User Association and Resource Allocation in 5G (AURA-5G

User Association and Resource Allocation in 5G(AURA-5G) A Joint Optimization Framework

Akshay Jain Elena Lopez-Aguilera and Ilker Demirkol

Abstractmdash5G wireless networks will be extremely dense giventhe projected increase in the number of users and access points(APs) as well as heterogeneous given the different types ofAPs and the applications being accessed by the users In suchchallenging environments efficient mobility management (MM)and specifically user association will be critical to assist the5G networks in provisioning the Quality of Service (QoS) ofdiverse applications 5G targets to serve Whilst determining themost suitable AP for the users multiple constraints such asavailable backhaul capacity link latency etc will need to beaccommodated for Hence to provide an optimal user associationsolution in this paper we present a joint optimization frameworknamely AURA-5G Under this framework we formulate our userassociation strategy as a Mixed Integer Linear Program (MILP)that aims to maximize the total sum rate of the network whilstoptimizing the bandwidth assignment and access point selectionWe analyze multiple active application profiles simultaneouslyie enhanced Mobile Broadband (eMBB) and massive MachineType Communication (mMTC) in the network and study theperformance of AURA-5G Additionally we provision a novelstudy on the multiple dual connectivity modes wherein the usercan be connected to either one macro cell and a possible small cellor with any two favorable candidate access points Utilizing theAURA-5G framework we perform a novel comparative study ofall the considered scenarios on the basis of total network through-put performance against baseline scenario and system fairnessWe show that the AURA-5G optimal solutions improve thedifferent network scenarios in terms of total network throughputas compared to the baseline scenario which is a conventional userassociation solution Further we also present a fidelity analysisof the AURA-5G framework based on the backhaul utilizationlatency compliance convergence time distribution and solvabilityAnd since a given network cannot always guarantee to satisfy thefuture network loads and application constraints we show thatAURA-5G can be utilized by the operatorsvendors to evaluatethe myriad network re-dimensioning approaches for attaininga feasible and optimal solution Henceforth we then explorethe possibility of network re-dimensioning and study its impacton system performance for scenarios where the performance ofAURA-5G is severely impacted due to the extremely strict natureof the constraints imposed in the MILP

Index Termsmdash5G User Association Optimization MobilityManagement HetNets Bandwidth Allocation

This work has been supported by the EU Horizon 2020 research andinnovation programme under grant agreement No 675806 (5GAuRA)

We would also like to thank Dr H Birkan Yilmaz now a professor atBogazici University in Turkey for providing important insights and commentsduring the development of this work

Akshay Jain and Elena Lopez-Aguilera are with the Department ofNetwork Engineering Universitat Politegravecnica de Catalunya BarcelonaTECHBarcelona 08034 ES Ilker Demirkol is with the Dept of Mining Indus-trial and ICT Systems Engineering Universitat Politegravecnica de CatalunyaBarcelonaTECH Barcelona 08034 ES Endashmail akshayjainupceduelopezentelupcedu and ilkerdemirkolupcedu Akshay Jain is the cor-responding author

I INTRODUCTION

The upcoming 5G networks will be characterized by anextremely dense and heterogeneous amalgamation of accesspoints (AP) with different Radio Access Technologies (RATs)users as well as application types [1] Such a network environ-ment will present significant challenges to the complex task ofmobility management (MM) [2] As part of the MM objectivefor 5G networks seamless mobility with extremely low latencywill need to be provisioned However to guarantee suchlatency and reliability characteristics 5G MM mechanismswill be broadly required to ensure fast handover methodsefficient signaling during mobility events optimal and fast userto AP associations reliable and fast path re-configuration aswell as service migration [3]

Specifically to ensure that the quality of service (QoS)requested by an application on a given user is met a userdoes not experience frequent handovers (FHO) as well asthe network capabilities and capacities are respected efficientuser association techniques will be critical And given theexponentially increasing number of users that 5G networkswill cater to [4] finding the optimal user equipment (UE)-APassociation will present a significant challenge for these userassociation techniques This will be further exacerbated by thefact that 5G networks will be constrained by a multitude ofrequirements imposed for ensuring application QoS as well aslimitations with regards to technological capabilties

To elaborate in [5] 3GPP established that to be able toprovision services such as Virtual Reality (VR)AugmentedReality (AR) among others a minimum rate requirement of100 Mbps would be required for enhanced Mobile Broadband(eMBB) services Further it was also determined that such ser-vices would necessitate anywhere between 5-10 ms latency (orround trip delay) [5] Alongside these requirements massivemachine type communication (mMTC) services would needto be serviced anytime and anywhere even though they donot communicate as regularly as the eMBB services Furtherthe network would have to accomodate very high density ofmMTC devices that will be prevalent in 5G networks eg24000 users per lt2 according to [6] Moreover the ultra-reliable low latency communication (URLLC) services willrequire latency within the range of 1-3 ms as well as extremenetwork reliability [5]

Coupled with these aforesaid requirements 5G networkswill also be challenged by the amount of available resourcesConcretely while mmWave small cells (SCs) will help resolvethe lack of resources in current sub-6 GHz access networkthe corresponding backhaul links will become increasingly

TABLE IABBREVIATIONS LIST

RSSI Received Signal Strength IndicatorAP Access Point

MILP Mixed Integer Linear ProgramQoS Quality of Service

HetNet Heterogeneous NetworksMM Mobility ManagementFHO Frequent HandoverRAT Radio Access TechnologySDN Software Defined NetworkingUE User EquipmentVR Virtual RealityAR Augmented Reality

eMBB enhanced Mobile BroadbandmMTC massive Machine Type communicationURLLC Ultra-reliable Low Latency communication

SC Small CellsMC Macro CellsSNR Signal to Noise RatioSINR Signal to Interference and Noise RatioMIH Media Independent Handover

TOPSIS Technique for Order of Preference by Similarity toIdeal Solution

MADM Multi Attribute Decision MakingDC Dual Connectivity

mmWave Millimeter WaveeNB Evolved NodeBHPP Homogeneous Poisson Point processLTE Long Term EvolutionMHz MegaHertz3GPP Third Generation Partnership ProjectRRC Radio Resource ControlMRT Minimum RateCPL Constrained Path LatencyCB Constrained Backhaul

gNB next generation NodeBdB Decibels

LOS Line of SightNLOS Non Line of Sight

SF Shadow Fading

strained Further the availability of APs with links that satisfythe latency requirements will be critical

Henceforth these aforesaid requirements and technologicalchallenges will make the problem of user association alongsideresource allocation in 5G heterogeneous networks (HetNets)important to explore and address Consequently in this workwe aim to do the same Notably a broad spectrum of strate-giesmethods to accomplish the task of user association in 5GHetNets have been discussed in the literature which we havealso taken cognizance of in Section II However certain gapsstill exist with regards to the aforesaid problem And so weelaborate them as follows

1) Most of the research works discussed in the literaturepresent a user association method that allows the userto connect to only one AP at most [25]ndash[28] Certainworks such as [7] [8] etc discuss the problem ofuser association in a Dual Connectivity (DC) scenarioHowever the analysis is limited to scenarios where SCsare tightly coupled to macro cells (MCs) Concretelythis means that the choice of an SC is governed by

the choice of the MC While this is inline with thecurrent 3GPP DC standards in Release-15 [9] it is ingeneral a very restrictive choice Henceforth we statethat a gap exists here where none of the works in thestate of the art to the best of our knowledge considerthat an independent choice of MC and SC or even twoSCs or MCs to serve a user can be made Note that arelatively tangential work to ours in [10] discusses sucha possibility for slice level mobility in 5G networksHowever they do not present any concrete methodologyor analysis for the purpose of user association

2) None of the works present in the literature provide anapplication aware strategy [12]ndash[21] [25]ndash[28] Con-cretely they do not consider or analyze the impact ofthe prevalence of eMBB and mMTC services togetherThis is critical for user association in 5G HetNets sincethe presence of different services will lead to differentbottlenecks within the same system as shown in thisstudy thus making the process of finding an optimalassociation even more complicated

3) Delving deeper into the analysis presented in the litera-ture it is evident that for the computation of the signalquality in terms of Signal to Interference and Noise ratio(() an isotropic transmission and reception model isassumed However none of the user association worksexplore the impact of transmit and receive beamformingon the overall system performance as well as for thecomplexity of computation of the (

4) While certain research efforts discuss the computationalcomplexity of the non-linear optimization framework forthe user association problem eg [22] [24] [33] [34]none of these works provision a detailed analysis withregards to the computation time solvability as well asother network parameters such as achieved latency andbackhaul utilization

Given the aforesaid deficiencies to the best of our knowledgewe present the very first study in literature with regardsto application aware user association in 5G HetNets in thispaper Concretely we have explored the prevalence of multipleservices and their impacts on the user association problemHenceforth for our study we have considered the scenarioswhere there are only eMBB services and where both eMBBand mMTC services co-exist We characterize the performanceof our Joint Optimization framework ie AURA-5G in boththese setups and present insights which currently are notprovided by any other research effort Note that we leavethe study involving URLLC services as part of a follow-upwork to this paper Additionally for our evaluation processwe utilize realistic network scenarios and parameters Thisconsequently helps establish the efficacy of the AURA-5Gframework However a detailed discussion with regards to thescenarios and parameters is deferred until Sections IV and V

Furthermore we also consider the DC scenarios whereinwe explore the futuristic trends of having independent choicesof MC and SC ie the choice of SC is not geo-restrictedto the coverage of the chosen MC and the possibility ofselecting either two SCs or two MCs In addition to the

Fig 1 AURA-5G Framework The logical flow ie flow of control within the developed tool is depicted using dashed arrows whilst solid arrows indicatethe data flow in the program

DC scenarios we also study the single association strategies(which most research works in the state of the art consider)and a baseline strategy (discussed in detail in Section IV)These latter scenarios provide us a basis for comparison forthe AURA-5G framework that we have developed

Moreover in this paper we have also presented a detailedstudy into the performance of such joint optimization strategieswhen the environment is interference limited due to omni-directional antennas or when beamforming is utilized Themotivation behind exploring the aforesaid scenarios is thefact that while most of the current day radio antennas do notutilize beamforming networks such as 5G and beyond 5G willutilize massive MIMO setups that will support beamformingHence it becomes imperative to study both these scenariosby virtue of the algorithm being deploy-able irrespective ofthe infrastructural setup

Next as part of the contributions of this paper we alsoemphasize on the AURA-5G software framework which wehave developed and published1 to obtain the aforementioneddeep insights into the user association problem As can beseen from the framework diagram in Fig 1 AURA-5Gis basically composed of four building blocks The veryfirst block ie location generator block takes care of thegeneration of location specific information for the users andAPs to be utilized during the analysis Concretely it generatesthe location coordinates for the users and APs within thetopology Next the system specifics generator block createsthe backhaul link based details for the system computes theSINR matrix for the system (according to whether we are inan interference limited scenario analysis or a beamformingbased scenario analysis) and saves the necessary metadatathat would be required by the subsequent optimizer blockFor the optimizer block we utilize the Gurobi toolbox [11]and solve the Mixed Integer Linear Programming formulation(MILP) discussed in Section III to compute the optimal

1The complete framework has been developed using Python It can befound at the github repository link will be provided after the review process

solution Note that in the optimizer block we also specify theparticular scenario (described in Section IV) that has to beevaluated on the system specifics generated by the precedingframework boxes The optimizer block then saves the optimalsolution and supporting metadata which are consequentlyutilized by the data representationanalytics box for gaininginsights into the obtained solution for the given scenario

The rest of this paper is organized as follows In Section IIwe present the state-of-the-art strategiestechniques employedwith regards to the user association mechanisms for 5Gnetworks In Section III we discuss in detail the optimizationframework ie the MILP framework for computing theoptimal user association given the objective and correspondingconstraints We also discuss the aspects of Linearization andchallenges involved in developing the AURA-5G frameworkin this section In Section IV we present the various scenariosthat have been studied in this paper We then elaborateon the evaluation framework that has been adopted in thispaper in Section V The results from the evaluation andthe corresponding insights have then been articulated inSection VI and VII Specifically in Section VII we discusshow AURA-5G can provide insights to the operators andhelp them re-dimension their networks for improved systemperformance Finally we conclude this paper in Section VIII

II STATE OF THE ART

User association has been an area of major interest eversince wireless networks came into existence Over the courseof those many years multiple intriguing algorithms andmethodologies have been proposed by academia and indus-try aiming to resolve the incremental challenges that userassociation presents as wireless networks continue to evolveConcretely while 2G and Wi-Fi networks utilized an RequiredSignal Strength Indicator (RSSI) Signal-to-Noise Ratio (SNR)based criterion for AP selection the increasing complexity ofthe networks has driven the growth of algorithms that utilizeTOPSIS [12] Fuzzy logic [13]ndash[15] Genetic Algorithms

[13] Multi Attribute Decision Making (MADM) [16]ndash[21]and Optimization theory [22]ndash[24] among others as toolsto accomplish an optimal user association Moreover theynot only aim to provision an optimal association based onmaximizing sum rate of users for example but they alsotry to optimize other system parameters such as interferencebandwidth allocation interference reduction etc

