A Novel Load-Aware Cell Association forSimultaneous Network Capacity and User QoS

Optimization in Emerging HetNetsAhmad Asghar, Hasan Farooq and Ali Imran

Department of Electrical and Computer EngineeringUniversity of Oklahoma

Tulsa, Oklahoma 74135, USAEmail: {ahmad.asghar, hasan.farooq, ali.imran}@ou.edu

Abstract—Ultra-dense Heterogeneous networks (UDHN)are emerging as the inevitable approach to cope with theimminent cellular network capacity crunch. However, loadimbalance and widely disproportionate SINR distributionbetween macro and small cells, remains the key hurdlein harnessing the full potential of UDHN. In this paperwe address this problem by proposing and analysing anovel load-aware user association methodology that offersa mechanism to simultaneously optimize network capacity,load distribution and coverage. The solution concurrentlyleverages the three key optimization parameters for Cover-age and Capacity Optimization (CCO) and Load Balancing(LB) SON functions i.e. antenna tilts, transmit powers andcell individual offsets (CIOs). The method incorporatesexponential-weighting based prioritization of CCO andLB SON functions within the user association process.The results suggest that the proposed approach offersa distribution of load between macro and small cellsthat yields more gain in terms of both network capacityand user quality of service than conventional max signalstrength or max SINR based association methods.

Index Terms—HetNet; Self-organizing networks; CCO;LB; Joint Optimization; User Association; QoS; 5th Gen-eration Cellular Networks.

I. INTRODUCTION

Efficient utilization of resources in emerging cellularnetworks, vis-a-vis 5G, is the most rapidly growing con-cern among the telecommunication community today.Despite recent advancements in many physical layertechniques and possible exploitation of new spectrumat higher frequencies, network densification remains themost yielding means to meet the capacity demands offuture 5G cellular networks. However, network densifi-cation comes with its own set of challenges [1], primeamong which is the heavily skewed distribution of loadbetween macro and small cells [2].

Theoretical studies [3] as well as field trials haveshown that in a UDHN, macro cells tend to be moreheavily loaded owing to the transmit power disparitybetween macro and small cells. To rectify this imbalanceand introduce flexibility into the standard ReferenceSignal Received Power (RSRP) based user association,

3GPP introduced the cell individual offset (CIO) param-eter [4]. CIO introduces a virtual boost to the RSRP ofa cell which can be used to forcefully associate usersto small cells with lower power and greater availableradio resources. This method, however, is far from idealwith several consequences, prime among which is theSignal to Interference and Noise Ratio (SINR) disparitycreated by such an association. Such CIO induced cellassociation also fails to accommodate for multiple fac-tors that affect user quality of experience. These factorsinclude SINR from the candidate cell, the effective loadgenerated by the user to be associated, available freeresources in the candidate cell, as well as the impact ofnew user association on interference and hence overallsystem capacity.

Some aftermaths of CIO induced cell association areillustrated in figures 1a and 1b. Figure 1a plots theuser associations with no CIOs, whereas in figure 1b,small cells are assigned 10 dB CIO each. As intended,introduction of CIO does force some UEs to switchfrom macro cell to small cell thereby, achieving someapparent LB; however, this change in association comeswith a caveat, i.e., reduced SINR for small cell usersacquired via CIOs, sometimes significantly, due to thelower transmit powers of small cells. This effect canbe seen by observing an example UE encircled in redin figures 1a and 1b, whose SINR goes from 17 dBto -20 dB after switching cells. It is worth noting thatsuch arbitrarily set values of CIOs, as is the currentpractice, do not and cannot ensure that the host small cellhas enough surplus Physical Resource Block (PRBs) tooffset the 37 dB drop in SINR compared to macro cell.This demonstrates that use of empirically determinedCIO values can affect overall resource efficiency inthe system negatively, thereby causing the problem thatCIOs were introduced to solve in first place. Instead,CIOs need to be determined through a method thatconsiders user traffic demands and current cell loads.Most importantly CIO values should be determined in

(a) With small cell CIOs = 0 dB

(b) With small cell CIOs = 10 dB

Fig. 1: RSRP-based UE Association

conjunction with other two key hard parameters thataffect SINR as well as cell association i.e., Tx powerand antenna tilts.

A. Related Work

The survey by Liu et. al. [5] provides a comprehensivesummary of the work done on user association method-ologies in cellular networks especially in conjunctionwith proposed 5G-enabling technologies including mas-sive MIMO systems, dense HetNets, energy conser-vation, mmWave, Cloud-RAN and internet of things(IoT). A key observation that can be drawn from thisrecent survey is the use of maximum RSRP based cellassociation with CIO by the overwhelming majorityof research studies on cell association. This is in partdue to the existence of mature standards for parametersand measurements shared with the user equipment (UE)while making the association decision. Later we explainthat our proposed resource conscious cell associationscheme can also be implemented using existing standardmeasurements in cellular systems.

