planning wireless cellular networks of future: outlook ...1 planning wireless cellular networks of...

2169-3536 (c) 2016 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2680318, IEEE Access

1

Planning Wireless Cellular Networks of Future:Outlook, Challenges and OpportunitiesAzar Taufique1, Mona Jaber2, Ali Imran1, Zaher Dawy3, Elias Yacoub4

1 Department of Electrical and Computer Engineering, The University of Oklahoma, TulsaEmail: azar.taufique, [email protected]

25G Innovations Center, University of Surrey, UKEmail: [email protected]

3Department of Electrical and Computer Engineering, American University of Beirut (AUB), Beirut, Lebanon.E-mail: [email protected].

4 Strategic Decisions Group (SDG), Beirut, LebanonE-mail:[email protected]

Abstract—Cell planning (CP) is the most important phase inthe life cycle of a cellular system as it determines the operationalexpenditure (OPEX), capital expenditure (CAPEX), as well asthe long-term performance of the system. Therefore, it is notsurprising that CP problems have been studied extensively forthe past three decades for all four generations of cellular systems.However, the fact that small cells, a major component of futurenetworks, are anticipated to be deployed in an impromptu fashionmakes CP for future networks vis-a-vis 5G a conundrum. Fur-thermore, in emerging cellular systems that incorporate a varietyof different cell sizes and types heterogeneous networks (HetNets),energy efficiency, self-organizing network (SON) features, controland data plane split architectures (CDSA), massive multiple inputmultiple out (MIMO), coordinated multipoint (CoMP), cloudradio access network (C-RAN) and millimeter wave (mmWave)based cells plus the need to support internet of things (IoT) anddevice-to-device (D2D) communication require a major paradigmshift in the way cellular networks have been planned in thepast. The objective of this paper is to characterize this paradigmshift by concisely reviewing past developments, analyzing state-of-the-art challenges and identifying future trends, challenges,and opportunities in CP in the wake of 5G. More specifically, inthis paper we investigate the problem of planning future cellularnetworks in detail. To this end, we first provide a brief tutorialon the CP process to identify the peculiarities that make CP oneof the most challenging problems in wireless communications.This tutorial is followed by a concise recap of past research inCP. We then review key findings from recent studies that haveattempted to address the aforementioned challenges in planningemerging networks. Finally, we discuss the range of technicalfactors that need to be taken into account while planning futurenetworks and the promising research directions that necessitatesthe paradigm shift to do so.

Index Terms—HetNets planning, Energy efficient planning, 5Gnetwork planning

I. INTRODUCTION

Research in cellular planning (CP) is older than the cellularsystem itself [1], [2], [3]. However, the first generation ofcellular systems were planned almost manually as the focuswas on providing coverage to serve the elite of society only.The gigantic subscription fees, low traffic loads, lack ofcompetition and relative abundance of spectrum at that time

meant not much effort had to be invested to optimize thenetwork plan. As the trend moved toward ubiquity of cellularservice, the foremost optimization objective that emerged wasto maximize the coverage while keeping the number of basestations at a minimum [4], [5], [6]. This prompted the firstcall for CP optimization techniques to be investigated andraised the need for automated computer-aided CP tools [7],[8] thereby triggering the academic and industrial research inthis area that has grown continuously thereafter. We can referto this initial era of CP research as classic CP that roughlyspanned over the decade of the 90s. Research in this classicCP era can be broadly described as being mainly focusedon optimizing the location and number of base stations (BS)while largely abstracting the parameter optimization of thebase station themselves.Introduction of data services and, consequently, crowded

networks at the beginning of the new millennium meantthat operators had to tweak and optimize a large number ofBS parameters in the planning process to squeeze out allpossible bits of capacity [9], [10]. This strategy shifted thefocus of CP research from classic and relatively primitiveto a more advanced planning [11] approach that we canrefer to as holistic CP. In holistic CP, in addition to BSlocations, BS parameters such as number of sectors, azimuths,tilts, transmission powers, pilot powers etc. were consideredwhile formulating and optimizing the CP problem [12]. Thisera of holistic CP can be roughly mapped to the first twoto three quarters of the last decade. Gould [13] describedsome challenges that CP engineers faced when doing holisticCP. While holistic CP solutions with reasonable computationcomplexity were still being sought [14], [15], [16], the adventof LTE and LTE-advanced (LTE-A) at the beginning of currentdecade again called for a major revamp of the CP paradigm.Unprecedented demand for higher data rates combined withprojected proliferation of internet of things (IoT) mean newtechnologies such as massive MIMO, smart femto cells [17],[18], fractional frequency reuse [19], CoMP, C-RAN, andmmWave had to be resorted to in emerging networks. Whileadaptation of these technologies in 5G offers promising av-



2

enues for raising cellular system capacity, it put forth a wholenew set of challenges to the CP research community. In addi-tion, in the wake of the rising cost of energy and environmentalawareness, energy efficiency became a newly added constraintto the CP problem that asks for a significantly different, ifnot totally new, approach toward CP [20]. Furthermore, inthe emerging socio-economic structure, the average revenueper bit earned by the operators is diminishing. This trendis pushing operators to rely on self organization network(SON) features to minimize OPEX and CAPEX. While SONis a promising paradigm that improves capacity, and reducestotal cost of ownership (TCO) for the network operators [21].However, it remains unclear how the two paradigms CP andSON, CP being too old and SON being too young, will fittogether in emerging networks such as 5G.Regardless of physical layer waveform and spectrum band

adapted for 5G cellular systems, it is clear that the majorityof the 1000x target capacity gain must come from networkdensification. Realizing the massive potential of network den-sification by small cells, industry pundits have been forecastingan explosive growth of small cells for the past few years.However, to date, mass deployments of small cells remainelusive mainly due to the fact that the ultra-dense deploymentof small cells comes with its own set of peculiar planningchallenges. The key challenge being how to plan and roll out aheterogenous network that will contain unplanned deploymentof small cells.In the backdrop of these recent developments, this paper

aims to analyse the state of the art in CP and identify thechallenges and opportunities therein in context of emergingcellular networks such as 5G. Though this article provides aconcise review of selected literature on CP, its main objectiveis not to provide comprehensive survey of literature. Insteadthis article has following goals:1) Provide a brief tutorial on CP process to highlight theconflicting objectives and constraints that make CP oneof the most challenging problems in wireless communi-cations. (Section II)

2) Provide concise and tabular recap of literature to guidethe reader to sources that have addressed different partsof CP to date. (Section III)

3) Identify recent trends in CP such as energy-focusedplanning, planning for traffic uncertainties and withCoMP in mind. (Section IV)

4) Provide an overview of the models and techniques thathave emerged recently to improve CP such as models forcell load, interference, BS location randomness, channelvariation, and total cost of ownership (TCO). (SectionV)

5) Identify the technical factors that make planning a het-erogenous network (HetNet) different from macro-cell-only networks and how these factors can be accountedfor in a new HetNet planning paradigm. (Section VI).