Specifically the authors in [25] consider an SDN enabledLTE network and utilize the global knowledge of the SDNcontroller so as to be able to distribute information aboutthe backhaul load of neighboring APs as well as the bot-tleneck backhaul link bandwidth for an AP of interest Sucha mechanism consequently allows for a more backhaul andAP load aware load-balancing mechanism Next in [26] theauthors propose a distributed user association policy whereinthey explore multiple dimensions to the user association prob-lem These dimensions explore the association policy whiletrying to optimize rate throughput minimize file transferdelays and load balancing They do so by varying a factorwhich characterizes each of these dimensions Further thedistributed nature encompasses the fact that part of the optimalsolution search is performed on the UE and a part of it isperformed on the AP Further authors in [27] utilize the k-nearest neighbors principle and the azimuth angle to determinethe next AP to associate to in a mobile environment Theyanalyze the pattern associated with a given k-value and thenutilize pattern recognition methods to determine the optimalAPs to associate to given an ultra-dense topology and severeinterference scenarios Moreover in [28] the authors utilizeMedia Independent Handover (MIH) and its correspondingservices so as to be able to execute a MADM algorithm Theycompare it with other simplistic counterparts such as AdditiveWeighting strategies to project the benefits of MIH basedMADM While these discussed strategies consider multipleconstraints and propose effective solutions they limit thenumber of APs that a UE can connectchoose to just one

As a consequence authors in [7] proposed a Dual Con-nectivity (DC) based User Association solution wherein theyformulate an optimization problem based on maximizing thesum throughput Since the problem is ridden with non-linearities and is NP-hard they propose a tractable solution toachieve the optimal solution Also in [8] the authors study theproblem of user association with the objective of maximizingthe weighted sum rate with user rate (minimum and maximum)constraints as well as with another objective of maximizingthe proportional fairness of the system Note that in [8] forDC the authors consider that a UE can be associated withan MC and an SC that is within the coverage of this selectedMC Next the authors of [29] consider the problem of userassociation for the uplink with power allocation optimizationbeing another objective They consider user minimum raterequirements as well as backhaul limitations at the pico BSsas the constraints and aim to find an association that reducesthe overall cost for the network while demands of all the usersare satisfied

Moreover in [30] the authors consider the downlink aspectof dual connectivity as well as the mmWave aided HetNetsCorrespondingly the authors develop a two stage iterative

algorithm for user association where their objective is tomaximize the total network throughput subject to the factthat the overall access-fairness amongst the users has to beimproved However for the analysis the authors consider avery small representative scenario which in essence does notrepresent the real world densities of SCs MCs and usersIn addition in [31] the authors propose an opportunisticmethod for cell selection in a DC scenario wherein the loadcharacteristics of the network are taken into account Howeveraccording to the network model considered for simulationstheir analysis is only limited to the sub-6 GHz scenariosAnother significant study in the direction of user associationfor 5G networks is in [32] The authors of this work analyzethe impact of wireless backhaul on user association Theoptimal association is determined such that it maximizes theoverall network throughput whilst not exceeding the backhaulcapacity limits

Lastly multiple studies such as [22] [24] [33] [34] proposea joint optimization approach towards user association Inthese studies the objective function is maximizedminimizeddepending on the utility and an optimal association strat-egy that provisions the same is determined Additionallythey also incorporate within their optimization approach asearch for the association that will lead to an optimal systeminterferenceenergy consumption regimespectrum allocationsystem etc Such mechanisms are termed as joint optimizationapproaches and through the use of binary decision variablesthey introduce another dimension of non-linearity to an alreadynon-linear optimization problem However a relaxation ofthese decision variables or a decomposition into simpler subproblems is in general possible And so in [22] [24] [33][34] such techniques have been utilized and a discussion onthe optimal solution obtained has been presented

III THE OPTIMIZATION FRAMEWORK MATHEMATICALFORMULATION AND SOLVER IMPLEMENTATION

The discussion with regards to the state of the art studiesin Section II and the contributions of this work in Section Iprovides us with the platform to now present the mathematicalformulation of our joint optimization strategy Subsequentlywe also discuss the challenges that need to be addressed foran efficient implementation of the AURA-5G framework

And so we consider a wireless heterogeneous networkscenario wherein 4G-LTE evolved NodeBs (eNBs) providethe role of MCs and the 5G next generation NodeBs (gNBs)function as the SCs We denote the set of MCs as M and theset of SCs as N We consider that the SCs are connected tothe MCs via a backhaul link This backhaul link can be eitherwired (fiber based) or wireless (mmWave based) Further theMCs have backhaul links to the core network However thesebackhaul links are only wired (fiber based) in nature Wedenote these aforementioned backhaul links as lt and =(where lt isin M and = isin N) for the ltCℎ MC and =Cℎ SCrespectively Further the capacity of each of the backhaul linksin the considered scenario is denoted as lt and = for theltCℎ MC and =Cℎ SC respectively Given the heterogeneouscharacteristic of the backhaul technologies their correspond-

ing capacities will also be different We elaborate more on thisin Section V wherein we describe the system model in detail

Next we specify the delay imposed by the backhaul linksas C where C = 1 3 Here 3 is the number of links inthe considered scenario Further 3 gt ( |M | + |N |) where|middot| denotes the cardinality of the set because each MC isdefined with a backhaul network that has one or more hopsto the core network However for the purpose of backhaulutilization analysis the multiple hops from any given MC canbe considered together as a single link This is so becauseall the wired hops from MCs are defined to have the samecapacity In addition for the SCs there is an additional hop(link) ie the connecting link to the MC which may be wiredor wireless depending on the operator deployment strategySimilar to the MC backhaul hops the wired SC to MC linksalso have the same capacity but less than that of the MC toCN links We provide numerical details with regards to thisin our system model description in Section V

The users within the HetNet are deployed using a homoge-neous poisson point process (HPP) and are denoted as 5 where 5 = 1 D and D being the total number of users withinthe scenario For the ease of understanding we introduce TableII which contains a list of all the variables constants andnotations that have been utilized for this work Given thesepreliminaries we state the objective of our user associationstrategy Concretely our objective is to maximize the overallsystem throughput in the downlink whilst adhering to thevarious constraints that the 5G HetNets will impose It isimperative to state here that the AURA-5G framework is alsoapplicable to the uplink Specifically in this work we considerthe backhaul capacity minimum required rate and path latency(one-way downlink delay) as the constraints for our jointoptimization problem Thus we frame our user associationstrategy as a MILP problem as follows

maxG8 9 68 9

G8 968 9F gt62 (1 + Ψ8 9 ) (1)

stsum8

G8 968 9F le 9 forall 9 (2)sum9

G8 9 le 2 forall8 (3)sum

68 9 le 1 forall8 9 (4)sum9

G8 9A8 9 ge 8 forall8 (5)sum8

G8 9A8 9 le 9 forall 9 isin N (6)sum8

G8 9A8 9 +sumC

bC 9 le 9 forall 9 isinM (7)

9G8 9 le 8 forall8 (8)G8 9 68 9 isin 0 1 forall8 9 (9)

where G8 9 indicates the association of user 8 to AP 9 Avalue of 1 signifies an active association and 0 defines thatthere is no association Further 68 9 defines the bandwidthassignment to a user 8 at AP 9 which has different available

TABLE IIDEFINITIONS LIST FOR NOTATIONS VARIABLES AND CONSTANTS

8 9 5 C Index variablesM Set of Macro-cells|M | Total Macro-cells in the systemN Set of Small-cells|N | Total Small-cells in the systemlt Backhaul link for Macro-cell lt= Backhaul link for Small-cell =lt Capacity of Backhaul link for Macro-cell lt= Capacity of Backhaul link for Small-cell =C Delay imposed by link C where C isin (1 3)3 Total number of links in the scenario 5 User 5 where 5 isin (1 D)D Total number of users in the scenario

G8 9Binary variable indicating association of user 8 with

68 9Binary variable indicating selection of bandwidth

option at AP 9 for user 8F Bandwidth option at an APΨ8 9 SINR registered by user 8 for AP 9

9 Total available bandwidth at AP 9

A8 9Composite variable defining the rate offered by AP

9 to user 8 with bandwidth option

bC 9Backhaul resource consumption by Small-cell C

associated to Macro-cell 9

9collection of links defining a path to the core

network from AP 9

8 Minimum Rate constraint for user 9

9Collection of backhaul capacities for the Macro and

Small cells

8Maximum bearable downlink latency [delay] for a

user 8

Binary variable introduced for linearization(Section IIA) indicating association of user 8 withAP 9 where bandwidth option has been assigned

+8 9 A constant representing F gt62 (1 + Ψ8 9 )

bandwidth options A value of 1 for any given 8 9 and

combination defines the fact that the bandwidth option atAP 9 for user 8 has been selected while a value of 0 for thesame defines vice versa In eq (1) which defines our totalsum rate maximization objective F is a constant value thatindicates the actual bandwidth resource in MHz for the option Next the constraint defined in eq (2) specifies that the totalbandwidth resources allocated to all the users associated withAP 9 cannot exceed the total available bandwidth 9 at AP 9 In eq (3) we define the dual connectivity constraint wherein auser can select a maximum of two APs As we will see later inthis section we modify this constraint to study both the singleand dual connectivity scenarios Subsequently the constraintin eq (4) guarantees that no more than one bandwidth optioncan be chosen by a user 8 at an AP 9

Next in eq (5) we specify the minimum rate constraintwherein the sum rate for a user 8 from the APs and thecorresponding bandwidth options it selects at those APs has tobe greater than minimum rate requirement 8 for every userin the scenario Here for the sake of brevity we also define acomposite variable A8 9 computed as 68 9F gt62 (1 + Ψ8 9 )which corresponds to the rate AP 9 offers to user 8 atbandwidth option in case there is an active associationbetween them ie G8 9 = 1 Note that 8 will depend on

the type of application a user accesses ie eMBB mMTCand URLLC applications will have different minimum raterequirements In this work we only consider the eMBB andmMTC applications and utilize the 3GPP 5G specifications[35] and other literature works such as [36] for their minimumrate requirements

In eqs (6) and (7) we introduce the backhaul capacityconstraint wherein the total allocated link rate to all the usersassociated to a given AP 9 cannot exceed the available linkbandwidth 9 It is important to state here that the backhaulcapacity constraints for the SCs (eq (6)) and MCs (eq (7))are characteristically different This is so because in theconsidered scenario an SC always has a backhaul link eitherwired or wireless to an MC Henceforth for the MC it ismandatory that we consider the contribution of the SCs as wellin order to ensure that the backhaul capacity constraint is notviolated Consequently in our MILP formulation in eq (7) weintroduce the term bC 9 which specifies the rate consumptionby SC C at MC 9 It is expressed by the left hand side ofeq (6) and is equivalent to the capacity of the backhaul linkutilized by all the users associated with SC C

Next in eq (8) we introduce the path latency constraintfor each user 8 We define 8 as the downlink latency (delay)that an application on user 8 can permit based on its QoSrequirements We also introduce an additional system variable 9 which specifies the cumulative latency offered by thelinks that connect AP 9 to the core network Here by a linkwe specifically mean a wiredwireless hop towards the corenetwork from the AP 9 Hence and as we will observe infurther detail in Section V different APs will offer differentlatency (delay) as is the case in real networks Consequentlythe constraint in eq (8) will assist the algorithm in selectingan association for all applications in the system that assuresthat their latency requirements are satisfied

Lastly in eq (9) we state that G8 9 and 68 9 are both binaryvariables Henceforth this preceding discussion concretizesour joint optimization objective wherein we not only aim tofind the right user-AP association ie G8 9 but also the possiblebandwidth allocation through 68 9 However it is important tonote here that the multiplication of G8 9 and 68 9 in our objectivefunction ie eq (1) and subsequently in the constraints in eqs(2) (5) (6) and (7) introduces a non-linearity To resolve thiswe perform a linearization operation which is detailed in thefollowing text

A Linearization

To avoid the non-linearity introduced by the multiplicativeterm involving two binary decision variables ie G8 9 and 68 9 in our optimization problem formulated in eqs (1)-(9) weperform a simplistic linearization operation that will enableus to apply our proposed user association strategy as a MILP

Firstly we introduce the linearization term in eq (10)wherein we replace the multiplicative quantity by a singlebinary variable

Γ8 9 = G8 968 9forall8 9 (10)

where Γ8 9 isin 0 1 A value of 1 denotes the active as-sociation of a user 8 with AP 9 with bandwidth option

allocated at this AP while 0 indicates vice versa Subsequentlywe replace G8 968 9 in eqs (1)-(9) with Γ8 9 In order tomake this linearization functional we will also need additionalconstraints that establish a relationship between Γ8 9 G8 9 and68 9 These additional constraints are as follows

Γ8 9 le 68 9 forall8 9 (11)Γ8 9 le G8 9 forall8 9 (12)Γ8 9 ge 68 9 + G8 9 minus 1 forall8 9 (13)

The aforesaid equations establish the necessary relationshiprequired between the linearizing variable and the variablescomprising the term that is being linearized Henceforth wenow present our modified MILP formulation as a result of theaforesaid linearization in eqs (14)-(25) as follows

maxΓ8 9

Γ8 9F gt62 (1 + Ψ8 9 ) (14)

stsum8

Γ8 9F le 9 forall 9 (15)sum9

68 9 le 1 forall8 9 (17)sum9

Γ8 9+8 9 ge 8 forall8 (18)sum8

Γ8 9+8 9 le 9 forall 9 isin (19)sum8

Γ8 9+8 9 +sumC

b primeC 9 le 9 forall 9 isin (20)

9G8 9 le 8 forall8 (21)Γ8 9 le 68 9 forall8 9 (22)Γ8 9 le G8 9 forall8 9 (23)Γ8 9 ge 68 9 + G8 9 minus 1 forall8 9 (24)G8 9 68 9 Γ8 9 isin 0 1 forall8 9 (25)

where +8 9 = F gt62 (1 + Ψ8 9 ) and is a constant since thevalues for both F and gt62 (1 + Ψ8 9 ) are definedcomputedbeforehand Further b prime