Conversely, load and offered cell capacity often pro-vide the underlying motivation in SINR, instead of

RSRP, based cell association approaches proposed inliterature, such as the one presented in [6]. A similarapproach is presented in [7] and [8] which attemptto optimize user association based on offered capacity.Authors in [9] utilize spectral efficiency, a derivativeof the SINR, to optimize user association. HoweverSINR based user association is not always the optimalmethodology as it can contribute to overloading of macrocells in a HetNet. In this work, we propose a userassociation methodology that not only takes into accountthe received power, but also considers cell load modeledas a function of SINR, while performing cell associationin order to avoid overloading, and consequently, userquality of experience degradation.

While optimizing user association, the parameters ofchoice also play a vital role. Many studies employ eitherantenna tilts [10], Tx power [11], or CIO [12] as theoptimization parameters. However, as demonstrated in[13], the impact of cell Tx powers, antenna tilts andCIOs on cell coverage, capacity and load is deeplyintertwined. This makes user association optimization inone or two parameters a futile exercise since any changein the third parameter drastically alters the capacity andload scenario of the network.

B. Proposed Approach and ContributionsOptimization of user association has the capability to

provide an optimal tradeoff between otherwise conflict-ing objectives of CCO and LB SON functions. However,along with its dependence on multiple optimizationparameters, user association is also affected by theinterdependence of load and SINR as highlighted in [2].The load in a cell for given traffic demand dependson SINR perceived by the users associated with thatcell. With poor SINR same traffic demand generatesmore load on the cell because of low spectral efficiencyand hence more PRB consumption. On the other hand,SINR of users associated with a cell also depends on theresource utilization in neighboring cells, thus creating anintertwined chain effect. The contributions and findingsof this paper can be summarized as follows:

1 We propose a novel cell association approach thattackles the aforementioned challenges by embed-ding the goals of CCO and LB SON functions intoa single load aware cell association method.

2 We formulate and solve an optimization problemthat jointly optimizes soft parameter CIO and hardparameters antenna tilt and Tx power to actuate theproposed cell association method.

3 We also introduce a parameter to set the priority ofCCO and LB SON functions for user associationwithout necessitating changes to existing LTE net-work standards. We also empirically determine theoptimal value of this parameter.

4 The analysis and results in this paper call for a shiftfrom signal strength or SINR focused optimization

of cellular networks to a hybrid load-aware opti-mization that can solve the aforementioned prob-lems in emerging UDHN.

The rest of the paper is organized as follows: a de-scription of system model employed in derivation ofthe proposed user association is provided in SectionII. A description of the proposed approach along withkey influencing factors is presented in Section III, whileSection IV contains simulation results demonstrating theefficacy of the proposed approach in comparison withcoverage and SINR based user association methodolo-gies.

II. SYSTEM MODEL

A. Network and User Specifications

Network topology considers at least one randomlydeployed small cell in the coverage area of a macro cell.Frequency reuse of 1 is considered and same frequencyspectrum is utilized by macro and small cells. Macrocells use directional antennas with three sectors per sitewhile small cells employ omni-directional antennas. AnLTE like OFDMA based system with resources dividedinto physical resource blocks (PRBs) of fixed bandwidth,is assumed. For conciseness, the downlink direction ischosen for the analysis as this is where most imbalancein coverage of macro cells and small cells occurs. Userassociation is calculated for a snapshot of network userdistribution. We also assume that requested user datarate is known which gives a lower-bound on the desiredinstantaneous user throughput. Desired user through-put can be modelled as a spatio-temporal function ofsubscriber behavior, subscription level, service requestpatterns, as well as the applications being used with thehelp of big data analytics as recently proposed in [14].

B. Parameters and Measurements

We consider the following network information to beknown to both UEs and eNodeBs (eNBs).