6) Finally, identify the prospects, challenges, and oppor-tunities that lie in the CP paradigm in wake of 5Gand beyond. (Section VII). This section answers thequestions: what, why and how the CP paradigm willchange with the advent of C-RAN, M2M, D2D, control

and CDSA, Massive MIMO, and mmWave in 5G andbeyond.Fig. 1 shows the contents, contribution, and layout ofthis papers details

II. A BRIEF TUTORIAL ON PLANNING PROCESS

The cell planning process consists of three phases: pre- plan-ning, or dimensioning; detailed planning; and post planning, oroptimization, as shown in Fig. 2. The output of the dimension-ing phase is an approximate number of BSs required to coveran area of interest. For a recent tutorial on the dimensioningprocess, readers can refer to [22]. The detailed planning phaseallows determining the actual positions of the BSs within thearea to be served. In the optimization phase, which occurs afterthe network has been deployed and is running, the networkperformance is analyzed, potential problems detected, andimprovements made to enhance network operation [23]. Thispaper focuses on the detailed planning phase. In this section,we briefly review the CP process, including its objectives,input and output parameters to the optimization process andto the CP phases. We also give a simple analysis of thecomplexity of the CP problem. This section aims to provide thenecessary background needed for the discussion in successivesections.

A. Cell Planning ObjectivesThe objectives of CP heavily depend on the business strat-

egy of the operators. The coverage target for different services,the pricing and throughput policies, regulatory constraints,market share goals and competition are some factors amongmany that define the CP objectives. Ultimately, CP objectivescan be boiled down to the following set of optimization targetsidentified in the cell planning problem:1) Minimize TCO. In addition to minimizing the overallnetwork cost, this objective may also include minimizingeconomic costs related to deployment costs and param-eter optimization.

2) Maximize capacity. For a single service, this objectivecan be defined as the number of users who can be servedat one time. In the case of multi-service traffic, capacitycan be approximated in terms of global throughput.

3) Maximize coverage. This includes satisfying coveragepolicy requirements for various services. Up Link (UL)and Down Link (DL) coverage must be balanced. Bothtraffic channels and coverage of common channels mustbe considered.

4) Minimize Power Consumption. Health concerns havemotivated the radiated power minimization objective.However, recent awakening of a desire for greenerwireless systems has added more depth to this objec-tive. Consequently, power consumption, including fixedcircuit power as well as variable transmission power,must be minimized.

5) Optimise handover (HO) zones. In a well-plannedcellular system, a certain proportion of the area of eachcell should overlap with neighboring cells to satisfy HO



3

Fig. 1. Layout of the contents and paper contribution: The flow chart explains challenges , opportunities , dependencies and factors in planning future cellularnetworks

conditions. HO zones are essential to guarantee continu-ity of service between the sectors. It also strengthens theradio link against fast fading and shadowing. However,too much overlap may result in wastage of power, andradio resources, and increase in interference and electro-smog, making it a tricky planning objective.

B. Conflicting Nature of Multiple CP ObjectivesIronically, the CP objectives listed in Section II-A mutually

conflict, hence, giving rise to the immense research on CP inpast two decades. For example, maximizing the coverage and

capacity requires deploying more base stations, which in turn,increases the network cost. Similarly, coverage maximizationcontradicts the objective of reducing power consumption andelectro-smog. Regardless of which technique is adopted tosolve the CP problem, competing multiple objectives need tobe addressed, although this is often done implicitly rather thanexplicitly. The main implication of having multiple objectivesis that a set of optimal solutions, rather than a single solution,is obtained. Although several alternatives to cope with multi-objectivity have been proposed in the literature, no approachproves more prevalent [24].



4

Fig. 2. Three phases of cellular network planning and optimization.

To cope with more than one CP objective, multi-objectivefunctions are often defined. Previous CP studies used twodifferent ways to represent a multi-objective function. One wayis to use a linear combination of different objective criteria toform a single objective function, where different objectives aregiven a certain weight between 0 and 1 [25], [26], [27], [28],[29].In the second method, the problem is formulated by a

set of decision variables i.e., parameter space vector and aset of objective vectors. When there is no solution that canimprove one objective without degrading the other objective, itis considered as the optimal solution. This solution is referredto the Pareto optimal solution [30], [31], [29]. The objectivefunctions of interest can also be assigned weights to reflecttheir importance relative to each other. Such weighted multi-objective functions give more flexibility to the network plannerby assigning higher or lower weight to put more or lessemphasis on a given objective.

C. Cell Planning Inputs

Different inputs are required to solve the cell planningdifficulty depending on objectives in focus and phase ofplanning. Usually, the following inputs need to be known [32]:1) Traffic Models: User traffic distribution is a main factorthat ultimately determines the cellular system plan and,hence, is a key input in the CP process. In GSM (mono-service systems), for instance, geographical characteri-sation of traffic distribution is sufficient. However, withmulti-service systems supporting data, traffic character-isation based on types and level of service is needed[33]. Test point based traffic models are often used forCP traffic modeling, for the sake of practicality [34],[35], [36], [6]. In this model, an area is characterizedover a time interval and all located mobile terminals arebundled into a single test point. This point representsthe cumulative traffic, or traffic intensity, from all theseterminals, over the determined interval.