C 9represents the modified variable for

expressing the contribution of the SCs towards the backhaulutilization to an MC We introduce this modified variable toaccount for the linearization operation since the computationof bC 9 in eq (7) involves the multiplicative term G8 968 9 asobserved from our discussions regarding eq (6) and eq (7)

B Solver Implementation Challenges

While we may have linearized the system of equations forour optimization framework in Section IIIA unfortunatelynon-linearity still exists given that the variables G8 9 68 9 andconsequently Γ8 9 are binary in nature However we establishthat a simplistic approach wherein we ndash a) relax the binarynature of the aforesaid variables to bounded constraints and

b) threshold the solution values of these integral variables canhelp us avoid such non-linearities Moreover solvers such asGurobi allow the users to program optimization problems suchas ours and solve them using LP relaxation branch-and-boundand other advanced mixed integer programming techniques[11] And so we utilize this powerful characteristic of Gurobito solve our optimization framework and consequently deter-mine the optimal user association strategy

In addition and as mentioned earlier in Section I we havedeveloped an implementation framework named AURA-5Gthat also undertakes the tedious task of computing the linkSINR matrix The complexity of this process is highlighted bythe fact that in scenarios where there is transmit and receivebeamforming the computation of the link SINR matrix willrequire the system to know beforehand the beam directions ofall the APs Concretely for an UE of interest all the other UE-AP associations must be known so as to be able to computethe interference from the APs other than the AP of interestNote that an AP will only create interference at the UE whenthe transmit beam of the AP and receive beam of the UE arealigned with each other whole or in part

And so in the following text through a hypothetical sce-nario we show the complexity of computing the aforemen-tioned link SINR matrix Let us consider the scenario wherethere is a UE and possible APs to which this UE can attachto in any receive beam direction Let us also define a binaryvariable X isin 0 1 which indicates whether an AP throughits transmit beam creates interference for the AP of interestat the UE under observation ie X = 1 or it does not ieX = 0 Thus the total number of combinations of interferingbeams (APs) that needs to be explored to determine the valueof SINR for an AP of interest is given as 2(minus1)

Next and for the sake of simplicity we quantize the numberof possible receiver beam directions as Φ As a consequencenumber of computations required to determine the vector ofSINR values for a given UE can be expressed as

[(2minus1 + 2)]Φ (26)

where the additive term of 2 indicates an addition operationfor computing interference plus noise term and a divisionoperation for ultimately computing the SINR Thus from eq(26) it can be seen that the number of computations andhence the number of combinations that need to be exploredgrows exponentially with the number of candidate APs inscenarios where there is receive and transmit beamformingThis validates our earlier claim regarding the tediousness ofcomputing the SINR matrix

Certain works in literature such as [37]ndash[39] provideinsights as to how a statistical estimate for the SINR can beobtained in a beamforming scenario given a user and multiplecandidate APs However these works do not account for thepossibility of multiple users in the vicinity of the user ofinterest Thus we utilize a simplified process to determinethe SINR at any given UE in a beamformed regime whereinwe only consider the receiver beamforming at the UE andallow the APs to transmit in an omnidirectional manner Thisreduces the number of computations significantly because now

the remaining minus1 APs will create an interference for the APof interest Hence for Φ quantized receive beam directionsthe number of operations required are

[( + 1)]Φ (27)

Comparing eqs (26) and (27) we establish that for agiven UE our method utilizes significantly less number ofoperations to compute the SINR and hence overcome theearlier said challenge of computing the SINR matrix in abeamformed environment However it must be stated thatthe computed SINR estimate will be a lower bound on theactual SINR value This is so because we do not consider thetransmit beamforming on the APs Consequently we increasethe number of interferers in our computation comparedto those where both transmit and receive beamforming isutilized Notably though the efficacy of our analysis for theoptimization framework is further enhanced as it utilizes thelower bound ie worst case scenario for the SINR accordingto the preceding discussions

Following this optimization framework in the next sectionwe introduce the various scenarios that have been exploredin this work We also introduce the necessary modificationsto the constraints in the MILP framework to study thecorresponding scenarios

IV SCENARIOS EVALUATED

The optimization framework developed in Section III pre-sented the objective and the multiple real-network constraintsthat will be utilized when deciding the most optimal AP selec-tion for a given set of users and their corresponding locationsBased on this framework in this section we introduce themyriad scenarios that have been explored in this work Wealso present the necessary modifications if required in ouroptimization framework to study the corresponding scenariosTable III illustrates all the scenarios that have been discussed

A Deployment Strategies

For the analyzed scenarios we generate a set of topologiesby deploying the MCs SCs and users based on the parametersdefined in Table V Of specific interest amongst these is thedeployment of SCs within the scenario map While MCs areat fixed locations governed by the scenario map size and theMC inter-site distance the SCs are distributed based on anHPP around each MC The density is defined in [6] based onthe Metis-II project guidelines

Given these deployment characteristics we undertake astudy on scenarios where these SCs are deployed in a circleof radius 05 times ( (see Table V) termed as CircularDeployment from here on and scenarios where they aredeployed in a Square Deployment In the latter scenario theSCs are deployed in a square whose center is at the MClocation and the length of each edge is equal to the MC inter-site distance Note that while actual deployments will varydepending on operator requirements Circular Deploymentprovides a realistic and simple deployment strategy for the

TABLE IIIANALYZED SCENARIOS

CompositeScenario

CircularDeploy-

SquareDeploy-

mentAnyDC MCSC Interference

Limited Beamformed eMBBeMBB

CABE X minus X minus minus X X minusCMBE X minus minus X minus X X minusCAIE X minus X minus X minus X minusCMIE X minus minus X X minus X minusSABE minus X X minus minus X X minusSMBE minus X minus X minus X X minusSAIE minus X X minus X minus X minusSMIE minus X minus X X minus X minus

CABEm X minus X minus minus X minus X

CMBEm X minus minus X minus X minus X

CAIEm X minus X minus X minus minus X

CMIEm X minus minus X X minus minus X

SABEm minus X X minus minus X minus X

SMBEm minus X minus X minus X minus X

SAIEm minus X X minus X minus minus X

SMIEm minus X minus X X minus minus X

SCs Further and as we will see in Section V (Fig 5) aCircular Deployment strategy will lead to areas around MCedges where there will be no coverage via SCs Hence tocircumvent this issue we also explore the Square Deploymentscenario

The goal of including these deployment strategies into ourstudy is to give the operators an insight as to how differentdeployment characteristics can impact the system performancewhilst defining a UE to AP association map This will allowthem to understand the benefits and drawbacks of each of thesedeployment strategies with regards to the joint user associationand resource allocation problem Moreover it also provides aframework for the operators to introduce their own customdeployments and analyze the behavior of the user associationstrategy

B Service Classes

5G as has been discussed in Section I will cater to themultiple service classes ie eMBB mMTC and URLLC [35]As a consequence in this work we study the performance ofAURA-5G in the presence of only eMBB service requests aswell as for the case where eMBB and mMTC service requestsare generated simultaneously within the topology under studyto show the impact of provisioning of diverse services Notethat while the eMBB services will request significantly higherthroughput (we study the impact of the minimum rate require-ments of eMBB services in 5G as detailed later) mMTCservices due to their relatively higher density but low indi-vidual data rates will create bottlenecks in the access and thebackhaul networks for the eMBB service requests Howeverin this work for mMTC devices we consider the guard bandmode of operation Hence the mMTC devices do not consumeresources in the access network and just contribute towards theconsumption of BH resources In addition to these servicesURLLC services present the unique challenge of ensuring notonly low latency but also significantly high levels of reliability

from the network Nevertheless we postpone our discussionwith regards to URLLC services to a sequel of this work

C Directivity Regimes

In Section III B we briefly commented upon the fact that inthis article two scenarios depending on whether beamformingis used are explored Concretely we consider one scenariowherein all transmit and receive antennas have a 360 transmitand receive pattern respectively As we will detail next thiswill be an Interference Limited regime And so we also studythe behavior of AURA-5G in beamformed regimes whereinand for the sake of simplicity (See Section IIIB for details) weonly consider receiver beamforming at the UE We elaboratefurther on the aforesaid directivity regimes as followsbull Beamformed Regime In the scenarios where there is

beamforming we consider only receive beamforming atthe UE and utilize it for the purpose of calculating thevalues of the SINR ie Ψ8 9 for all user 8 and small cellAP 9 pairs2 For the macro-cells we employ sectorizationdetails of which are specified in our system model inSection V and allow the UEs to have isotropic receptionfor the frequencies at which the MCs operate

bull Interference Limited Regime Without beamformingthe environment is flooded with multiple interfering sig-nals which can deeply degrade the performance of thesystem Concretely for this scenario neither the SC andMC APs employ any sort of beamformingsectorizationnor do the UEs employ any receiver beamforming Thuswe also evaluate our MILP framework for user associa-tion in such a challenging network environment

D Dual Connectivity Modes

With the current 5G standardization ie Release-15 EN-DC and MR-DC have been formalized as one of the critical

2Note that transmit beamforming at the Small Cell APs can also beconsidered here However this complicates the computation of the SINR asdiscussed in Section IIIB

features for provisioning higher data rates for users Howevercurrent standards constrain the choice of small cells severelyby limiting them to the secondary cell group (SCG) specifiedby the MC [40] Hence in this paper we outline two DCstrategies that build on the current standards and explore thepossibility of either ndash a) MCSC having an MC and a possibleSC (not geo-restricted by the choice of MC) or b) AnyDCchoosing any two possible APs We elaborate further on theaforementioned DC connectivity modes as followsbull MCSC In this mode of DC a UE is required to attach

to an MC and select at most one SC The choice of MCdoes not geo-restrict the choice of SC as we attempt togo beyond the existing standards on SCG Additionallyit must be reiterated here that a connection does notguarantee bandwidth allocation as it is subject to theavailable physical resources at that moment Such ascenario can be seen as equivalent to the one where aUE is in a RRC connected inactive state at that AP [41]

And so for this DC mode the dual connectivity con-straint [eq (16)] in our optimization framework in SectionIII is modified tosum

9isinMG8 9 == 1 forall8 (28)sum

9isinNG8 9 le 1 forall8 (29)

recall that M and N as shown in Table II represent theset of MCs and SCs respectively Concretely eq (28)ensures that each UE selects an MC and eq (29) enablesthem to select at most one SC

bull AnyDC This mode for DC will permit the users to selectany two APs irrespective of the fact that whether they arean MC or an SC Consequently we also incorporate thisscenario in our study which as we will see in SectionVI rightly points towards the potential for improved per-formance but at a higher computational cost as comparedto MCSC scenarios By higher computational costs herewe refer to the convergence time to the optimal solutionwhich we study later in Section VIE

Hence to study the AnyDC scenario the dual connec-tivity constraint in our optimization framework in SectionIII is modified as followssum

9isinMcupNG8 9 == 2 forall8 (30)

E Baseline and Single Association

In our study we also analyze the single association andbaseline association scenarios as benchmark solutions Con-sequently we conduct a performance comparison of our userassociation and resource allocation strategy ie AURA-5Gwith these scenarios based on the obtained performance met-rics from the DC modes discussed in Section IVD

We further elaborate on these two association strategies inthe text that follows

bull Single Association As the name suggests in this sce-nario we enable the UE to connect to at most one APThis is in essence what current day wireless networksoffer And so we modify the dual connectivity constraintin eq (16) to sum

9isinMcupNG8 9 le 1 forall8 (31)

Note that the Single Association (SA) scenarios alongside the DC mode scenarios are of significant interestsince they will be prevalent in situations where it is notpossible for the network to allocate resources on twoAPs for a given UE Henceforth we observe and analyzetheir performance along side the DC mode scenarios andcompare it with the baseline scenario which we elaborateupon next

bull Baseline Association For the baseline scenario we adoptthe user association strategy that is being used by currentday mobile networks Wi-Fi etc Concretely we utilizeAlgorithm 1 wherein we first compute the SNR thatthe users would observe from each AP Based on thisobserved SNR we associate the users to the AP with thebest SNR However to compute the achieveable data ratewe utilize the SINR (Ψ(8 9)) at the UE for the chosenAP Given these UE-AP pairs the bandwidth () at anygiven AP is then divided equally amongst all the UEsassociated to it

Algorithm 1 Baseline Scenario Generation1 procedure BASELINEGENERATOR2 _B4A larr Number of Users3 _Blarr Number of Access Points4 larr Vector of data rates for all users5 larr Vector for number of users per AP6 lt_83 larr Index of the AP with the highest SNR for user i7 larr I4AgtB(_B)8 larr I4AgtB(_B4A)9 8 9 larr 1

10 8C4A_DB4A larr _B4A11 8C4A_larr _B12 for 8 lt 8C4A_DB4A do13 for 9 lt 8C4A_ do14 ((8 9) larr SNR of user i from AP j15 Ψ(8 9) larr SINR of user i from AP j16 lt_83 larr 5 8=3 (lt0G(((8 )))17 (lt_83) larr (lt_83) + 118 for 8 lt 8C4A_DB4A do19 83G larr 5 8=3 (lt0G(((8 )))20 (8) larr (

(83G) )gt62 (1 + Ψ(8 83G))

F Constraint Based Scenarios

In addition to different combinations of topology DC modeDirectivity and Service Class based scenarios in our study weintroduce an amalgamation of different network constraintslisted in Table IV as well These myriad combination ofconstraints are then combined with the scenarios in Table