1) Cell Loads: For an OFDMA based network, wecan define instantaneous cell load as the ratio of PRBsoccupied in a cell during a Transmission Time Interval(TTI) to total PRBs available in the cell. This informa-tion is available as a standard measurement in LTE as"UL/DL total PRB usage" [15] and can be broadcast tothe UEs. To define cell load ηc for our system model,we first calculate minimum number of PRBs ηcu to beallocated to a user:

ηcu =1ωB

(τu

f (γcu)

)(1)

where τu represents the (desired) throughput of user u∈ Uc , where Uc is the set of all active users associatedwith cell c which have requested resources from the cell,γcu represents the SINR of user u when associated withcell c and ωB is the bandwidth per PRB. f (γcu) denotes

the spectral efficiency of the user link for given SINR. Ifwe consider features such as MIMO or coding schemegains and scheduling gains, f (γcu) can be defined asf (γcu) B Alog2

(1 + Bγcu

), where A and B are variables

that can be used to model throughput gains (per PRB)achievable from various types of diversity schemes, orlosses incurred by signaling overheads, or hardwareinefficiencies. For sake of simplicity, without loss ingenerality, we assume A = B = 1. Ratio of the sumof requested PRBs in a cell to the total cell bandwidthNcb

gives the cell load:

Cell Load = ηc =1

Ncb

©­« 1ωB

©­«∑Uc

τulog2 (1 + γcu)

ª®¬ª®¬ (2)

Since there is no limit to the requested PRBs by theusers associated with a cell, the range of cell load isηc ∈ [0,∞). If cell load exceeds 1, the cell in reality willbe fully loaded and oncoming users, for whom there areno more resources left, will face blocking. The value ofload ηc is therefore referred to as virtual load and ηc > 1reflects congestion in cell c.

2) Received Power: In LTE networks, downlink Ref-erence Signal Received Power (RSRP) from nearbybase stations is continuously monitored by the UEs andreported to the serving eNB for a number of purposes.In the proposed user association method, we use the userreceived power (RSRP) to calculate coverage probabilityin the network.

3) Cell Individual Offset: CIOs can be defined asa combination of multiple cell association parametersintroduced by the 3GPP in Release 8 E-UTRAN Spec-ifications [4]. More specifically, CIO includes cell hys-teresis, cell offsets and event related offsets which areused by the UE to decide association with a cell. CIOinformation can easily be broadcast by each cell anddecoded by the UEs as part of standard operation. Forthe purpose of this paper we treat CIO as a simple virtualboost in RSRP.

4) Antenna Tilt: As macro cells in the system underconsideration use directional antennas, the gain frombase station to user Gc

u is dependent on the 3D antennagain model. For development purposes, 3GPP has pro-vided a theoretical 3D antenna gain model which is givenin [16] as:

Gcu = 10

−1.2

(λv

(ψcu−ψc

t iltBv

)2+λh

(φcu−φcazi

Bh

)2)

(3)

where ψcu is the vertical angle between user c and

the transmit antenna of cell c, ψctilt

is the tilt angle ofserving cell antenna, λh and λv are the weighting factorsfor horizontal and vertical beam pattern respectively, φcuis the horizontal angle of user u from cell c, φcazi isthe azimuth of antenna of cell c, and Bh and Bv arehorizontal and vertical beamwidths of the transmitter

antenna of cell c. As our variable of interest in (3) is tiltangle and rest of the antenna parameters can be treatedas constants, for the sake of conciseness we can simplify(3) using the following substitution:

xcu =(Bv)2λhλv

(φcu − φcazi

Bh

)2

(4)

5) Signal-to-Interference and Noise Ratio: Equation(2) demonstrates the relationship between cell load anduser SINR. To develop a load-aware user associationmethodology, we need to have SINR estimation in orderto estimate cell load. Based on the system model, wepropose to estimate SINR for user u as a function ofantenna tilts and transmit power along with interferingcell loads using the standard exponential pathloss modelas:

γcu =Pct GuGc

uδa(dcu

)−βκ +

∑∀i∈C/c ηiPi

tGuGiuδa

(diu

)−β (5)

where Pct and Pi

t are the transmit powers of servingcell c and interfering cell i, Gu is UE gain, δ is signalshadowing, a is the pathloss constant, dc

u and diu repre-

sent distance of user u from cell c and i, respectively,β is the pathloss exponent, and κ is the thermal noisepower. Here, ηi denotes actual cell load in a cell such thatηi ∈ [0, 1] and is used exclusively for SINR calculation.Substituting xcu in (3) and replacing Gc

u and Giu in (5)

with the resulting expression gives:

γcu =Pct Gu10µ

((ψc

u−ψct ilt )

2+xcu

)δa

(dcu

)−βκ +

∑∀i∈C/c ηiPi

tGu10µ((ψi

u−ψit ilt )

2+xiu

)δa

(diu

)−β(6)

where µ is consolidated constant based on fixed antennacharacteristics.

III. A NEW USER ASSOCIATION METHODOLOGY

The state-of-the-art method of determining cell asso-ciations is to use the RSRP measurements along withCIO values as given below:

Pcr,udBm

= Pcr,udBm

+ PcCIOdB

(7)

where Pcr,udBm

is the true signal power in dBm receivedby user u from cell c and Pc

r,udBmis the received power

reported back by user u to cell c in dBm. This valueincludes the Pc

CIOdB(the CIO value) of cell c in dB.