2) Potential Site Locations: Theoretically, a base stationcan be installed anywhere. However in the real world,a set of candidate sites is first pre-determined and usedas input to the CP, to incorporate the real estate con-straints. The objective, thus, is to find the optimumsubset of BS locations. These potential BS locations aredetermined by taking into account the constraints suchas, socio-economic feasibility and availability of site(s),traffic density, building heights, terrain height(s) and pre-existence of a site(s) by the same or other operators.

3) BS Model: There are many parameters that define theBS model such as: antenna type and height, receiversensitivity, load capacity, transmit power and capitaland operational costs [37] . Moreover, heterogeneousnetworks necessitate modeling of new types of nodes;for instance relay stations (RS), pico-cells, femto-cells,and small cells.

4) Propagation Prediction Models/Maps: A key input tothe planning process is the signal propagation model.The potential of this model is to incorporate reflection,differentiation, absorption, and propagation of the signalin real environment. Taking into account the natural andman-made structures, vegetation and topography of anarea, highly determines the accuracy of the CP out-comes [38]. Very sophisticated planning tools rely on ac-tual measurement based propagation maps, or ray tracingbased complex analysis, to predict the propagation [39].However, obtaining complete propagation maps of alarge area using these methods is a very cumbersome,time consuming , and expensive process. For this reason,different empirical models have been proposed in theliterature. Such models abstract the experimental andstatistical data in the form of deterministic expressions,that can easily be used in the CP. Okumura [40],Hata [41], and COST 231 [42] are a few examplesof such well known propagation models used in CPto depict propagation loss in different environmentsand scenarios. A fine tuning of these models is doneby setting parameters within these models to reflectthe real-world conditions as closely as possible. Whilepropagation models for sub 5 GHz frequencies are wellestablished, research on developing such models forhigher frequencies such as mmWaves is still in progress[43].

D. Cell Planning OutputsThe goal of the CP process is to provide one or more of

the following outputs:1) The optimal number of base stations;2) The best locations to install base stations;3) The types of base station optimal for each location;4) The configuration of parameters such as antenna height,number of sectors and sector orientation, tilt, power;

5) Frequency reuse pattern;6) Capacity dimensioning, e.g. number of carriers or carriercomponents per sector.

E. Types of CP and their ComplexityThe objectives, input and output of the CP process also

depend on the type of planning. There are generally two typesof CP, roll out and incremental, as explained below:1) Roll-out CP: This is the CP where no prior networksexists and a plain state approach can be used to meetall the objectives of interest. In terms of input parame-ters, in this phase the traffic distribution is not exactlyknown yet. Estimates of traffic based on geo-marketingforecasts are used for planning in this phase



5

2) Incremental Planning: This type of CP is generallycarried out after the first roll-out planning to meet the in-creasing demand. Unlike the plane state approach, plan-ning in this phase is bounded by additional constraintsimposed by existing sites. However, in this phase thetraffic distribution can be modeled now with much betteraccuracy using the measurements from existing networkreports [44]. It is anticipated that 5G deployment willmostly require incremental planning by building onLTE/UMTS/GSM network.

Both CP types correspond to the second part of Fig. 2, withincremental planning also touching on the third part (postdeployment optimization).

F. A sample formulation of CP and complexity analysis

For CP , if the main objective is to maximize service areafairness, maximize capacity and minimize power consumptionin the system. The objective function can be modelled for anumber of constraints. Using the notation defined in Table I,the problem of holistically planning a cellular network cannow be formulated as multi-objective optimisation problem asbelow:

maxQb,Qr,Hr,Hb,Sb,Ps,Pr,Fs,φs,θ

f(Γ,Υ,Ω) (1)

subject to:B ! Bmax

R ! Rmax

1 ! Sb ! Sb,max , ∀Sb ∈ Sb

360Sb

i−kφ,maxδφ ! φs ! 360Sb

i+kφ,maxδφ, i = 1, 2, 3...Sb

0 ! θs ! kθ,maxδθ , ∀θs ∈ θ

hs,min ! hs ! hs,max , ∀hs ∈ Hs

hr,min ! hr ! hr,max , ∀hr ∈ Hr

ps,min ! ps ! ps,max, ∀ps ∈ Ps

pr,min ! pr ! pr,max , ∀pr ∈ Pr

The above formulation can help to gauge the unfathomablenessof the solution space of the CP problem. Taking a toyexample of only 56 cell cellular system and focusing onsolving for optimal tilt angle only, assuming a quantizationto ten possible values, a brute force based solution will haveto assess kθ,max + 1(

!Bb=1 Sb) = 1056 possible solutions.

With a state of the art computer having processing speed of1012 evaluations per second, finding an optimal solution mayrequire as long as 1056

1012×8.6×104×365 seconds, which is clearlyprohibitive. Note that the actual size of the solution space ofa typical holistic planning problem represented by (1) wouldbe even more gigantic as can be sketched by the expressionbelow:

TABLE ISYMBOL DESCRIPTION

Symbol Descriptionb bth base stationB set of all base stations in systemsB total number of BS i.e. |B| = B

Bmax maximum number of BS that can be afforded.A Total area of interestQ set of Q bins that constitute Aq qth bin,

!Qi=1 qi = A, & A

Q = q, ∀q ∈ QQb set of bins in which BS are located, Qb ⊆ QS set of all sectors in the systemsS total number of sectors in system i.e. |S| = Ss denotes sth sectorSb total number of sectors bth BS hasSb Sb = S1, S2, S3...SB, S = |S| =

!Bb=1 Sb

Sb,max maximum number of sectors a BS can havehs (antenna) height of sth sector antenna on BSHs set of all sector antenna heights

hs,max maximum allowed hs

hs,min minimum allowed hs

δhbstep with which hs can vary

fs fractional frequency reuse factors in sth sectorF set of fs for all sectors.kf number of different values fs can haveR set of RSs in the system R i.e. |R| = Rr rth RS in the system

Rmax maximum RSs that can be affordedQr set of bins in which RS are located, Qr ⊆ QHr set of all RS antenna heightshr height of rth RS antenna,δhr step with which hr can varyφ vector of azimuth angles of all sectorsφs azimuth angle of sth sectorθ set of tilt angles of all sectorsθs tilt angle of sth sector

δφ, δθ steps sizes for azimuth and tilts changekθ,max maximum steps of tilt changekφ,max maximum steps of azimuth change

Ps set of transmission powers of all sectorsps transmission power from sth sectorδps step with which ps can vary

ps,max maximum allowed value of ps

ps,min minimum allowed value of ps

Pr set of transmission powers of all RSpr transmission power from rth RSδpr step with which pr can vary

pr,max maximum allowed value of pr

pr,min minimum allowed value of pr

Gsq gain from the sth sector antenna to qth bin.

