads in thenetwork after 10 minutes of network operation.

Fig. 8. The value of T h Adapt for the network at two different traffic loads.

In a low traffic load situation, when most of the smallcells remain underloaded, the previous algorithms show betterperformance, as shown in Fig. 7a. However, when the trafficload increases heavily and most of the small cells becomeoverloaded, the joint MLB and ICIC algorithm and the fixed-threshold MLB algorithm fail to balance the load. On theother hand, by changing the threshold value according to thenetwork load conditions, the adaptive MLB algorithm balancesthe load among the cells.

Fig. 7b shows the average cell throughput with differentMLB algorithms in the network after 10 minutes of networkoperation. Although in the low traffic situation the MLBalgorithms balanced the load well, they have very little impacton throughput. However, with the increase in traffic, averagecell throughput increases when the MLB algorithms are con-sidered. Among the MLB algorithms, with the proposed MLBalgorithm in the network, the increment is at maximum.

We studied the change in the threshold value of the proposedalgorithm with different loads in the network. Fig. 8 shows thatfor a traffic load where each of the UEs has a 1 Mbps GBR,the threshold is 0.89; however, when GBR is 128 kbps perUE, the threshold value changed to 0.81. The figure illustratesthat our proposed algorithm balances the load adaptive to thevariant load in time, and various levels of load, by changingthe threshold according to the network load status, whereasalgorithms with a fixed threshold cannot.

F. Variance in the Number of UEs

To determine the impact of per-UE traffic on MLBapproaches, we simulated the evenly deployed small-cellnetwork with CW mobility and fixed UEs, as seen inScenario 1 of Table II. We simulated the network with differentnumbers of UEs but kept the total traffic demand unchanged.In the first simulation, we considered 80 UEs with connectivityof 512 kbps, whereas 40 UEs with 1 Mbps connectivity wasconsidered in the second simulation. We kept the total datademand in the network at 40 Mbps in both simulations.

Fig. 9 shows the performance comparison among the differ-ent MLB approaches, which clarifies that per-UE traffic has asignificant impact on load balancing. In Fig. 9a, the standarddeviation for RBUR with 40 UEs at 1 Mbps per UE is higherthan the standard deviation with 80 UEs at 512 kbps per UE.The performance of MLB algorithms with UEs that havelow per-UE traffic demand is better than UEs with high per-UE demand. UEs with a higher data rate required more RBs

HASAN et al.: ADAPTIVE MLB ALGORITHM FOR LTE SMALL-CELL NETWORKS 2215

Fig. 9. Performance of different MLB algorithms in the network for differentnumbers of UEs keeping the total data demand for network throughput at40 Mbps: (a) Standard deviation of RBUR among the cells (b) Average cellthroughput in the network.

than UEs with a low data rate, and consequently, the MLBalgorithms have no chance to fine-tune the load among thecells. On the other hand, shifting UEs with low traffic demandtransfers a small load from the overloaded cells to the under-loaded cells. As a result, the MLB algorithms can distributethe load more evenly among the cells.

Fig. 9b shows the average cell throughput observed fromboth the simulations. Although we kept the total data demandidentical in both the simulations, the variation in the number ofUEs results in different average throughput. However, in bothsimulations, our proposed algorithm shows better performancethan the other MLB algorithms.

G. Impact of User Mobility on MLB

In practice, mobile users may have different mobility, andvelocity as well, which can affect the performance of theMLB algorithm. Therefore, to scrutinize the effect of usermobility we simulated Scenario 2, which has the same smallcell deployment as Scenario 1 but different user mobilitymodels. In Scenario 2, we adopted RWP and RW mobilitymodels instead of the CW and static models of Scenario 1.For RWP, we considered two different UE velocities (low andhigh). At the low velocity, we randomly set the UE velocity toa pedestrian speed of between 5 km/h and 10 km/h. However,at the high velocity, the speed of UEs was set between 10 km/hand 30 km/h, considered running and cycling speeds.

Fig. 10 shows the performance of different MLB algorithmsin the network when different user velocities are considered.Since Scenario 1 has a high ratio of static UEs, and the speed

Fig. 10. Performance of different MLB algorithms while considering variousUE velocity in the network: (a) Standard deviation of RBUR among the cells(b) Average cell throughput in the network.

of CW-mobility UEs is relatively low (3.6 km/h), the standarddeviation converges smoothly and remains steady in Fig 5.However, Fig. 10a shows fluctuations in the standard deviationwhen considering different UE velocities in Scenario 2. Due tothe higher user mobility in Scenario 2, more frequent change inUE topology and handovers make an oscillated load situationamong the cells. Hence, the MLB algorithms cannot achieveoptimal load balancing, and the figure shows fluctuationsin standard deviation. However, despite user mobility, ourproposed algorithm outperforms the other MLB approachesin both mobile environments.

Fig. 10b shows the average cell throughput in the networkwith the MLB algorithms. In the figure, the average cellthroughput at the high velocities is lower than at the lowvelocities because the MLB algorithms are unable to achieveoptimal load balancing due to load oscillation. However, withvarious MLB approaches, the average cell throughput in thenetwork increased. With our proposed MLB algorithm in bothvelocity environments, the average cell throughput is higherthan the others.