However, as explained earlier, this method overlooksthe key impact of user association on cell loads andconsequently SINR, and thus results in negative impacton overall capacity and QoS through imbalanced loadsand poorer SINR distribution among users (See intro-duction section and Fig. 1 and 2). To overcome thischallenge, we propose to establish user association with

cell j not only based on received power but also loadin that cell. More specifically, in this load aware cellassociation method user to be associated with cell j canbe determined as:

where Uj,t is a set of all active and idle users forwhom a scaled product of the RSRP (+CIO) in WattsPcr,u , and the residual cell capacity for cell j is the

highest. α ∈ [0, 1] is a weighting factor introduced toallow trading between the impact of RSRP and cellload measurements in the cell association. Note thatin (8), to make new user association decision with acell we use the virtual cell load which provides a truerpicture of effective potential load in the candidate cell bytaking into account the users that are already associatedwith that cell but were not served due to lack of freeresources. The time subscripts for Uj,t and ηc,t−1 signifythe fact that the user association at time t is calculatedusing the last known cell loads i.e. before the new setof users are associated.

The set Uc used in the expression for SINR in (6)represents the set of only active users associated withthe cell c. With α = 1, the user association becomessimply a function of cell load at the time of associationand consequently the SINR of users already associatedwith the cell and thus this cell association actuates theLB SON function only. On the other hand, if α = 0,the proposed user association method simply representsstate-of-the-art RSRP based cell association methodwhich helps achieve coverage optimization aspect inthe CCO SON function. An important characteristic ofthe proposed user association formulation in (8) is thatit can be implemented in a practical network usingexisting measurements and parameters including cellloads, RSRP measurements, and CIOs. Therefore, themethodology does not require any significant changesto the existing LTE network standards in order for it tobe implemented.

Although the proposed user association methodologycan ensure that cell loads are optimized, it cannot ensurethat quality of service is optimized along with them.Therefore, the proposed user association is used to max-imize the geometric mean of achievable user throughputin the network by optimizing Tx powers, antenna tiltsand CIO parameters of cells in a UDHN. The geometricmean is used to ensure fairness in terms of cell loads ontop of the user association since maximum geometricmean can only be achieved when all inputs are equal.Although this is not possible in a practical network, thefinal results demonstrate a considerably even cell loaddistribution due to this formulation which is given as:

maxPct ,ψ

ct il t

,PcCIO

©­«∏C

(∏Uc

ωculog2

(1 + γuc

)) 1|Uc | ª®¬

1|C|

(9)

Uj,t B

{∀u ∈ U | j = arg max∀c∈C

((1

ηc,t−1

)α∗

(Pcr,udBm

) (1−α))}(8)

TABLE I: Parameter Settings for Simulation

System Parameters ValueNumber of Base Sta-tions 7

Sectors per Base Station 3Small Cells per Sector 1Number of UE per Sec-tor 25

Transmission frequency 2 GHzTransmissionBandwidth 10 MHz

Network Topology Hexagonal

Macro Cell Tx Power Max: 46 dBm, Min: 40dBm

Macro Cell Antenna Tilt Max: 15◦, Min: 0◦

Small Cell Tx Power Max: 30 dBm, Min: 27dBm

Small Cell CIO Max: 10 dB, Min: 0 dBCellular System Stan-dard LTE

Macro Cell Height 25 mSmall Cell Height 10 mInter-site Distance(Macro) 500 m

where ωcu is the minimum number of physical resource

blocks, rounded up to the nearest whole number, re-quired by the user u to achieve its desired throughputwhen associated with cell c. The formulation in (9) usesSINR expression derived in (6) to obtain the achievablethroughput of a user.

IV. SIMULATION AND RESULTS

In this section, we evaluate cell load distribution witha range of α values and discover interesting trends thatcan be used to develop some practical design guidelines.We also present a comparison of the proposed userassociation with a Max RSRP and Max SINR based userassociation in terms of cell load distribution and otherimportant KPIs.

A. Simulation Setup

We employ a LTE 3GPP standard compliant networktopology simulator [16] to generate typical macro andsmall cell based network and UE distributions. Thesimulation parameters details are given in Table I.

We use wrap around model to simulate interferencein an infinitely large network thus avoiding boundaryeffect. To model realistic networks, UEs are distributednon-uniformly in all the sectors such that a fractionof UEs are clustered around randomly located hotspotsin each sector. Due to the non-convexity of SINRexpression in (6), sequential quadratic programming(SQP) is used to maximize the geometric mean of userthroughput since it allows an approximation of a non-convex function as a convex function, while Monte Carlosimulations are used to estimate average performance.