α path loss co-efficientβ pathloss exponentϕv vertical beamwidth of the antennaϕs

h horizontal beamwidth of sth sector antennaΥ capacity wise performance indicatorΩ total power consumption in the systemΓ service area fairness wise key performance indicator (KPI)

X\y means all elements of X except y.



6

Q!B!(Q − B)!

× (Q − B)!R!(Q − B − R)!

×

!hs

b,max − hsb,min

δh+ 1

"(!Bb=1 Sb)

× (Smax)B×!

hr,max − hr,min

δh+ 1

"R

×!

psb,max − ps

b,min

δp+ 1

"(!Bb=1 Sb)

×!

pr,max − pr,min

δh+ 1

"R

× k(!B

b=1 Sb)f × kφ,max × 2 + 1(

!Bb=1 Sb)

× kθ,max + 1(!B

b=1 Sb) (2)

By the preponderance of (2) it is clear that a brute forcesolution is theoretically not possible in reasonable computingtime. Since there is no known polynomial time efficient algo-rithm in the literature for this or similar problem, CP problemhas been shown to be NP-hard a number of times [45], [46],[47], [48]. Appreciation of the complexity of cell planningproblem through this analysis should help better understandrationale behind solution approaches taken in various worksin the literature.

G. Coping with NP-hard CP Problems in PracticeWhen considering the CP problems that belong to the

class of NP-hard combinatorial problems, the most commonapproaches that have been used in literature can be classifiedinto the following:

• Easy special cases. In this approach, the problem isnot solved in its full generality. Rather properties of theinput instances are identified and exploited that makethe problem easier and mathematically tractable, andthen an algorithm is designed that makes use of theseproperties. Although the advantages of this approach suchas robustness and transparency are strongly advocated in[49], [50], it largely remains an under explored territoryin CP domain. Recent examples of use of this approachcan be found in [51], [52].

• Somewhat efficient exponential algorithms. Here analgorithm is designed that always solves the problem withrunning time not polynomial, but still much faster thanexhaustive search. This approach may be useful for inputsof moderate size. Examples for use of this approach canbe found in [53], [26], [54].

• Approximation algorithms. In this approach the qualityof solution is sacrificed to obtain more efficient algo-rithms. Instead of finding the optimal solution, the algo-rithm settles for a near optimal solution with advantageof making the problem easier. Examples of use of suchapproach can be found in [55], [56], [57].

• Heuristics In this approach heuristics are used to designalgorithms that work well on many instances, thoughnot on all instances. This is perhaps the approach

most commonly used in practice [58] and heuristicssuch as simulated annealing [15], [59], genetic algo-rithms [47], [60], [61], [62], particle swarm [48], [63],Taguchi’s method [64], bee colony optimisation [65],tabu-search [66] or k-mean algorithm [67] have beenapplied to obtain near optimal solutions for various CPproblems. A detailed discussion of use of evolutionaryheuristic for planning problems can be found in [61].

• Hybrid approach A number of hybrid approaches thatcombine analytical and heuristic techniques or combinemore than one heuristic in cascaded stages to solve theNP-hard planning problems have also been proposed inthe literature [68]. For example, authors in [69] presenta hybrid approach consisting of three stages. In the firststage, a good feasible solution to the problem is foundby using constraint satisfaction technique embedded witha problem-specific search guidance. The second stage isto apply a good local search procedure to improve thissolution. The third stage is to make a further improvementto the solution derived from the second stage. The bestobjective function value obtained from the second stageis used as the upper bound, then a constraint optimizationtechnique is applied to improve the solution. Numericalresults show that optimal solutions are always obtainedfor small to medium sized problems. For larger sizedproblems, the final results are on average within 6 -7 percent of the lower bounds. Such hybrid approachcan be an efficient tool for tackling a wide range ofcombinatorial NP-hard problems.

III. RECAP OF THE PRIOR STUDIES ON CPThe objective of this article is not to provide a com-prehensive survey the past of CP but characterize thefuture of CP by building on insights from past andpresent. Therefore, instead of providing a detailed reviewof past literature on planning, in Table II, we conciselysummarize the representative research works on CP thathave been carried out in time between the dawn of 3rdgeneration cellular system (UMTS) and the emergence ofa 4th generation cellular system (LTE). In addition to thedetailed classification labels given in columns of TableII, these works can broadly be classified into– Classic CP– Holistic CP

The Classic CP was mainly concerned with optimisingthe number and location of base stations. As we can seein Fig. 5 , in the previous technologies there were nonew features added. With the widespread deployment of2G and 3G cellular networks, explosive traffic demand,and invasive data services, the classic planning could nolonger serve the objectives of CP. In order to cater tomultiple objectives, such as coverage, capacity, QoS, costof network and energy consumption, many parametersneeded to be considered in the CP, which motivated thedevelopment of holistic CP.In the Table II, we have also summarized the holisticCP parameters whose optimization have been addressed



7

TABLE IIPARAMETERS ADDRESSED IN THE CP LITERATURE

Parameters BSLocation

Frequency

Budget

Incremental

ConsideringInterference

Tx.power,pilotplanning

Height

Heterogeneoustraffic

No.ofSectors

ConsideringBS

Antennas

AzimuthPlanning

Tiltplanning

2G/TDMAfriendly

3G/CDMAfriendly

4G/OFDMAfriendly

Trafficuncertainties

energyefficiency

heterogeneousdeployment

analyticalmodels

[6], [70], [71] ! ! ! ![72], [73], [71] ! ! ! ! ! ! ! ! ![10], [74] ! ! ! ! ! ![75] ! ! ! ! ! ! ! ! ![76] ! ! ! ! !