H. Impact of Irregularity on MLB

The practical deployment of small cells may be irregular,and user mobility patterns may vary according to road andfloor plans. Therefore, we scrutinized the robustness of theproposed MLB algorithm in an irregularly deployed small-cell network (Scenario 3 in Table II) with a realistic mobilitypattern following real small cell deployment information [33].Furthermore, considering the variations in the number ofusers in reality, we also investigated the performance of the

2216 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 17, NO. 4, APRIL 2018

Fig. 11. Performance of different MLB approaches in irregularly deployedsmall-cell network with 80 UEs at 1 Mbps per UE: (a) Standard deviation ofRBUR among cells (b) Average cell throughput in the network.

proposed MLB algorithm with different numbers of UEs inthe considered network.

1) Impact of Cell Layout: To figure out the impact ofcell layouts and user mobility patterns we simulated Sce-nario 3 with 80 UEs. We set the data rate at 1 Mbps GBR perUE, and the velocities of the UEs were set randomly between5 km/h and 10 km/h (considered pedestrian speed). Theperformance of different MLB algorithms in the irregularlydeployed small-cell network is shown in Fig. 11. Fig. 11ashows that our proposed MLB algorithm balanced the loadacross the network well, even in the irregular deployment withvarious cell sizes, and outperforms the other MLB approaches,as seen in the previous scenarios. The more evenly loaddistribution by the proposed algorithm leads to an increasedaverage cell throughput in the network. Fig. 11b shows thatthe average cell throughput in the network is also the highestwhen the proposed MLB approach is adopted in the network.

2) Impact of the Number of UEs: To study the effect ofchanges in the number of UEs in the network, we simu-lated Scenario 3 with various numbers of UEs. The standarddeviation and the average cell throughput were measuredafter 10 minutes of network operation, and the performanceof MLB algorithms are compared in Fig. 12. As discussedin Subsection VI-E, the proposed MLB algorithm updatesthe triggering threshold adaptive to the network load, whichenables the algorithm to cope with the different load situations,and distributes the load more evenly than the other MLBalgorithms, as shown in Fig. 12a. Fig. 12b shows that theproposed algorithm also outperforms the other MLB algo-rithms in terms of average cell throughput in all situations.

Fig. 12. Performance of different MLB algorithms in irregularly deployedsmall-cell network with various numbers of UEs: (a) Standard deviation ofRBUR among cells (b) Average cell throughput in the network.

Therefore, the simulation results from different aspects demon-strate that in a realistic network scenario, the proposed MLBalgorithm is able to achieve more even load balancing.

VII. CONCLUSION

In this paper, we proposed an adaptive algorithm formobility load balancing in a small-cell network in orderto improve network performance, such as the service droprate and throughput. Due to lack of resource, overloadedcells cannot allocate the amount of required resource to UEseven if underloaded neighboring cells are capable to serve.Since such unbalanced loads bring throughput degradation,load balancing can improve network performance. In thecase of LTE, the resource usage depends on wireless signalquality and traffic demands of UEs. Hence, to develop a loadbalancing algorithm, we defined a resource block-utilizationratio as a measurement of cell load, and employed an adap-tive threshold to determine overloaded cells according to thenetwork load situation. To avoid ping-pongs of load amongthe cells, we considered not only overloaded cells but alsotheir neighboring cells by estimating the moving load’s impacton the existing cell loads. Via simulations, we showed thatour proposed algorithm balanced more of the load acrossthe network, and provided higher throughput than previousalgorithms.

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Md Mehedi Hasan received the B.Sc. degreein computer science and engineering from theUniversity of Chittagong, Chittagong, Bangladesh,in 2009. He is currently pursuing the Ph.D. inelectrical engineering with University of Ulsan,Korea. From 2012 to 2014, he was a SoftwareEngineer in two different leading software develop-ment companies in Bangladesh. His current researchinterests include mobile network optimization,energy-efficient mobile communication, mobilitymanagement, traffic offloading, and load balancing.

Sungoh Kwon (S’05–M’08–SM’18) received theB.S. and M.S. degrees in electrical engineering fromKAIST, Daejeon, South Korea, and the Ph.D. degreein electrical and computer engineering from PurdueUniversity, West Lafayette, IN, USA, in 1994, 1996,and 2007, respectively. From 1996 to 2001, he wasa Research Staff Member with Shinsegi TelecommInc., Seoul, South Korea. From 2007 to 2010, hedeveloped LTE schedulers as a Principal Engineerwith Samsung Electronics Company, Ltd., SouthKorea. He has been an Assistant Professor with the

School of Electrical Engineering, University of Ulsan, since 2010, wherehe is currently an Associate Professor. His research interests are in wirelesscommunication networks.

Jee-Hyeon Na (M’03) received the B.S. degreein computer science from Chonnam National Uni-versity, and the M.S. and Ph.D. degrees in com-puter science from Chungnam National Universityin 2002 and 2008, respectively. She has been withthe Electronics and Telecommunications ResearchInstitute (ETRI) since 1989, where she is currentlythe Director of the Radio Access Network S/WSection. Her research interests are in the area of 4G,5G small cells, enhanced mobile broadcast and mul-ticast services, and self-organizing networks. She isa member of IEICE communication part.