III following which they are optimized and analyzed bythe AURA-5G framework for user association and resourceallocation

TABLE IVCONSTRAINT COMBINATIONS FOR SCENARIOS

ConstraintCombina-

tionDescription

MRT Minimum Rate Constraint for eMBB services

CBThe wired backhaul link capacity is capped For SCswe cap the capacity of the backhaul to the MC at 1Gbps while for the MC to the CN it is 10 Gbps [42]

eMBB applications will also have latency con-straints although not as strict as the URLLC ap-plications However taking the requirements intoaccount we also explore the impact of constrainedpath latency

MRT + CB Minimum Rate Requirements and Constrained Back-haul together

MRT + CPL Minimum Rate and Constrained Path Latency con-straints together

MRT + CPL+ CB

Minimum Rate Constraint Constrained Backhauland Path Latency constraint all need to be satisfiedsimultaneously

CB + CPL Backhaul and Path Latency constraints are employedtogether

As we will see from our observations in Section VI differ-ent combinations of these constraints on the scenarios analyzedhave significant impact on the performance metrics Furtherinteresting insights that can be utilized by the operator toenhance the performance for the purpose of UE-AP associationas well as resource allocation have been outlined

V EVALUATION FRAMEWORK

In this section we establish the evaluation framework thatwe have considered for analyzing the multiple scenariosdescribed in Section IV by utilizing the optimization frame-work developed in Section III Concretely we consider thetopology as shown in Fig 2 with a geographical area of600lt times 600lt The scenario under investigation consists ofa heterogeneous multi-layered radio access deployment Forthis we consider the 4G-LTE eNodeB as the MCs operating atthe sub-6GHz frequency range specifically at 355I withan inter-site distance of 200lt [6] Further we deploy SCsutilizing a homogeneous poisson point process (HPP) withinthe vicinity of each MC Note that we repeatedly generate thelocation coordinates for the SCs using the aforementionedHPP until they have a minimum of 20lt inter-site distance[6] In addition they operate on the mmWave frequency rangeof 27I for the access network ie from SC to user andat 73I for the possible wireless backhaul to the MC inaccordance with [43] [44]

These SCs are then connected to an MC either via awireless or a wired backhaul link We utilize the fact thatSCs operating in the mmWave frequency range due to theoperational characteristics of mmWave ie high atmosphericabsorption and severe blockage from the various objects in thetransmission (TX) path will have a significantly reduced TXrange as compared to the MCs [45] Henceforth we specify abreakpoint distance of 25lt from the MC beyond which SCs

Users Macro Cells Small Cells

Fig 2 Illustrative example of the network topology under study

are linked to the MC using a wired backhaul link This inturn implies that the SCs within the aforementioned distanceof the MC connect to it via a wireless link Further we specifyan out of band operation regime for these wireless backhaullinks wherein the total available bandwidth is divided equallyamongst the SCs attached to a given MC Thus to compute theavailable capacity on this link we utilize the Shannon-Hartleytheorem specified in eq (32)

= times gt62 (1 + Ψprime) (32)

where is the channel capacity is the transmissionbandwith and Ψprime is the calculated signal to noise ratio betweenthe SC and MC Additionally for the backhaul network weconsider relevant wired technologies as specified in [42] anddeploy a 10Gbps capacity fiber link from the MC to the corenetwork The wired backhaul link between SC and MC hasa capacity of 1 Gbps Note that we dimension the backhaullink capacities such that an MC is able to serve all the SCsconnected to it

Next we specify that each MC is connected to the corenetwork via wired links The number of hops for a given MC tothe core network is chosen from a discrete uniform distributionover 1 to 4 hops Further each of the wired links within ourdefined topology imposes a delay of 1ms [42] Additionallysince the SCs can have a wireless backhaul link to the MCwe define that a wireless link also imposes a 1ms delay [46]

We then deploy the users in the scenario area by utilizing aHPP As specified in our discussions in Section IV we considerscenarios where either only eMBB devices or both eMBB andmMTC devices exist Hence for the purpose of analysis weconsider the various user densities for both eMBB and mMTCdevices as listed in Table V We simplify our evaluationframework by utilizing the fact that mMTC devices operatein the guard band mode Hence they consume only backhaulresources in our evaluation framework Further in [36] it isstated that mMTC devices generate traffic between 1 Kbpsand 1 Mbps mostly Consequently we consider a uniformdistribution between 1-1000 Kbps and utilize it to compute thebackhaul network resources consumed by the mMTC devices

TABLE VEVALUATION PARAMETERS

Parameter Description Value Parameter Description Value

Speed of Light (2) 3 times 108 msLTE eNB (Macro Cell) operating

frequency 355 GHz

Small-cell access network operatingfrequency 27 GHz Small-cell backhaul operating

frequency 73 GHz

Height of user 15 m Height of Small-cell 10 mHeight of Macro-cell 25 m Simulation Area 036 times 106m2

Number of eMBB users [150175200225250275] Density of mMTC users 24000 per MCTransmit Antenna Gain for MCBS 17 dBi Transmit Antenna Gain for SCBS 30 dBi

Macro-cell Transmit Power 49 dBm Small-cell Transmit Power 23 dBmUE Receive Gain for Small-cell 14 dBi UE Receive Gain for Macro-cell 0 dBiSmall-Cell Bandwidth for access

network 1 GHz Macro-cell Bandwidth for accessnetwork 80 MHz

Number of hops from MC to corenetwork isin [14] White Noise Power minus174 dBmHz

Break point distance for wirelessbackhaul between SC and MC 25 m Macro-cell Intersite distance

(ISDMC) 200 m

Small-cell Intersite distance 20 m Minimum Rate for eMBB users 100 MbpsWired Backhaul Capacity from SC

to MC 1 Gbps Wired Backhaul Capacity from MCto core network 10 Gbps

Wireless link delay 1 ms Wired link delay 1 msMaximum Permissible latency for

eMBB services 3 ms Number of Iterations for evaluation 100

Small-Cell bandwidth options forusers (BWSC) [50 MHz 100 MHz 200 MHz] Macro-cell bandwidth options for

users (BWMC)[15 MHz 3 MHz 5 MHz 10

MHz 20 MHz]

Data rate range for mMTC services isin [11000] Kbps UE receive beam Half PowerBeamwidth (HPBW) 45

Pathloss Exponent (Small Cell andLOS condition) 21 SF Std deviation (Small Cell and

LOS condition) 44

Pathloss Exponent (Macro Cell andLOS condition) 20 SF Std deviation (Macro Cell and

LOS condition) 24

Pathloss Exponent (Small Cell andNLOS condition) 32 SF Std deviation (Small Cell and

NLOS condition) 80

Pathloss Exponent (Macro Cell andNLOS condition) 29 SF Std deviation (Macro Cell and

NLOS condition) 57

Minimum Rate Requirement(eMBB services) 100 Mbps Latency Requirement (eMBB

services) 3 ms

Optimizer Cutoff Time 600 seconds Number of SCs per MC isin [310]

We also specify the bandwidth options that a UE has froma given SC as well as an MC [47] In addition for the channelmodel we adopt the NYU CI model [43] [48] which isexpressed as follows

= ( + 10=gt610 (330) + -f (33)

where defines the pathloss in dB ( [in dB] iscomputed as 20gt610

(4c 5 30times109

) 30 is 1m and -f denotes

the shadow fading component with a standard deviation off Based on the experiments carried out in [43] we adopt

the pathloss coefficient ie the value of = and the standarddeviation f for shadowing These values have been specifiedin Table V Note that we consider both the Urban Micro (U-Mi Street Canyon) and the Urban Macro (U-Ma) scenariosin [43] [48] as they are reflective of the scenarios that willprevail for SC and MC respectively in a dense urban environ-ment Moreover we also take into account the possibility ofencountering obstacles in such dense urban environments bysimulating the LOS-NLOS probability models for U-Mi StreetCanyon and U-Ma scenarios as specified by 3GPP in [49] Toillustrate here for U-Mi Street Canyon the LOS probabilitymodel is expressed as shown in eq (34) where 32 is the

1 32 le 18lt

1832+ 4G (minus

3236 ) (1minus

18lt lt 32(34)

1 32 le 18lt[ 18

32+ 4G (minus

3263 ) (1minus

1832) ] [1 + prime(ℎ) ) 54 ( 32

100)24G (minus

32150 )

] 18lt lt 32

prime(ℎ) ) =

0 ℎ) le 13lt( ℎ) minus1310

)15 13lt lt ℎ) le 23lt

two dimensional distance between transmitter and receiverFurther the U-Ma LOS probability model has been expressedin a manner similar to the U-Mi model in eqs (35) and (36)where ℎ) represents the height of the user terminal ie UE

Lastly we also provision parameters such as the MC heightSC height UE height transmit and receive gains intersite dis-tances as well as QoS requirements for the services discussedin this paper (minimum rate and latency) These parametershave been derived utilizing 5GPPP project proposals [6] 3GPPspecifications [50] and other relevant research efforts [44]

Given the setup detailed so far we perform 100 MonteCarlo runs for each scenario and constraint combinationThese Monte Carlo trials help us attain a certain measure ofconfidence over our observations Additionally we also definea cutoff period of 600 seconds for our optimizer to determine asolution for the user association problem The reason for sucha cutoff timer being in any dynamic network environmentsuch a time period would be more than sufficient to determinean optimal association And so we now list all the other

simulation parameters along side the parameters discussedthus far in this section and their corresponding descriptionand values in Table V

With this background in the next section we evaluate theperformance of the AURA-5G framework based on Totalnetwork throughput System fairness Backhaul utilizationLatency compliance Convergence time and Solvability It isimportant to state here that the scenarios detailed in SectionIII along side the parameters elaborated upon earlier in thissection provision a very realistic scenario As a consequencethis accentuates the efficacy of our framework in provisioninga realistic and implementable framework for industry andacademia

VI RESULTS AND DISCUSSIONS

Based on the evaluations performed by utilizing the AURA-5G framework in this section and Section VII we consolidateand discuss our findings in detail We structure our discussioninto two phases wherein at first (in Section VI) we consider

160 180 200 220 240 260 280

Number of eMBB users

SADCDC + MRTSA + MRTDC + CBDC + CB + CPL

DC + CPLSA + CPLSA + CBSA + CB + CPLBaseline

(a) CABE

160 180 200 220 240 260 280125150175200

(b) CMBE

160 180 200 220 240 260 280050

(c) CAIE

160 180 200 220 240 260 280040608

(d) CMIE

Fig 3 Total Network Throughput for multiple combination of constraints being employed on (a) CABE (b) CMBE (c) CAIE and(d) CMIE scenarios

the setup that utilizes the evaluation parameters in Table V asis The observations for this setup considers scenarios withonly eMBB users and eMBB and mMTC users togetherSecondly based on crucial observations from the first phasein Section VII we perform network re-dimensioning and thenpresent details on how AURA-5G can assist the operator ingaining insights that will ultimately lead to improved systemperformance through re-dimensioning

We now proceed towards our discussion and reiterate thatwe utilize the notations presented in Tables III and IV for themyriad scenarios explored in this paper

A Total Network Throughput1) eMBB services based scenarios For the scenarios where

users with only eMBB services are considered we present ourobservations with regards to the total network throughput inFigs 3 and 4 For the purpose of comparison we also includethe observations from the baseline scenario in Figs 3 and 4Note that the observations presented have been obtained after

averaging the total network throughput over 100 Monte-Carlotrials In addition the Minimum Rate (MRT) constraint (seeTable IV for description) due to its strict nature can leadto circumstances where either an optimal solution does notexist or it takes too long (greater than 600 seconds) for theoptimizer to search for one In either case we consider thesesimulation trials to be unsuccessful with regards to finding anoptimal solution and hence exclude them from the evaluationof the AURA-5G framework for the total network throughputsystem fairness backhaul utilization and latency compliancemetrics We refer the reader to Section VIF and VII wherein amore detailed discussion with regards to the issue of solvabilityand how it is addressed has been provided Henceforth forthe total network throughput analysis we now evaluate theCABE CMBE CAIE and CMIE scenarios in Figs 3(a)-(d)where multiple combination of constraints (specified in TableIV) beamformed regime and circular deployment have beenconsidered

From Figs 3(a)-(d) firstly we deduce that the AURA-5G

160 180 200 220 240 260 280125150175

(a) SABE

160 180 200 220 240 260 280125150175

(b) SMBE

160 180 200 220 240 260 280

(c) SAIE

160 180 200 220 240 260 280050

(d) SMIE

Fig 4 Total Network Throughput for multiple combination of constraints being employed on (a) SABE (b) SMBE (c) SAIE and (d) SMIE scenarios

Fig 5 Circular and Square deployment characteristics for SCs around MCs

framework outperforms the baseline scenario for all set ofconstraints and scenarios Next the DC scenarios outperformthe corresponding SA scenarios when AnyDC is employed(Fig 3(a)) However when MCSC is employed the gains arenot as significant (Fig 3(b)) because with DC in MCSC theUEs are connected to one MC and can additionally connectto at most one SC Further in SA due to the nature ofour optimization methodology being to maximize the totalsum rate the UEs are associated mainly to the SCs Hencethe gains for DC scenarios in the MCSC setup are not assignificant as those in AnyDC Moreover from Figs 3(a) and(b) it can be observed that constrained path latency (CPL) andbackhaul (CB) severely impact the overall network throughputThe reason being that the APs with the best available SINRand hence capacity might not be able to satisfy these latencyand backhaul constraints