[66], [77] ! ± ! ± ! ±[8], [47] ! ! ! ![60] ! ! ![70] ! ! !

[49], [36] ! ! ! ![68], [78] ! ![79] ! ! ! ! ![80] ! ! ! ! ![81] ! ! ! ! ! ![82] ! ! ! ! ! ! ! ![83] ! ! ± ± ±[84] ! ± ± ± ![85] ! ! ± ± ±[16] ! ! ![61] ! ! ! ! ! ! ! ! ![86] ! ! ![87] ! ! ! ! !

[88], [89] ! ! ! !

and researched in CP literature. The table provides ataxonomy of the works done in terms of BS location, fre-quency, budget, interference, transmission power, height,heterogeneous traffic, number of sectors, antennas, tiltplanning, traffic uncertainties, and analytical models etc.

IV. RECENT TRENDS IN PLANNING FUTURENETWORKS

In this section, we review the literature on recent trendsin CP that include considerations for energy efficiency,uncertainty in traffic, and CoMP.

A. Energy Focused Planning: Green PlanningIn recent years improving energy efficiency in cellularoperation has become an integral part of CP, partly toreduce carbon footprint and partly to reduce OPEX. Fur-thermore, as the cellular networks are becoming denserand revenues per bit are decreasing, the need for energyefficient cellular systems is growing more than ever. In thefollowing we review several mechanisms to incorporateenergy efficiency in to the CP process that have beenproposed in literature.1) Energy Savings through optimal BS positioning:Authors in [80] tackle the basic BS location problem andassignment of mobile users to appropriate BSs in 3G W-CDMA uplink environment. The authors propose a con-straint satisfaction model and apply different techniqueslike variable ordering and value ordering to find good

optimal solutions. Instead of cost minimization, the ob-jective of their model is to minimize the total transmittedpower. Once the location and power configuration of BSsare known, the next step is to study BS assignment to thehigher level. This involves the investigation of an accessnetwork sub problem. It is shown that local approaches,that aim at reducing the energy consumption of individualnetwork components, can be quite effective. However,global approaches, that consider the entire network en-ergy consumption in the network design, planning, andmanagement phases are a must, for a holistic approachto energy efficient networking.Compared to previous works on energy savings via BSswitching, in [90], [91] the authors investigate the domi-nating factors in the energy savings. Energy consumptionof the BS amounts to nearly 850W, with the energyneeded to transmit from the antennas amounting only upto 40W and the rest expended even in case of idle opera-tion. Their analysis shows that the mean and variance oftraffic profile and the BS density are the dominant factorsthat determine the amount of achievable energy saving.Moreover, an expression is obtained that indicates that theenergy saving increases when the traffic, mean/varianceratio and the number of neighboring BS have highervalue. It means, for instance, that the greatest energysavings are likely to be realized in urban commercialareas (since such an area is likely to show both hightraffic variance between day time and night time as well



8

as high BS density). It is also emphasized that, the slopeof traffic variation is more important than the maximumvalue in estimating the traffic profile because the slopedirectly determines the switching-on/off time.In [92] authors investigate techniques to optimize thenumber of base stations and their locations for energyefficiency. The key contribution of this work is thatit takes into account the nonuniform user distribution.Authors make use of a stochastic programming approachusing mixed integer programming to model and solve thebase station location problem from a BS power efficiencyperspective. It is claimed a power reduction of at least96% is possible with the proposed solution. However, theproposed techniques assume full knowledge of channelstate information (CSI) at BS while neglecting effect ofsmall scale fading and shadowing.2) Energy Savings through BS with proportional energyModel: In [93] authors provide an analytical estimationof the energy savings that can be achieved for two BSmodels: a) On-Off BS energy model (current BS aremore of this type) b) Proportional energy model whereenergy consumed is proportional to load in the cell.They use a QoS metric of delay, which is defined asinverse of throughput, which is further defined as anabstract function of distance from BS only, for analyticaltractability. With this model, they present expressionsfor expected delay and variance of delay for given BSand user densities. These expressions are then used toanalyze the BS densities and, hence, energy consumptionfor a given user density and QoS constraints. They alsoformally show the fact that lowest BS density for a givenuser density is possible with circular cells (as circle haslargest area and thus largest number of users for a givendistance allowed form the center of shape i.e. BS). Theproof actually yields lower bound on the BS densities forany allowed topology of BSs (grid, hexagon, Poisson).The key inferences obtained are: 1) Poisson topologyis less energy efficient than the regular topologies dueto irregularly large distances from BS; 2) on/off modelallows much more energy savings than the proportionalmodel, advocating the use of system level techniques(compared to transmission power focused physical layertechniques that try to reduce variable energy consump-tions on individual BSs). With proportional energy model(i.e. futuristic and unrealistic at the moment) the optimalenergy saving model is not the one with lowest BSdensity. This advocates low power large number of smallcells.3) Energy savings by switching ON/OFF BS: In [94],the authors present a methodology to calculate the en-ergy savings by switching off BSs. They model energyconsumption as a linear function of the number of BSs.Then, using the traffic profile for 24 hours, it is arguedthat as the traffic decreased by a factor X, a fractionX of the BSs can be shut down, and consequently,energy consumption will also be reduced by a factor X.Next, they remove the assumption that any BS can beshut down, and suggest that, in specific topologies, only