B. Results

In the formulation presented in (8), user associationis dependent on 3 features: cell loads at the time ofassociation, RSRP with CIO as reported by the UE,and the user association exponent α. The impact ofcell loads and RSRP on user association are obviousfrom (8); however, the impact of exponent value on userassociation requires quantitative evaluations of systemKPIs. A very relevant KPI is the cell load and itsdistribution among cells for given total traffic in thenetwork. A lower average cell load and more normalload distribution among cells for given traffic reflectsa better performing CCO-LB solution. A comparisonfor α ∈ [0, 1] was done with results showing that forα = 0.4375, cell load distribution is the closest to anormal distribution. Due to space limitations, figure 2only presents cell load kernel density distribution forvalues of α ∈ [0.25, 0.5].

Fig. 2: Comparison of Offered Cell Load Distributionfor α ∈ [0.25, 0.5]

Figure 3 presents a comparison of cell load kerneldensity distribution of the proposed load-aware userassociation with Max RSRP and Max SINR user as-sociation methodologies. The results indicate that the

proposed load-aware user association achieves far morebalanced load distribution compared to either of thetwo competing methodologies, despite the fact that MaxSINR association scheme provides the best overall SINRdistribution, as shown in figure 6. Conversely, the impactof RSRP or SINR centric approaches is clearly evidentin terms of uneven load distribution among cells. Thisis demonstrated in terms of macro and small cell loaddistributions in figures 4 and 5, which serve to highlightthe true capacity gains of the proposed load-aware userassociation over existing user association approaches byavoiding underloading small cells and overloading macrocells.

Fig. 3: Comparison of Offered Cell Load Distributionfor load-aware vs. Max RSRP and Max SINR userassociation

Fig. 4: Comparison of Offered Macro Cell Load Dis-tribution for load-aware vs. Max RSRP and Max SINRuser association

The even load distribution offered by the proposedload-aware user association methodology also results ingains in terms of user QoS by minimizing the number ofusers who are unable to achieve their desired throughputdue to a lack of physical resources at the serving cell.This is evidenced by the ratio of unsatisfied users in

Fig. 5: Comparison of Offered Small Cell Load Distri-bution for load-aware vs. Max RSRP and Max SINRuser association

Fig. 6: Comparison of average SINR CDF for load-awarevs. Max RSRP and Max SINR user association

the network and the utilization of physical resourcesin the network given in figure 7. The results in figure7 highlight two important points. Firstly, the resourceutilization in load-aware user association is higher thanthe other two schemes due to the lower user SINR whichresults in more PRBs being utilized to achieve desireduser throughput. However, the high user SINR achievedby Max RSRP and Max SINR associations in compari-son is inconsequential if there are no physical resourcesto serve the users at serving cell, as is demonstrated bythe ratio of unsatisfied users in figure 7. The ratio ofunsatisfied users due to load-aware user association in 7is significantly lower compared to either Max RSRP andMax SINR association methodologies. This is becausethe max RSRP and max SINR association methodologiesare blind towards network loading while associatingusers with cells, thus exposing new users to resourceunavailability. The load-aware user association avoidsthis issue by balancing user associations based on RSRPand cell loads, thus forcing users to switch to cells withlower SINR even when a cell with higher RSRP isavailable.

Fig. 7: Comparison of network utilization and unsatisfieduser ratio for load-aware vs. Max RSRP and Max SINRuser association

V. CONCLUSION

In this paper, we presented a novel load-aware userassociation methodology for capacity optimization withthe challenge of load distribution in emerging HetNetsfor 5G networks taken as the key constraint. The pro-posed user association methodology exploits joint opti-mization of CIO, Tx power and antenna tilt to balanceloads between all cells, including macro and small cells,in a way that increases overall physical system capacity.Results suggest that for the optimal user associationexponent α, SINR does not suffer considerably as com-pared to the RSRP(+CIO) only approach. The proposedassociation methodology also provides a balanced net-work loading without any significant depreciation indownlink SINR compared to existing methods. As partof a future study, use of game theoretic techniques todetermine optimal α value will be pursued. Moreover,considering the assumption of apriori knowledge of re-quired user throughput, further research can be directedtowards incorporation of big data aided knowledge likeuser mobility prediction to optimally set α values foreach cell and UE.

ACKNOWLEDGMENT

This work was supported by NSF Grant No.1619346. For details about this project see :http://bsonlab.com/ASSH/.

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