We then consider the interference limited regime based sce-narios ie CAIE and CMIE in Figs 3(c) and (d) Immediatelywe can observe a significant reduction in the total networkthroughput as compared to that in the beamformed regime(Figs 3(a) and (b)) This is inline with our expectationssince the SINR in an interference limited scenario will besignificantly degraded as compared to that observed in thebeamformed regime Further due to this interference limitednature of the scenarios in Figs 3(c) and (d) we do not observea significant gain in performance from AnyDC over MCSC

Next we analyze the square deployment based scenariosie SABE SMBE SAIE and SMIE in Figs 4(a)-(d) Toreiterate in square deployment based scenarios the SCs aredistributed in a square geometry around each MC to whichthey have a backhaul link Given these scenarios from Figs4(a)-(d) the generic trend of observations does not changecompared to the circular deployment Concretely we observethat the AURA-5G framework given any set of constraintcombinations and scenarios always outperforms the baselinescenario (beamformed regime scenarios perform better thantheir interference limited regime counterparts (Figs 4(a)-(b)and 4(c)-(d)) AnyDC based DC scenarios have a significantperformance gain over SA scenarios which is not the casewith MCSC based DC scenarios (Figs 4(a)-(b) and 4(c)-(d))

and latency and backhaul constraints significantly reduce thetotal network throughput (Figs 4(a)-(d))

However in addition to these aforesaid observations thesquare deployment based scenarios provision approximately6 increase in total network throughput across all the con-straint combinations explored except when backhaul capacityconstraints are applied The reason beingbull In a circular deployment based scenario the SCs are

deployed around the MCs such that there will be blindspots ie there will be areas where there is weak or noSC coverage at all since circular geometries leave emptyspaces where their edges meet However with a squaredeployment scenario the probability that there are suchblind spots is less as square geometries do not leaveany empty spaces like their circular counterparts Thisconsequently gives users in these geographically transi-tional areas a better opportunity to connect with an SCAn illustration to highlight the aforesaid characteristic ofcircular and square deployments has been shown in Fig5

bull A square deployment configuration however means thatthe probability that SCs are further away from the MCis higher This consequently increases the probability ofemploying a wired backhaul from SC to MC which ascan be seen from our emulation framework in SectionV might have lower capacity than the mmWave basedwireless backhaul Hence this reduces the overall datacarrying capacity of the network which corresponds tothe stated reduced total network throughput as comparedto the circular deployment when backhaul capacity con-straints are employed

2) mMTC with eMBB services based scenarios For thescenarios where both mMTC and eMBB services co-existwe firstly re-iterate the fact that the mMTC devices onlyconsume backhaul resources (see Section V) Hence the maintotal network throughput characteristics stay similar to thoseobserved for the scenarios where only eMBB services existHowever certain scenarios where we take into account thebackhaul capacity constraints show interesting observationsConsequently in Figs 6(a)-(d) for the CABE CABEm CAIEand CAIEm scenarios we illustrate the total network through-put only when CB and CPL (see Table IV for descriptions)constraints have been imposed Note that the above mentionedscenarios are all circular deployment based

Concretely from Figs 6(a)-(d) we observe that the presenceof mMTC devices leads to a reduction in the overall networkthroughput experienced by the eMBB services This is alongexpected lines given that the mMTC devices as stated beforeconsume a portion of the available backhaul capacity Inaddition from Figs 6(a) and (c) it can be seen that the totalnetwork throughput for the beamformed regime (Fig 6(a))is much higher than that observed in an interference limitedregime (Fig 6(c))

Next we consider through Figs 7(a) and (b) scenariosCMBEm and CMBE where MCSC configuration for DCmodes is utilized We observe that for the scenarios wheremMTC services also exist along side the eMBB services thetotal network throughput achieved by the eMBB services is

(a) CABE (b) CABEm

(c) CAIE (d) CAIEm

Fig 6 Total Network Throughput for multiple combination of constraints being employed on (a) CABE (b) CABEm (c) CAIE and (d) CAIEm scenarios

lower The reasoning as has already been stated before inthis section is that the mMTC devices consume a portionof the available backhaul capacity thus reducing the overallachievable throughput for the remaining services in the systemWe further present the Square Deployment scenarios in aninterference limited regime in Figs 7(c) and (d) and comparethem with their circular deployment counterparts presented inFigs 6(c) and (d) We deduce that as compared to the circulardeployment scenarios in Figs 6(c) and (d) the total networkthroughput observed for the square deployment scenarios islower when CB and CPL constraints are considered Thereason being and we re-iterate that a square deploymentconfiguration leads to a higher probability of the SCs beingfurther away from the MC As a consequence this increasesthe probability of employing a wired backhaul from SC toMC which might have lower capacity than the mmWavebased wireless backhaul Hence this reduces the overall data

carrying capacity of the network which corresponds to thestated reduced total network throughput as compared to thecircular deployment

B System FairnessWe analyze the fairness of our optimization based frame-

work through the Jainrsquos Fairness Index [51] for the scenariosexplored in this work The fairness index is computed for theconstraint combinations and scenarios that have been discussedin Section VIA and then a detailed discussion has beenprovided It is important to state here that the objective ofevaluating the AURA-5G framework for fairness measure isto be able to study the impact of various constraints andscenarios prevalent in the 5G networks on the fairness offeredby the system given the objective function of total sum ratemaximization (Section III)

1) eMBB service based scenarios In Figs 8(a)-(d) theJainrsquos fairness index for the scenarios CABE CMBE CAIE

(a) CMBE (b) CMBEm

(c) SAIE (d) SAIEm

Fig 7 Total Network Throughput for multiple combination of constraints being employed on (a) CMBE (b) CMBEm (c) SAIE and (d) SAIEm scenarios

and CMIE with the different constraint combinations havebeen illustrated Specifically from Fig 8(a) we observe thatthe AURA-5G framework in the Single Association (SA)setup provisions a higher fairness as compared to the DualConnectivity setup except when backhaul capacity constraintsare considered The reason being since SA allows the UEsto connect to at most one AP network resources are moreevenly distributed and hence more users are able to connectand reserve resources in the network However since inDC the UEs have the possibility of connecting to two APsthe amount of resources available per user in the networkis significantly less Hence the disparity in the amount ofresources reserved by the users in DC modes is much higherThis as a consequence results in the aforementioned fairnesscharacteristic However as can be seen in Fig 8(a) whenwe consider the Minimum Rate (MRT) requirement constraint

there is a slight improvement in the fairness because thealgorithm tries to find a solution wherein each user is allocatedat least 100 Mbps This forces the system to allocate resourcesmore fairly so as to satisfy the minimum rate constraint andthus results in the marginal increase in fairness as observed

Further in Fig 8(a) we observe that the imposition ofpath latency (CPL) and backhaul capacity (CB) constraintsresults in significant lowering of the overall system fairnessThis is as a consequence of only a small subset of APs andbackhaul paths being able to satisfy the set constraints Hencein order to satisfy these requirements the UEs share the limitedavailable resources on these candidate APs and backhaul linksMoreover given that the algorithm aims to maximize the totalsum rate of the system UEs with better SINR are allocatedbetter bandwidth resources from the limited available systemresources Hence this creates significant disparities between

(a) CABE

(b) CMBE

(c) CAIE

(d) CMIE

Fig 8 Jainrsquos Fairness index deviation measure for multiple combination of constraints being employed on (a) CABE (b) CMBE (c) CAIE and (d) CMIEscenarios

the throughput of different users thus leading to a reduction insystem fairness Lastly DC scenarios provision better fairnessthan the SA scenarios when backhaul capacity (CB) constraintis applied This is so because the users have the opportunityto compensate for the lack of backhaul capacity resources inone link by acquiring bandwidth resources in the other linkconnected to the second AP selected However in SA thelack of backhaul capacity resources combined with the natureof the optimization algorithm to maximize the total sum rateleads to a significant disparity in how the system resources areallocated to the users

Next in Fig 8(b) for the CMBE scenario wherein MCSCsetup is utilized an overall improvement in the system fairnessin the DC modes is observed This is as a result of the fact

that the users are now forced to select one MC amongst thetwo APs they choose Hence this relieves resources fromthe SCs which are the main drivers for maximizing the totalsum rate of the system However this was not the case inthe AnyDC scenario wherein users could select even twoSCs As a consequence MCSC provides more users with theopportunity to select an SC and maximize their possible datarate which leads to the improvement in the system fairness asstated before Moreover and as expected for the interferencelimited regime scenarios shown in Figs 8(c) and (d) thefairness measures are significantly lower as compared to thebeamformed based scenarios (Figs 8(a) and (b)) Given thesevere interference and the objective of maximizing sum rateonly a select few users will have a good SINR which as a

(a) SABE

(b) SMBE

(c) SAIE

(d) SMIE

Fig 9 Jainrsquos Fairness index deviation measure for multiple combination of constraints being employed on (a) SABE (b) SMBE (c) SAIE and (d) SMIEscenarios

consequence will receive the maximum share of the networkresources Hence this leads to the severe disparity in theachievable rate per user which subsequently explains the dropin the system fairness Note that rest of the trends in systemfairness measures for the interference limited regime scenariosfollow those already observed for the beamformed regimescenarios

Following the discussion for circular deployment basedscenarios we next consider the square deployment basedscenarios ie SABE SMBE SAIE and SMIE in Figs 9(a)-(d)From these figures we observe that the generic trend for thefairness measure is similar to those observed for the circulardeployment scenarios (discussed above) However the squaredeployment for certain constraint combinations and scenarios

enhances the overall system fairness An example being theSABE scenario wherein for all constraint combinations weobserve between 5-6 improvement in system fairness Thisis because of the reasons we have already elaborated in SectionVIA2 ie square deployments result in less blind spotswithin the deployment hence resulting in a fairer allocation ofresources to the users as compared to the circular deployment

2) mMTC with eMBB based scenarios For the scenarioswhere mMTC and eMBB services are considered togetherwe present our observations through Fig 10 It can be seenfrom Figs 10(a)-(d) that the fairness index does not changesignificantly as compared to the fairness measure observed ineMBB only scenarios even though we consider the mMTCdevices within our framework The reason for such a behavior

(a) CABE

(b) CABEm

(c) CAIE

(d) CAIEm

Fig 10 Jainrsquos Fairness measure for multiple combination of constraints being employed on (a) CABE (b) CABEm (c) CAIE and (d) CAIEm scenarios

is two-folds Firstly the fairness is computed by utilizingthe throughput experienced by each individual eMBB userin the system which is a function of the access networkresources And secondly the mMTC users operate in theguard band thus not consuming any access resources fromeMBB users Henceforth there are very slight variations inthe fairness index measure as mMTC users only impact thesystem performance through backhaul resource consumption

Further we also considered scenarios with square deploy-ment and circular deployment along side the MCSC setup Andsimilar to the aforesaid deductions we observed negligiblechange in the fairness index when mMTC and eMBB servicesare considered together as compared to when only eMBBdevices are considered Note that for the sake of brevity wedo not present the illustrations for any other scenarios except

those presented in Fig 10

C Backhaul Utilization

The primary goal for analyzing the backhaul utilizationafter the AURA-5G framework has been implemented oncertain scenarios is to determine if the existing backhaul setupwherein we consider a combination of wired and wirelessbackhaul links with the wired links having capacities of1Gbps and 10Gbps (Section V) is a bottleneck Further wealso utilize this analysis to understand the compliance of theAURA-5G framework with the backhaul capacity constraintsFor the sake of brevity and as we have done throughout thispaper for this analysis we select a subset of representativescenarios Hence through Figs 11 and 12 we depict thebackhaul utilization as observed for CABE CMBE CAIE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76

Access Points

Constrained BackhaulUnconstrained BackhaulMaximum Available SC capacityMaximum Available MC capacity

(a) CABE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76

Access Points

(b) CMBE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76

Access Points

(c) CAIE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76

Access Points

(d) CMIE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72

Access Points

(e) SABE

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72

Access Points

(f) SAIE

Fig 11 Backhaul Utilization for Dual Connectivity (DC) and DC with Backhaul Capacity constraints in (a) CABE (b) CMBE (c) CAIE and (d) CMIEscenarios Red colored AP indices are for MCs and the rest for SCs

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62

Access Points

(a) CABEm

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62

Access Points

(b) CAIEm

Fig 12 Backhaul Utilization for Dual Connectivity (DC) and DC with Backhaul Capacity constraints in (a) CABEm and (b) CAIEm scenarios Red coloredAP indices are for MCs and the rest for SCs

CMIE SABE SAIE CABEm and CAIEm scenarios Thechoice of the aforesaid scenarios stems from the fact that theseselected scenarios include the MCSC and AnyDC setup beam-formed and interference limited regime square deploymentsetups as well as the mMTC and eMBB services together inthe beamformed and interference limited scenarios alongsideAnyDC setup This set of scenarios owing to their diversityand challenging nature give the necessary and sufficient ideawith regards to the backhaul utilization characteristics