certain BSs can be shut down to avoid coverage holes(e.g. in hexagon, six out of seven or three out of fourBSs can be shut down). Similarly, authors identify thenumber of BSs for crossroad (urban street scenario witheach cell having four neighbors) and Manhattan lay out.Note that the paper assumes omni directional antennasappointed in center of the cells and does not quantify theloss of coverage, capacity or takes into account the localuser demands when shutting down BSs.Authors in [95], [94], [93] present a scheme for energymanagement of base stations according to the networktraffic that incorporates binary on/off activation or con-tinuous cell zooming capabilities at the BSs. It is shownthat noticeable energy savings can be achieved for lownetwork traffic.The authors in [96] present energy efficiency metricsand investigate the performance of different planningstrategies of LTE networks in an empirical way. In [97],the authors propose to incorporate the on/off switchingof BSs in the planning process itself. They first presenta heuristic to have a minimum number of BSs. In thisalgorithm, first a Verona tessellation is established, thenBSs are classified in feasible and infeasible set. A feasibleset consists of BSs whose removal will not decrease thecoverage below the threshold. This step is repeated untilno BS can be removed without decreasing the coverage.To incorporate on/off switching, first the network isplanned for the lowest traffic (this defines the infeasibleset that cannot be switched off) and then it is planned forhighest load. Turning on additional BS and finding theirlocations is done by repeating the same algorithm.In [98], the authors use a detailed energy consumptionmodel of the BS and definitions of site load factor topredict how much energy will be consumed to providetarget capacity demands (100 Mbps in the paper). Theytake the energy consumption and capacity of existingHSPA+ in Finland (2008) as reference and compare itwith that of LTE while considering the gains obtained inLTE by node level (energy/capacity) efficiency as well asby network level deployment strategies.4) Energy saving though cell size adaptation: In [99]authors present an analytical framework coupled witha simple mathematical traffic model, to investigate thepotential energy savings that can be achieved by adap-tively adjusting the cell size according to the spatialtraffic variation. The key idea is that, instead of havingthe same cell size throughout, areas with low trafficdensity can have larger cells compared to areas withhigh traffic density, resulting in energy and cost savings.The cell radius is calculated such that the cell includesmaximum number of users while maintaining a thresholdblocking probability resulting from underlying M/M/N/0queuing model of traffic and geographically varying userdensity. The results show that energy savings increaseas cell density decreases until a certain point, wherethe large transmission power overcomes the fixed powerconsumptions, and the energy savings start to diminish,reaching negative values. This work does not incorporate



9

Fig. 3. Green planning with BS on/off switching: proactive versus reactiveapproach.

impact of interference, frequency reuse and sectorizationon coverage.In [56] the same authors present a modification of theirwork in [99] again for energy savings in LTE. Hereinstead of adapting the cell sizes in order to cope withspatial traffic variation, they propose dynamically adapt-ing the number of sectors per site (three to two sectorsper site) to cope with temporal variation of the sectors.They argue that though adaptive sectorization has beenpreviously used for CDMA systems, it suffered fromtwo short comings: 1) QoS constraints are not taken intoconsideration which makes the user terminals suffer fromhigh blocking probability and low coverage once somesectors are off; 2) the transmission power of the BSsis assumed to be adjustable in a large dynamic rangewhich is normally impractical in real systems because ofthe power amplifier and RF link. In order to overcomethis shortcoming they suggest to increase the beam widthand change the azimuth of two remaining sectors whenthe third is shut down in low traffic times. The QoSand power constraints are satisfied while saving 21%energy per BS using EARTHs measurement based powerconsumption model of the BS. Instead of full buffertraffic model, they use event triggered traffic model that isbased on the continuous time Markov process. However,the results presented seem to be independent of theunderlying traffic model.5) Energy saving through Network Infrastructure shar-ing : In [100], the authors exploit the observation thatmetropolitan areas are normally served by a few com-peting cellular network operators, providing 24/7 fullcoverage, each dimensioning its network according topeak traffic, but providing redundant resources whentraffic is low. So provided that operators are willing toaccept the competitors subscribers as roaming customers,some energy can be saved. For the case of just twooperators, the authors show that 20% or more energy canbe saved, though the exact saving will depend on lot ofparameters including operator policies, that remain to beinvestigated.6) Energy Savings through Proactive Approach: The pre-viously proposed approaches for switching BSs on andoff are reactive, i.e., the network plan and BS sites are

fixed and then reactive measures are implemented todecide which sites to switch on/off and which sites toreconfigure as network conditions change. To increaseenergy efficiency, an alternative approach would be toperform network planning as a function of the traffic loadover large-scale time durations (hours and days) insteadof planning only based on peak hour worst case trafficconditions. The process would then start by performingnetwork planning based on the low traffic load conditions;this will give the set of BSs that need to be switched onat all times (during both high and low load conditions).The next step would be to perform network planningbased on the higher traffic load conditions but with thesites obtained in the previous step fixed. The aim isto determine how many new sites to add and whereto place them in order to support the increase in load;these are the sites to be additionally switched on in highload conditions. This approach, summarized in Fig. 3 isgeneric enough to account for any number of traffic loadstates depending on the large-scale traffic variations inthe area of interest.A proactive approach proposing a modeling and optimi-sation framework for the planning of energy-aware wire-less networks is introduced in [101]. The key idea isthat energy awareness should be introduced at the plan-ning stage in order to reach energy-efficient networkoperation. The authors formulate a joint planning andenergy management problem, that aims to minimize autility comprising a weighted sum of CAPEX and OPEX,including power consumption costs. By solving a binarylinear program, the authors optimize BS positions, types,and configurations.

B. Planning with Traffic Uncertainty ConsiderationsDue to heavy traffic fluctuation over time cellular net-works, operators often use peak hour traffic volumeduring network planning in order to avoid capacity bot-tlenecks [102]. A more efficient approach would beto consider the design of cellular networks under trafficuncertainty. For example, in [103] the authors suggest thatbetter radio resource usage is possible by incorporatingthe time-varying traffic in the planning. They demonstratethat the user mobility can be similarly converted to themulti-period optimization problem. To this end, they for-mulate a simple one dimensional cell planning problemand demonstrate how it can be converted into a binarylinear programming problem to look for the optimalsolution. An overview of the most important techniquesdealing with traffic uncertainty is presented in [104]. Theliterature that provides solutions for planning with trafficuncertainty can be divided in two categories discussedbelow.1) Multi-Period Network Design: In multi-period (multi-hour) network design [105], an explicit set of demandmatrices is given, and the network is designed in sucha way that each of the demand matrices can be routednon-simultaneously within the installed capacities. In