From Figs 11(a)-(f) and 12(a)-(b) we firstly observe thatthe AURA-5G framework is successful in satisfying the back-haul capacity constraints as and when they have been imposedHere by backhaul capacity constraints we mean that the wiredbackhaul links are capped by their designated capacities asstated in Table IV Further the wireless backhaul links areconstrained by the capacity computed utilizing the Shannon-Hartley formula based on their corresponding SINR value Itis also important to state here that on the vertical axis inall the subplots presented in Figs 11 and 12 we representthe difference between the demand and the available capacityHence a negative value on the vertical axis indicates that thebackhaul resources on the corresponding AP have not beenfully utilized whilst a positive value indicates over-utilizationby the corresponding amount And so we can see that forthe unconstrained scenarios the backhaul resources are alwaysover-utilized However for the backhaul capacity constrained(CB) scenarios we observe that our framework succeeds infinding an optimal solution without over-utilizing the totalavailable backhaul resources This significant difference inbackhaul utilization also reflects the greedy nature of theoptimization framework whose objective is to maximize thetotal network throughput Note that we have also indicated themaximum available backhaul capacity amongst the SCs andMCs This assists the readers in understanding the maximumdata carrying capacity that the set of SCs and MCs in thenetwork have as well as in exemplifying the fidelity of the

AURA-5G framework Concretely the maximum capacity onan SC should not exceed that on an MC This is so becauseall SCs in our framework route their traffic through the MCHence a higher maximum bandwidth availability on the SC ascompared to an MC would be equivalent to trying to balancea big box on a thin needle Additionally and for the reasonsas stated above from the backhaul utilization values for theunconstrained setup we observe that the backhaul through theMCs is significantly over-utilized as compared to the SCs

Next we observe that scenarios wherein beamforming hasbeen applied ie Figs 11(a) (b) (e) and 12(a) are severelylimited for backhaul resources The reason being that theblue bars (darker bars if viewed in black and white) whichindicate the available backhaul capacity in the constrainedsetup are extremely small This indicates that nearly all ofthe available capacity has been utilized even without theMinimum Rate constraints being applied The reason beingthat beamforming results in an improved SINR measurewithin the system This consequently enables the users toachieve a better throughput and hence the aforementionedbackhaul utilization characteristic Thus an important insightfor network operators suggesting a requirement for networkre-dimensioning (Section VII) can also be drawn from theseobservations Further in Figs 11(c) (d) (f) and 12(b) whereinthe interference limited regime has been adopted the overallbackhaul utilization in the unconstrained setup is much lowerthan that observed for the scenarios involving beamformedregime This is as a result of the severe interference causinga significant loss in SINR and hence per user throughputThis claim is also corroborated by the reduction in networkthroughput observed in Section VIA for interference limitedscenarios

Lastly through Figs 12(a) and (b) wherein the mMTCservices have been considered alongside the eMBB servicesit can be observed that the AURA-5G framework is able toprovision optimal user-AP associations whilst adhering to the

backhaul capacity constraints Furthermore as compared tothe corresponding scenarios where only eMBB services arepresent ie CABE and CAIE the backhaul utilization forthe constrained backhaul case in CABEm (Fig 12(a)) andCAIEm (Fig 12(b)) scenarios is slightly higher This is sobecause in addition to the eMBB services the mMTC servicesalso consume a portion of the backhaul resources Hence theoverall increase in backhaul utilization

D Latency Requirement Compliance

As part of our fidelity analysis for the AURA-5G frame-work we delve into how it satisfies the specified servicelatency requirements through Figs 13(a)-(f) It is important tostate here that the latency (or the downlink delay which we de-fine as latency in our work) is governed by the number of hopsthe data has to traverse to reach the user from the core networkHence we consider certain representative scenarios such asCABE CMBE SAIE SMIE CMIEm and CAIEm for ouranalysis These scenarios encompass the AnyDC and MCSCsetup the beamformed and interference limited regimes aswell as the eMBB only and eMBB with mMTC services basedsetups Note that we do not include the last wireless hop ieMC or SC to the UE in our optimization framework as it doesnot induce any variability within the scenario given that it willbe omnipresent whatever the given association be Hence wefocus on the number of hops that the data has to traverse fromthe CN to the AP

From Figs 13(a) (c) and (e) wherein the AnyDC setupis employed we observe that the density of users with 3ltBlatency is higher as compared to the ones where MCSC setupis employed ie in Figs 13(b) (d) and (f) This is so becausein AnyDC to maximize the total sum rate our algorithm triesto find SCs first for all the users However in the MCSCsetup we force our algorithm to find atleast one MC foreach user Hence given the fact that an SC is connected tothe CN through a corresponding MC the latency incurredby the users in the MCSC scenarios is comparatively less ascompared to the AnyDC scenario The operators can utilizethis insight to design access point selection schemes whereinfor services that can tolerate higher delays the AnyDC setupmaybe employed whereas for services with extreme latencyconstraints an MCSC setup could be employed

Further for the square deployment scenarios (Figs 13(c)and (d)) and mMTC based scenarios (Figs 13(e) and (f)the trend for latency compliance follows that of the CABE(Fig 13(a)) and CMBE (Fig 13(b)) scenarios as discussedabove Hence through this analysis we reinforce fidelity of theAURA-5G framework towards the joint optimization problemthat we explore in this paper

E Convergence Time Distribution

Next we study the convergence time to the optimal solutionfor the AURA-5G framework This will be critical for real timeimplementation and hence is of immense value to not onlythe academic but also to the industrial community The reasonbeing network scenarios with the combination of constraintsdiscussed in this work will be prevalent in 5G networks And

given that there will be a central controller within a local areaof these networks [2] [3] [52] the newly designed mobilitymanagement algorithms such as the AURA-5G frameworkwill be placed on these controllers to enhance the QoE for theusers Consequently through Figs 14 and 15 we evaluatethe convergence time parameter for the various constraintcombinations imposed on the myriad scenarios explored inthis paper From the CDFs presented in Figs 14 and 15 aprobability measure of 1 indicates that all the 100 iterations(monte carlo trials) for the specific constraint combinationover the scenario under study have converged On the otherhand a probability measure of 0 indicates that none of theiterations converge Note that the simulations were performedon a commodity server with 20 cores (with each being an i9-7900x at 33GHz core) Ubuntu 1604 LTS OS and 64GB ofRAM

From Figs 14(a)-(d) and 15(a)-(b) we observe that for allscenarios and most constraint combinations the AURA-5Gframework is able to determine an optimal solution It isworth mentioning that the AURA-5G framework is able toprovision an optimal solution in an acceptable time framegiven the density and heterogeneity of 5G networks This isof extreme importance for real-time implementation becauseof the elevated level of dynamics in 5G networks as comparedto its predecessors

Next we observe that for the Single Association (SA)scenarios the time required for obtaining an optimal solution issignificantly less as compared to the Dual Connectivity (DC)mode scenarios This is so because the solution space foran SA scenario will be much smaller than that for a DCscenario hence the time required to search for the optimalsolution is correspondingly also reduced Next we observethat as constraints are imposed the amount of time requiredto search for the optimal solution increases This is inlinewith our intuition since addition of constraints adds extradimensions to the search space Most notably scenarios withthe Minimum Rate (MRT) constraints for both the SA andDC modes do not converge to an optimal solution in thegiven timeframe (we set a 600 seconds cutoff time) for all theMonte Carlo trials carried out This reflects the complexityintroduced by the MRT constraint and a possible requirementto re-dimension the network so as to be able to accomodatethe rate requirements for any given topology We refer thereader to Section VII for further details on the network re-dimensioning aspects

Further in Figs 14(a)-(d) and Figs 15(a)-(b) we alsohighlight an exception to the generic trend stated above ThePath latency (CPL) constraint when imposed on SA and DCleads to a faster search time as compared to their respective SAand DC counterparts in most scenarios This is due to the factthat while most constraint combinations in our work lead toan increasingly complex search space and hence an increasedconvergence time as corroborated by our results in Figs 14and 15 the addition of path latency constraint creates a cutin the solution hyperspace that reduces the overall complexityof the search space and consequently the convergence timeThis is also indicative of the fact that very few APs in thetopology are actually able to satisfy the path latency constraint

0 50 100 150 200 250Users

(in se

(a) CABE

0 50 100 150 200 250Users

(in se

(b) CMBE

0 50 100 150 200 250Users

(in se

(c) SABE

0 50 100 150 200 250Users

00040La

(d) SMBE

0 50 100 150 200 250Users

(in se

(e) CAIEm

0 50 100 150 200 250Users

(in se

(f) CMIEm

Fig 13 Observed Latency for (a) CABE (b) CMBE (c) SABE (d) SAIE (e) CAIEm and (f) CMIEm scenarios

200 400 600 800 1000 1200 1400 1600 1800Processing time (in seconds)

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(a) CABE

200 400 600 800 1000Processing time (in seconds)

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(b) CAIE

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(c) CMBE

100 150 200 250 300 350 400Processing time (in seconds)

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(d) CMIE

Fig 14 Convergence time CDF (Empirical) for (a) CABE (b) CAIE (c) CMBE and (d) CMIE scenarios

when imposed in combination with other network constraintsThus this gives an insight into the contrasting behavior ofdifferent constraints and their overall impact on the systemperformance

Lastly from Figs 15(c) and (d) wherein the mMTC servicesare considered as well it can be observed that most of theiterations for the scenarios in which the backhaul capacityis constrained do not converge to an optimal solution inthe stipulated time This is so because the mMTC servicesplace an additional burden on the backhaul by consuminga portion of their available capacity This as a result leadsto a more challenging scenario for the AURA-5G frameworkto determine an optimal solution as the eMBB services haveless amount of available backhaul capacity Consequently weobserve the non-convergent behavior of the scenarios with thebackhaul capacity constraint

F Solvability Analysis

In Section VIE we observed that certain scenarios withbackhaul capacity and minimum rate constraints do not con-verge to an optimal solution in the cutoff time period of 600seconds as defined in our evaluation framework However itmight very well be possible that either a solution does notexist or the optimizer got timed out ie it might or mightnot have a feasible solution but it was not able to determinethe same up until the 600 seconds timeframe Hence with thisbackground in this section we undertake a solvability analysiswith the specified time limit parameters and aim to understandthe bottleneck constraints for the AURA-5G framework giventhe various scenarios we have studied

For the solvability analysis we introduce Figs 16(a)-(f)wherein we have also provisioned an analysis for the mostcomplex combination of constraints ie CB + MRT CPL +MRT and CB + MRT + CPL (see Table V for description) Itmust be highlighted that the analysis provisioned here is for

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(a) SABE

200 400 600 800 1000 1200 1400 1600Processing time (in seconds)

SA+MRTDC+CB

DC+MRTDC+CPL

DC+CB+CPLSA+CPL

SA+CBSA+CB+CPL

(b) SAIE

(c) CAIEm (d) CMIEm

Fig 15 Convergence time CDF (Empirical) for (a) SABE (b) SAIE (c) CAIEm and (d) CMIEm scenarios

the case when we consider 275 eMBB users in the system iethe maximum number of users evaluated within our evaluationframework From Figs 16(a)-(f) we observe that for allthe scenarios explored the Minimum Rate (MRT) constraintbehaves as a bottleneck constraint for the optimizer in theAURA-5G framework This is also reflected from the timeconvergence plots in Figs 14 and 15 The reason being thatthere is limited access bandwidth available In addition giventhe nature of the scenario ie if its beamformed or interferencelimited the SINR characteristics transform and subsequentlyimpact the decision of the optimization framework Suchsystem based variability in SINR also limits the achievable peruser rate hence rendering the MRT constraint as a bottleneck

Further from Figs 16(a)-(f) we see that in the interfer-ence limited regime the optimizer performance is much moreseverely affected as compared to the beamformed scenariowhich is in line with the rest of our analysis so far Moreoverfor the square deployment scenario (Figs 16(e) and (f)) the

backhaul constraints are even more restrictive given the factthat the probability of the SC APs being more distant fromthe MC is higher Hence the probability of having a wiredbackhaul which has a 1 Gbps capacity and is in most casesmuch lower than what is offered by the mmWave wirelessbackhaul is also subsequently higher As a consequencefrom Figs 16(a) (c) and (e) it can be deduced that for thesquare deployment scenario with CB and MRT constraint thesystem performance is impacted severely with at least 10 moreiterations without a solution compared to those in the circulardeployment scenario

Next when we observe the scenarios wherein both themMTC and eMBB services have been considered (Figs 17(a)and (b)) it can be seen that the backhaul capacity constraintalso emerges as a bottleneck This is corroborated from thetime convergence curves in Figs 15(c) and (d) where thescenarios with the CB constraints do not illustrate convergencefor all the iterations This is because of the fact that the

SA DCSA

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0

Infeasible IterationsTimed out iterations

(a) CABE

SA DCSA

0 0 0 0 0 0 0 0

100 100 100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(b) CAIE

SA DCSA

0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(c) CMBE

SA DCSA

0 0 0 0 0 0 0 0

100 100 100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(d) CMIE

SA DCSA

0 0 0 0 0 0 0 0

99 98 98 97 99 99

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1

(e) SABE

SA DCSA

0 0 0 0 0 0 0 0

100 100 100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(f) SAIE

Fig 16 Optimizer Status for (a) CABE (b) CAIE (c) CMBE (d) CMIE (e) SABE and (f) SAIE scenarios with 275 eMBB users

SA DCSA

100 100 100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(a) CAIEm

SA DCSA

100 100 100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(b) CMIEm

Fig 17 Optimizer Status for (a) CAIEm and (b) CMIEm scenarios with 275 eMBB users

mMTC devices consume a portion of the available backhaulcapacity Consequently due to the reduced backhaul capacitythe optimizer finds itself in a situation wherein these verybackhaul resources transform into a bottleneck

Lastly from Figs 16(a)-(f) and 17(a)-(b) we deduce that asthe complexity of the constraint combinations increases theAURA-5G framework finds it increasingly challenging to de-termine the optimal solution In particular the MRT and CPLconstraints appear to be fairly challenging for our user associ-ation methodology Further as has already been stated abovefor the scenarios with mMTC services included constraintcombinations with CB also transform into being extremelychallenging ones to satisfy Consequently in the next sectionwe explore certain network re-dimensioning methodologiesthat assist the optimizer to determine an optimal association

VII NETWORK RE-DIMENSIONING

From the analysis presented in Sections VIE and VIFwe have observed that certain constraint combinations for thescenarios analyzed prove to be significantly difficult for theMILP framework to satisfy These insights can be a usefulnetwork designing tool for the operators and subsequentlythey can re-dimensionupgrade their networks to meet thedemands Hence in this section through Figs 18-29 wediscuss certain network re-dimensioning options and theircorresponding results We present the fact that re-evaluatingand re-defining appropriate network parameters results in animproved performance by the AURA-5G framework How-ever for our analysis and also the sake of brevity we consideronly SABEm and CABEm scenarios in this section as theyencompass all the complexities of the scenarios that we havestudied in this paper

And so the analysis presented thus far has led to theconclusion that one of the constraints that has proven to beextremely difficult to satisfy especially when mMTC andeMBB services are considered together in the system is

the backhaul capacity constraint Moreover scenarios whereinbeamforming and AnyDC modes have been utilized will proveto be particularly challenging given the lack of backhaulresources and the throughput maximization nature of theoptimizer In addition due to the lack of access resources aswell as the prevailing SINR characteristics the MRT constraintalso imposes a severe challenge for the AURA-5G frameworkHence through Figs 18-23 we analyzed scenarios with are-dimensioned backhaul and access network wherein bothmMTC and eMBB users are considered alongside the circularand square deployment and the beamformed and AnyDCregime

For the re-dimensioning we firstly calculated the averageamount of backhaul utilized in all the SCs when no re-dimensioning is done for the scenario under study Next weincrease the backhaul capacity of all the SCs in the system bya percentage of this average consumption For the percentageincrement we utilized four quantized levels ie 30 5080 and 100 Subsequently and to account for the worstcase scenario we increment the capacity of the backhaulfor each MC by 10 times the aforementioned average SCbackhaul utilization The factor of 10 arises from the factthat in our evaluation framework the maximum number ofsupported SCs by an MC is also 10 Next we re-dimensionthe access network by increasing the average number of SCsper MC in the topology from 6-7 (uniform distribution of 3to 10 SCs per MC) to 8 (uniform distribution of 6 to 10 SCsper MC) This automatically provisions more access networkresources in terms of bandwidth as well as increases thelikelihood for a user to find an SC in close proximity It isimportant to state here that we maintain the receive beamangle and beamforming gain Whilst these can be exploited toimprove the performance of the system further it might lead toincreased capitaloperating expenditure for the operator givenrequired infrastructure overhaul such as antenna replacementetc Hence we leave the discussion on this aspect for a sequel

SA DCSA

76 7670

95 94100 100 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(a) SABEm without Relaxed Backhaul

SA DCSA

65 66 6662

96 96 99 97 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

(b) SABEm with Relaxed Backhaul

Fig 18 Optimizer Status for (a) SABEm without Relaxed Backhaul and (b) SABEm with Relaxed Backhaul scenarios with 275 eMBB users

Fig 19 System Fairness Measure for (a) SABEm without Relaxed Backhaul and (b) SABEm with Relaxed Backhaul scenarios with 275 eMBB users

150 175 200 225 250 275Number of eMBB users

DC + CB DC + CB + CPL SA + CB SA + CB + CPL

Fig 20 Total Network Throughput for (a) SABEm without Relaxed Backhaul and (b) SABEm with Relaxed Backhaul scenarios with 275 eMBB users

SA DCSA

100 100

0 0 0 0 0 0 0 0 0 0 0 0 05

(a) SABEm with Relaxed BH and Increased SC density

SA DCSA

31 29 31 29

89 88 89 88

0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2

(b) SABEm with Relaxed BH Increased SC density and 5mslatency requirement

Fig 21 Optimizer Status for (a) SABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) SABEm scenario withRelaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

Fig 22 System Fairness Measure for (a) SABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) SABEm scenariowith Relaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

Consequently from the analytical results in Figs 18(a)-(b)and 21(a) we observe that when the backhaul capacities areenhanced by the methodology explained above the scenarioswhere backhaul was a constraint ceases to be so anymore Forexample in Fig 18(a) constraint combinations only CB andCB + CPL highlight the fact that the backhaul capacity is aconstraint for the scenario under study ie SABEm Henceby the re-dimensioning employed as specified in our workthrough Fig 18(b) we observe that the number of iterationsthat converge for the only CB and CB + CPL constraintcombinations increases by 1447 Furthermore when weemploy the increased SC density framework to provision moreaccess network resources the percentage improvements in the

number of converged iterations as can be seen in Fig 21(a)for constraint combinations CB CB + CPL MRT and CB +MRT are at 302 30 55 and 11 respectively As aresult to a great extent the re-dimensioning performed accord-ing to the guidelines specified above helps in alleviating thebottlenecks that hampered the AURA-5G framework earlier

In addition to the solvability analysis discussed above wesee that the re-dimensioning efforts result in an increase inthe system fairness This is more prominent for constraintcombinations CB and CB + CPL as understood from Figs19(a) and (b) while from Fig 22(a) we deduce that a re-dimensioned access network topology leads to an across theboard positive effect on the system fairness The positiveeffects of network re-dimensioning are also prevalent in the

Fig 23 Total Network Throughput for (a) SABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) SABEm scenariowith Relaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

SA DCSA

69 69 6658

100 100 99 97 100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0

(a) CABEm without Relaxed Backhaul

SA DCSA

99 9992

100 100

0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

(b) CABEm with Relaxed Backhaul

Fig 24 Optimizer Status for (a) CABEm without Relaxed Backhaul and (b) CABEm with Relaxed Backhaul scenarios with 275 eMBB users

Fig 25 System Fairness Measure for (a) CABEm without Relaxed Backhaul and (b) CABEm with Relaxed Backhaul scenarios with 275 eMBB users

network throughput plots in Figs 20(a)-(b) and 23(a) Fromthese results we observe that the network re-dimensioningenables approximately 35 and 40 increase in the totalnetwork throughput for the CB and CB + CPL constraintcombinations respectively

However as can be seen from Fig 21(a) path latency stillremains a bottleneck constraint in scenarios where MRT+CPLand CB+MRT+CPL constraint combinations are imposedMoreover when path latency is imposed as the only constraintour optimizer converges to a solution in each of the 100Monte Carlo trials Hence in a network wherein there canbe multiple constraint combinations such as MRT+CPL andMRT+CPL+CB the operators should be careful when theysign the service level agreements (SLAs) These SLAs shouldnot be overly restrictive such as the one we have here wherethe 3ms downlink latency cannot be guaranteed in most topolo-gies As a consequence we present our observations for thecase when this downlink latency requirement is relaxed to 5msImmediately through Fig 21(b) we observe that the optimizeris able to determine an optimal association in 683 moreiterations for the CB+MRT constraint scenario and in 11more iterations for the CB+MRT+CPL Further the fairnessand the total network throughput in the presence of MRT+CPLand MRT+CPL+CB constraints are also improved as seenthrough Figs 22(b) and 23(b) In addition to the relaxationin the SLAs edge clouds through appropriate placement[53] [54] can also provision great improvements in systemperformance This is so because they bring the services closerto the users which reduces the total round trip time and hencethe downlink delay as well

Also from Figs 24-29 wherein the circular deploymentis considered we observe a similar trend in results as that inFigs 18-23 Concretely from Figs 24(a) and (b) we notice thatthe number of iterations that converge to an optimal solutionfor the CB and CB+CPL constraint combinations increases by159 For the system fairness from Figs 25(a) and (b) itcan be observed that for scenarios with CB and CB + CPLthe fairness is also improved The reason being the improvedbackhaul capacity allows the AURA-5G framework to assignresources more equitably to the users in the system Addi-tionally as seen from Figs 26(a) and (b) the improvement insystem throughput is nearly 714 and 60 for the CB andCB+CPL scenarios respectively Furthermore from Figs 27-29 it can be deduced that the increase in the average numberof SCs per MC from 6 to 8 as well as having less stricterlatency requirements results in resolving to a great extentthe bottleneck nature of the MRT and path latency constraintalongside increasing the system fairness and the total networkthroughput

Thus the above observations highlight an important aspectof the AURA-5G framework wherein the operators can testtheir network topology and infer its performance as well as thebottlenecks And although we randomly increased the variousnetwork settings the operators through the use of the AURA-5G framework are empowered with the ability to re-dimensiontheir networks according to the inferences they make and inlinewith their ultimate objectives

VIII CONCLUSION

In this paper we have provided a first holistic study inliterature with regards to the joint optimization based userassociation framework for multiple applications in 5G net-works The study entails utilizing a MILP formulation basedjoint optimization process which takes into account the serviceclasses and accordingly assigns the bandwidth and AP tothe users in the system Further we organized the entireprocess into a software suite wherein the location generationsystem model specifics optimization and data analytics areperformed We refer to this softwarized framework as AURA-5G and present it to the academic and industrial communityfor analyzing the user association and resource allocationbehavior in conjunction with each other

Next for the optimizer we developed a MILP frameworkwherein the objective is to maximize the overall networkthroughput and find the appropriate user-AP-bandwidth as-sociation that achieves the same We established the variousconstraints that help us perform the multi-dimensional studycarried out in this paper Subsequently we also establishcertain methodologies to resolve the non-linearities introducedby multiplication of binary decision variables This assists inreducing the complexity of our optimization problem Furtherwe also present a novel discussion on the complexity ofSINR calculation and how our computation method whilebeing a lower bound and sub-optimal assists in reducing thecomplexity of the AURA-5G framework

In addition as part of this study we presented the per-formance of the AURA-5G framework for dual connectivity(DC) and single connectivity (SA) modes For the performanceevaluation process we utilized realistic network scenariosand parameters so as to be able to ensure the efficacy ofthe AURA-5G framework Consequently we showed that theestablished framework outperforms the baseline scenario interms of the total network throughput Further we presentedthe performance characteristics of the AURA-5G frameworkfor the DC and SA modes alongside the multiple constraintcombinations in terms of system fairness backhaul utilizationlatency compliance convergence time distribution and solv-ability Note that for DC modes we present a novel analysisfor scenarios where the choice of SC does not have to be geo-restricted by the choice of MC and where the user has theopportunity to connect to any two APs ie SC-SC SC-MCor MC-MC

We now summarize some of the important findings fromour detailed analysis as follows

1 For the total network throughput metric (Figs 3 4 6 and7) scenarios wherein circular deployment beamformedonly eMBB services and AnyDC setup was consideredshowed significant performance gains for dual connectiv-ity as compared to single association However with theMCSC setup the gains were not as significant Furtherthe path latency (CPL) and backhaul (CB) constraintsseverely impact the overall network throughput for allscenarios In addition for the scenarios with the inter-ference limited regime one can immediately observe asignificant reduction in the overall network throughput

Fig 26 Total Network Throughput for (a) CABEm without Relaxed Backhaul and (b) CABEm with Relaxed Backhaul scenarios with 275 eMBB users

SA DCSA

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(a) CABEm with Relaxed BH and Increased SC density

SA DCSA

29 26 29 26

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

(b) CABEm with Relaxed BH Increased SC density and 5mslatency requirement

Fig 27 Optimizer Status for (a) CABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) CABEm scenario withRelaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

due to the degradation in SINR Moreover for the squaredeployment scenarios a gain of nearly 6 in the totalnetwork throughput is observed as compared to thecircular deployment scenarios Finally for the scenarioswith both eMBB and mMTC services since we considerthe mMTC services to operate in the guard band andconsume only the backhaul resources a correspondingreduction in the overall throughput for the eMBB servicesin the presence of CB constraint was observed

2 For the system fairness metric (Figs 8-10) scenar-ios wherein only eMBB services circular deploymentAnyDC setup and beamforming are considered showedthat single association achieved higher fairness than dualconnectivity The Minimum Rate (MRT) constraint how-ever assisted in a slight improvement of system fairnessgiven the DC setup Moreover the CB and CPL con-straints resulted in a siginificant lowering of the overall

system fairness Furthermore for MCSC setup an overallimprovement in system fairness for the DC setup wasobserved However for the scenarios where an interfer-ence limited regime was considered a significant drop insystem fairness was noticed given the SINR degradationand the greedy nature of the objective function (SectionIII equation 14) Next the square deployment scenariosshowed an overall improvement of 5-6 in system fair-ness as compared to the circular deployment scenariosLastly we analyzed the scenarios wherein both eMBBand mMTC services co-exist For these scenarios weobserved that fairness measure is not affected significantlyas compared to that noticed for only eMBB scenarios

3 For the backhaul utilization metric (Figs 11 and 12)we discern that the AURA-5G framework works ex-ceptionally well in being able to adhere to the strictbackhaul capacity constraints imposed by the network

Fig 28 System Fairness Measure for (a) CABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) CABEm scenariowith Relaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

Fig 29 Total Network Throughput for (a) CABEm with Relaxed Backhaul and Increased SC density scenario with 275 eMBB users and (b) CABEmscenario with Relaxed Backhaul Increased SC density 5 ms downlink latency requirement and 275 eMBB users

Further for the scenarios with beamforming we observethat the backhaul capacity is almost completely utilized ascompared to that in the scenarios with interference limitedregime Additionally for scenarios wherein both eMBBand mMTC devices are considered it was observed thatthe overall backhaul utilization by the eMBB devices islower than that in scenarios where only eMBB devicesexist