10

this context, authors in [106] introduce the concept ofdominating demand matrices (i.e., D1 dominates D2 ifevery link capacity vector supporting D1 also supportsD2). Instead of describing demand matrices explicitly,the authors in [107] consider the optimized routing ofdemands that may vary within a given prototype. Fornetwork design problems, this concept has mainly beenapplied using the hose model, a polyhedral demanduncertainty set which has been introduced in the contextof virtual private networks (VPNs) [108]. More detailsand a good overview about methods to deal with networkdesign under uncertainty are presented in [102], whererobust optimization is advocated as a possible solution.2) Robust Optimization: Robust optimization was firstconsidered by Soyster [109], and it aims at findingsolutions that are feasible for all realizations of data in agiven (bounded) uncertainty set. In robust optimisation aparameter Γ is used to control the price of robustness,the trade-off between the degree of uncertainty takeninto account and the cost of this additional feature [110].In [111] authors apply robust optimization to deal withdemand uncertainty in cellular networks. Their robustoptimization model offers for operators a trade- offbetween robustness and energy consumption by varyingthe robustness parameter. The complexity of problemis reduced by applying cutting planes. A case studyis performed to compare the robust formulation to itsdeterministic counterpart and to conventional networkplanning. It is observed that energy savings are possibleeither by deploying less BSs or serving more users withthe same number of BSs using the proposed robustoptimization approach.

C. Planning with CoMPIn conventional CP, the BS coverage areas are controlledto minimize coverage overlap. However, when the BSscan coordinate to dynamically reduce interfere or bal-ance loads, as in CoMP, standardised for LTE-Advanced(Release 11) and beyond, signal coverage overlap canbe tolerated or even becomes desired. Thus, planningwith CoMP becomes a very different problem comparedto traditional planning discussed in previous sections,triggering some dedicated studies in recent years.In [112] authors investigate the impact of coordinatedmultipoint (CoMP) transmission on cell planning pa-rameters such as coverage, traffic, handover, and cost.To assess achievable coverage and capacities with andwithout CoMP, authors propose and use two ratios: Localto-uncooperative- plus-noise ratio (LUNR) and the localto-cooperative-ratio (LCR). Simulation results show thatCoMP maximizes its gains over noncooperation (NC) ina network. However, NC may produce higher throughputin certain scenarios. Therefore, to avoid low system wideaverage throughput with CoMP, authors recommend adynamic or semi-static switching between CoMP and NCcalled fractional base station cooperation. It is highlightedthat because of interference from non- cooperating BSs,

the gains of CoMP over NC are upper bounded anddiminish at greater inter site distances due to noise. Thisencourages smaller cell sizes, higher transmit powers,and dynamic clustering of cooperative BSs. Cell planningwith CoMP may require additional steps e.g. determina-tion of cooperative and non-cooperative regions, LCR,and LUNR thresholds. Findings also show that gains ofCoMP remain moderate, hence the complexity and costincurred by CoMP should also remain moderate. Thekey limitation of this work is that each BS is assumedto be incapacitated. Therefore, user throughput solelydetermines the selection of the BS cluster in dynamicclustering, and load balancing remains an unexploredaspect.In [113], the authors compare the energy saving potentialof relay station (RS) and CoMP with single BS sce-nario while maintaining an average outage constraint.The impact of the traffic intensity and BS density arealso investigated. Results show that traffic intensity canbe divided into three classes: coverage-limited region,energy-efficient region, and capacity-limited region. Theinteresting finding is, as BS density goes higher, theenergy-efficient region becomes larger, and the trafficload region where the cooperation schemes bring benefitsbecomes smaller. Furthermore, it is observed that RSsenergy cost needs to be designed as low as possible toget high performance, otherwise BS cooperation would bemore favorable. The analytical models developed provideuseful insights for green planning with CoMPIn [114], the authors extend their work in [113] forBS location and number planning taking into accountCoMP, thus making the process of network planning moreenergy- aware. The optimal network planning problem isformulated as a mixed integer programming problem andan approximate solution is proposed using Lagrangianrelaxation. Numerical results show that the overall energyconsumption is decreased by over 20% compared withno cooperation while the system QoS is guaranteed. Itis also observed that the low network density and trafficdistribution asymmetry lead to higher energy efficiencygain.

V. NEW MODELING TOOLS FOR PLANNING FUTURENETWORKS

In wake of new requirements and technologies discussedin previous section that are a becoming vital part of theCP paradigm, planning future networks call for corre-sponding evolution in modelling tools. In this section,we provide a brief overview of recent developments inmodels that have been proposed in literature to cope withnew requirements in CP paradigm

A. Modeling dynamic channel variations in CPIn [115], the authors argue that conventional planningtechniques rely on static propagation and interferencemodels, which, although they take geographic informa-tion into account, they overlook the dynamic channel



11

Fig. 4. Typical phases of cellular network planning and parameters taken into consideration in each phase

variations. To bridge the gap between static and dynamicplanning the authors established a simple relationshipthat relates the static SIR to its dynamic counterpartby a factor, which represents the influence of a fadingenvironment. They use this new relation to study severalaspects of a cellular system that reveal the physicalimplication of the static SIR in a dynamic operationalenvironment. They also provide a simple method forevaluating the average outage probability. Finally, theydetermine the relationship between the conflicting re-quirements on the system capacity and on the minimumoutage performance.

B. Modeling Multi-Carrier systems in CP

In [116], the authors argue that classic interference mod-els used in CP have been limited to single carrier systemsand present an analytical method to assess effectiveSINR in multi- carrier systems operating over frequencyselective channels. This extension of single carrier isachieved by expressing the link outage probability interms of the statistics of the effective SINR. Two approx-imations for the link outage probability are obtained byconsidering Log-Normal and Gaussian assumptions forthe derivation of the statistics of the exponential effectiveSINR. The SINR statistics are used to further assess theoutage probability and thus obtain a simplified planningprocedure for two cells interference scenario.