4 For the latency compliance metric (Fig 13) we observedagain that the AURA-5G framework is able to determineuser-AP-bandwidth associations such that the latencyconstraints are satisfied It was observed that while inAnyDC setups the users accessed SCs more than MCs forthe MCSC setups a higher density of users was observedto have access to MCs and thus a reduced latency

5 Through our novel convergence time distribution andsolvability analysis it was observed that certain constraintcombinations are very restrictive and hence the network

requires re-dimensioning It is imperative to state thatsuch insights will be significantly important for the oper-ators in network planning

6 We presented in Section VII an analysis of certain chal-lenging scenarios wherein the network re-dimensioningwas carried out on both the access and backhaul net-work We showed that a simple re-dimensioning processwherein the SC density was increased from 6 to 8 SCs onaverage per MC the backhaul capacity was increased andless restrictive SLAs were agreed to resulted in signifi-cant improvement in system performance thus alleviatingthe bottleneck constraints concern

Lastly as part of our future work we will be analyzingscenarios wherein the URLLC services will be considered andserviceability of requests through edge servers will also betaken into account

REFERENCES

[1] I F Akyildiz et al ldquo5G roadmap 10 key enabling technologiesrdquoComputer Networks vol 106 pp 17ndash48 2016

[2] A Jain E Lopez-Aguilera and I Demirkol ldquoMobility Managementas a Service for 5G Networksrdquo in IEEE ISWCS 2017 Workshop aug2017 pp 1ndash6 [Online] Available httparxivorgabs170509101

[3] A Jain E Lopez-Aguilera and I Demirkol ldquoAre Mobility ManagementSolutions Ready for 5G and Beyondrdquo Submitted to Elsevier ComputerCommunications journal 2019

[4] Ericsson ldquoEricsson Mobility Reportrdquo Tech Rep November 2019[5] 3GPP ldquoTS22261 Service requirements for the 5G system (Stage 1)rdquo

2018[6] 5GPPP Initiative ldquo5G PPP use cases and perfor-

mance evaluation modelsrdquo Tech Rep 2016 [On-line] Available https5g-pppeuwp-contentuploads2014025G-PPP-use-cases-and-performance-evaluation-modeling_v10pdf

[7] Y Shi H Qu and J Zhao ldquoDual Connectivity Enabled User Asso-ciation Approach for Max-Throughput in the Downlink HeterogeneousNetworkrdquo Wireless Personal Communications vol 96 no 1 pp 529ndash542 2017

[8] N Prasad and S Rangarajan ldquoExploiting Dual Connectivity inHeterogeneous Cellular Networksrdquo [Online] Available httpsarXiv170908791v1

[9] ETSI and 3GPP ldquoETSI TS 137 340 v1550rdquo Tech Rep 2019[10] A Nayak P Jha A Karandikar and P Chaporkar ldquoEnhanced UE Slice

Mobility for 5G Multi-RAT Networksrdquo Mobislice II Workshop - IEEENFV SDN 2019 2019

[11] Gurobi LLC ldquoMixed Integer Programming Basicsrdquo [Online] Availablehttpswwwgurobicomresourcemip-basics

[12] S Kan H Chen Y Zhu and W Li ldquoAggregation based Cell SelectionMethods for multi-RAT HetNetrdquo pp 1ndash5 Oct 2016

[13] M Alkhawlani and A Ayesh ldquoAccess Network Selection based onFuzzy Logic and Genetic Algorithmsrdquo Advanced Artificial Intelligence(AAI) vol 1 no 1 pp 1ndash12 2008

[14] K Radhika and A V G Reddy ldquoNetwork Selection in HeterogeneousWireless Networks Based on Fuzzy Multiple Criteria Decision Mak-ingrdquo 3rd International Conference onElectronics Computer Technology(ICECT) pp 136ndash139 2011

[15] M Mansouri and C Leghris ldquoA comparison between fuzzy TOPSISand fuzzy GRA for the vertical handover decision makingrdquo in 2017Intelligent Systems and Computer Vision (ISCV) Apr 2017 pp 1ndash6

[16] M Mansouri and C Leghris ldquoA battery level aware MADM combina-tion for the vertical handover decision makingrdquo 2017 13th InternationalWireless Communications and Mobile Computing Conference IWCMC2017 pp 1448ndash1452 2017

[17] A Bazrafkan and M R Pakravan ldquoAn MADM network selectionapproach for next generation heterogeneous networksrdquo pp 1884ndash1890May 2017

[18] M Mansouri and C Leghris ldquoThe use of MADM methods in thevertical handover decision making contextrdquo International Conferenceon Wireless Networks and Mobile Communications WINCOM 2017

[19] S Radouche C Leghris and A Adib ldquoMADM methods based on utilityfunction and reputation for access network selection in a multi-accessmobile network environmentrdquo pp 1ndash6 Nov 2017

[20] S Liang et al ldquoMulti-Attribute Vertical Handover Decision-MakingAlgorithm in a Hybrid VLC-Femto Systemrdquo IEEE CommunicationsLetters vol 21 no 7 pp 1521ndash1524 2017

[21] L Tie and P G Bai ldquoA New Multi-Attribute Decision-Making VerticalHandover Algorithmrdquo IET Conference on Wireless Mobile and SensorNetworks (CCWMSN07) Dec 2017

[22] Y Liu et al ldquoJoint User Association and Spectrum Allocation for SmallCell Networks With Wireless Backhaulsrdquo IEEE Wireless Communica-tions Letters vol 5 no 5 pp 496ndash499 2016

[23] C Kim R Ford and S Rangan ldquoJoint interference and user associationoptimization in cellular wireless networksrdquo Asilomar Conference onSignals Systems and Computers pp 511ndash515 Apr 2015

[24] Z Su B Ai D He G Ma K Guan N Wang and D Zhang ldquoUserassociation and backhaul bandwidth allocation for 5G heterogeneousnetworks in the millimeter-wave bandrdquo 2017 IEEECIC InternationalConference on Communications in China ICCC 2017 vol 2018-Januano Iccc pp 1ndash6 2018

[25] F H Khan and M Portmann ldquoJoint QoS-control and handover optimiza-tion in backhaul aware SDN-based LTE networksrdquo Wireless Networksvol 7 2019

[26] H Kim et al ldquoDistributed-optimal user association and cell load bal-ancing in wireless networksrdquo IEEEACM Transactions on Networkingvol 20 no 1 pp 177ndash190 2012

[27] S Kapoor D Grace and T Clarke ldquoA base station selection schemefor handover in a mobility-aware ultra-dense small cell urban vehicularenvironmentrdquo IEEE International Symposium on Personal Indoor andMobile Radio Communications PIMRC pp 1ndash5 October 2018

[28] M Q Khan and S H Andresen ldquoA semi and fully distributed handoveralgorithm for heterogeneous networks using MIISrdquo IEEE Symposium onComputers and Communications pp 145ndash150 2012

[29] Y Liu M Derakhshani and S Lambotharan ldquoDual Connectivityin Backhaul-Limited Massive-MIMO HetNets User Association andPower Allocationrdquo IEEE Global Communications Conference pp 1ndash6 Dec 2017

[30] Y Shi et al ldquoDownlink Dual Connectivity Approach in mmWave-AidedHetNets With Minimum Rate Requirementsrdquo IEEE CommunicationsLetters vol 22 no 7 pp 1470ndash1473 2018

[31] G Pocovi et al ldquoAnalysis of Heterogeneous Networks with DualConnectivity in a Realistic Urban Deploymentrdquo pp 1ndash5 May 2015

[32] A Sutton et al ldquoWireless Backhaul Performance Modeling and Impacton User Association for 5Grdquo IEEE Transactions on Wireless Commu-nications vol 17 no 5 pp 3095ndash3110 2018

[33] S Bayat et al ldquoDistributed user association and femtocell allocation inheterogeneous wireless networksrdquo IEEE Transactions on Communica-tions vol 62 no 8 pp 3027ndash3043 2014

[34] A Mesodiakaki et al ldquoEnergy and spectrum efficient user associationin 5G heterogeneous networksrdquo IEEE International Symposium onPersonal Indoor and Mobile Radio Communications PIMRC pp 1ndash6 2016

[35] ITU ldquoIMT Vision ndash Framework and overall objectives of the futuredevelopment of IMT for 2020 and beyond M Series RecommendationITU-R M2083-0 (092015)rdquo 2015 [Online] Available httpswwwituintdms_pubrecitu-rrecmR-REC-M2083-0-201509-IPDF-Epdf

[36] V Bantildeos-Gonzalez et al ldquoIEEE 80211ah A technology to face theIoT challengerdquo Sensors (Switzerland) vol 16 no 11 2016

[37] H Shokri-Ghadikolaei et al ldquoMillimeter wave cellular networksA MAC layer perspectiverdquo IEEE Transactions on Communicationsvol 63 no 10 pp 3437ndash3458 2015

[38] H Shokri-Ghadikolaei C Fischione and E Modiano ldquoOn the accuracyof interference models in wireless communicationsrdquo 2016 IEEE Inter-national Conference on Communications ICC 2016 pp 1ndash6 2016

[39] H Shokri-Ghadikolaei and C Fischione ldquoThe Transitional Behavior ofInterference in Millimeter Wave Networks and Its Impact on MediumAccess Controlrdquo IEEE Transactions on Communications vol 64 no 2pp 723ndash740 2016

[40] ETSI and 3GPP ldquoETSI TS 138 300 v1531rdquo Tech Rep 2018 [Online]Available httpsportaletsiorgTBETSIDeliverableStatusaspx

[41] S Ryoo J Jung and R Ahn ldquoEnergy efficiency enhancement with RRCconnection control for 5G new RATrdquo IEEE Wireless Communicationsand Networking Conference WCNC pp 1ndash6 Apr 2018

[42] M Jaber et al ldquo5G Backhaul Challenges and Emerging ResearchDirections A Surveyrdquo IEEE Access vol 4 pp 1743ndash1766 2016

[43] S Sun et al ldquoInvestigation of Prediction Accuracy Sensitivity andParameter Stability of Large-Scale Propagation Path Loss Models for5G Wireless Communicationsrdquo IEEE Transactions on Vehicular Tech-nology vol 65 no 5 pp 2843ndash2860 May 2016

[44] M Polese et al ldquoImproved handover through dual connectivity in5g mmwave mobile networksrdquo IEEE Journal on Selected Areas inCommunications vol 35 pp 2069ndash2084 2016

[45] T S Rappaport et al ldquoOverview of Millimeter Wave Communicationsfor Fifth-Generation (5G) Wireless Networks-With a Focus on Propaga-tion Modelsrdquo IEEE Transactions on Antennas and Propagation vol 65no 12 pp 6213ndash6230 2017

[46] Z Savic ldquoLTE Design and Deployment Strategiesrdquo Cisco pp 1ndash792011

[47] Keysight ldquoUnderstanding the 5G NR Physical Layerrdquo Tech Rep 2017[Online] Available httpswwwkeysightcomuploadcmc_uploadAllUnderstanding_the_5G_NR_Physical_Layerpdf

[48] S Sun et al ldquoPropagation Models and Performance Evaluation for 5GMillimeter-Wave Bandsrdquo IEEE Transactions on Vehicular Technologyvol 67 no 9 pp 8422ndash8439 2018

[49] 3GPP ldquoTR 138 901 - V1400 - 5G Study on channel modelfor frequencies from 05 to 100 GHz (3GPP TR 38901 version1400 Release 14)rdquo Tech Rep 2017 [Online] Available httpwwwetsiorgstandards-search

[50] T Report ldquoTR 138 901 - V1500 - 5G Study on channel model forfrequencies from 05 to 100 GHz (3GPP TR 38901 version 1500Release 15)rdquo vol 0 2018

[51] R Jain D Chiu and W Hawe ldquoA Quantitative Measure Of FairnessAnd Discrimination For Resource Allocation In Shared ComputerSystemsrdquo 1998 [Online] Available httparxivorgabscs9809099

[52] A Jain E Lopez-Aguilera and I Demirkol ldquoEvolutionary 4G5GNetwork Architecture Assisted Efficient Handover Signalingrdquo IEEEAccess vol 7 pp 256ndash283 2019 [Online] Available httpsieeexploreieeeorgdocument8565842

[53] A Santoyo-Gonzaacutelez and C Cervelloacute-Pastor ldquoLatency-aware cost op-timization of the service infrastructure placement in 5G networksrdquoJournal of Network and Computer Applications vol 114 pp 29ndash372018

[54] I Leyva-Pupo A Santoyo-Gonzaacutelez and C Cervelloacute-Pastor ldquoA frame-work for the joint placement of edge service infrastructure and user planefunctions for 5Grdquo Sensors (Switzerland) vol 19 2019

Introduction

State of the Art

The Optimization Framework Mathematical Formulation and Solver Implementation

Linearization

Solver Implementation Challenges

Scenarios Evaluated

Deployment Strategies

Service Classes

Directivity Regimes

Dual Connectivity Modes

Baseline and Single Association

Constraint Based Scenarios

Evaluation Framework

Results and Discussions

Total Network Throughput

eMBB services based scenarios

mMTC with eMBB services based scenarios

System Fairness

eMBB service based scenarios

mMTC with eMBB based scenarios

Backhaul Utilization

Latency Requirement Compliance

Convergence Time Distribution

Solvability Analysis

Network Re-dimensioning

Conclusion

References