C. Modeling Cell Load in CP

In [117], the authors introduced a method to analyti-cally approximate cell load levels while planning OFDMnetworks by building on corresponding ideas in UMTSnetworks, namely load scaling and continuous trafficdistributions. The idealized power control equations forUMTS are replaced by affine linear approximations ofthe adaptive modulation and coding (AMC) mechanismfor OFDM which results in a different structure of theequations. They proposed a simple iteration for solvingthe equations numerically. The approach avoids timeconsuming snapshot simulations. The model is usefulfor automatic network planning and optimization as fastanalytical capacity evaluation can build the foundationfor various local search algorithms for improving networkdesigns.The authors in [118], extend their work in [119] andpresent a mathematical analysis of fundamental proper-ties of the load coupling among cells for LTE system.They also develop and prove a sufficient and neces-sary condition for the solutions existence. Theoreticalresults for numerically approaching the solution or de-livering a bounding interval are also presented. Finally,the application of the proposed system model for plan-ning LTE network is presented. The analysis in [118]has been supported by theoretical proofs and numericalexperiments and can serve as a basis for developingradio network planning and optimization strategies forLTE. Furthermore, the presented linearization and the



12

bounding based optimization can potentially be used formore general convex optimization problems with similarproperties. However, the analysis does not take intoaccount interference dynamics specific to the LTE. In theSINR model of [118], interference is considered fromall cells weighted with the load in those cells only. Inother words if two interfering cells A and B are loaded50% and 20%, the interference from these cells will bescaled by 0.5 and 0.2 when being received by the cellunder consideration. This is more like CDMA whereinterference is independent of frequency reuse. In LTEpresence of sub carrier allocation and scheduling meansboth cell A and B can still be interfering as long asweight is greater than zero depending on if that particularsub carrier is being reused or not. Furthermore, use offractional frequency reuse adds another dimension to theinterference modeling in LTE that is not considered inthis paper.

D. Modelling BS location Randomness in CPIn [51], the authors argue that, although the problem ofBS placement has been addressed with standard Voronoipartitioning, the standard Voronoi partition cannot beused in a scenario where the BSs have heterogeneousand anisotropic characteristics (directional antennas) orwhere geographical terrain is not planar two-dimensional.Therefore they suggest to use a generalization of thestandard Voronoi partition replacing the usual distancemeasure with the concept of an abstract general function(named node function in the paper) associated with eachsite. The optimisation objective is formulated as a productof the node function with user density, integrated overthe generalised Voronoi tessellation of each BS and thensummed over all BSs. It is concluded that the solutionwill be to place each BS at the centroid of generaltessellations (for which an abstract formula is given). Butit is not clear from the paper whether the BS location orits corresponding tessellation will be decided first.In [120] authors address BS location problem with a sin-gle objective of minimizing outage (evaluated by MonteCarlo simulations). Their case study assumes HSDPAsystem with pre-presence of fixed number of micro sites.For performance evaluation they make use of a planningtool and Monte Carlo method using real network data thatincludes: 3-D geolocations of the base stations, digitalelevation map and digital clutter map data, antenna char-acteristics (pattern, tilt, and cable losses), total transmitpower levels and spatial broadband traffic.The proposed algorithm to address the problem, namedSMART, consists of three simple steps.Greedy, SimulatedAnnealing (SA), Greedy with memory (Greedy Mem),and Simulated Annealing with memory (SAMem). It isshown that simulated Annealing and Greedy algorithmsachieve almost the same optimal deployment when thenumber of optimization iterations is large and, the greedyalgorithm converges much faster than simulated annealingalgorithm when the number of optimization iterations is

small. It is also shown that in practical network deploy-ment scenario SMART outperforms previously proposedmeta heuristics such as Deployment Formula Metric(DFM) based schemes proposed in [121] and [102].

E. Modeling Intercell InterferenceIn [122], intercell interference modeling is performed inthe uplink, while taking user scheduling into account.Scenarios with round robin, proportional fair, and max-imum SINR are considered in the presence of variousfading types. The obtained semi-analytical expressionsare used to evaluate network performance metrics suchas the outage probability, ergodic capacity, and averagefairness numerically. The derived model can be usefulas an input for radio network planning algorithms, inorder to take inter-cell interference into account with userscheduling during the planning processModeling intercell interference in the presence of up-link power control is investigated by the same authorsin [123]. Fading is also incorporated in the models,along with basic scheduling assumptions. The expressionsderived in [123] are then utilized to quantify numericallycertain network performance metrics including averageresource fairness, average reduction in power consump-tion, and ergodic capacity. Although the models of [122]and [123] are derived for the uplink, indications on theirdownlink extensions are described. Main limitation ofthese models stems from considering a single subcarrier.Generalization to the scenario of multiple OFDMA sub-carriers with dynamic subcarrier allocation (as in the caseof LTE scheduling) is a daunting task.

F. Total Cost of Ownership Models1) Net Present Value (NPV): In [124], authors use aplanning tool to compare the performance of severalalgorithms, using the optimization objective of NPV. NPVtakes into account expected revenues, CAPEX and OPEX(of BS as well as sectors) over a period of 6 years.Optimization is done in terms of BS locations, theirnumbers (out of a set of locations), and the numberof sectors per BS. These results show the Tabu searchperforms well compared to other approaches though atthe expense of additional execution time2) Cost Analysis of BS and Relay Station (RS) : In [125]authors do a cost analysis of joint BS and RS deploymentin the context of LTE (they assume 2x2 MIMO). To thisend, they use simple linear cost model that is sum ofBS and RS densities weighted with their relative costfactors. Then via simulations they derive the curves foriso-capacities while varying BS and RS densities (persquare kilometer). Hence, an increase in RS or BS densityincreases capacity in general, so by varying their densitiesreciprocally, the same capacities can be obtained. Allsuch combinations of RS-BS densities that achieve samecapacity (area spectral efficiency) make an iso-capacitycurve. Then for a given iso-curve the point where thelinear cost model defined earlier, is tangent to it, yields



13

Fig. 5. Brief history and time line of cellular technology generations. Different service offerings which became available in each generation are listed

the least cost deployment for achieving that capacity. Forthis kind of static deployment, the optimal number of RSsis reported between 7 and 11. As after certain RS density,further increase in RS density can decrease the capacity(the same capacity cannot be maintained), therefore, iso-capacity curves also provide and upper bound on the RSdensity. Authors also analyze the impact of back hauldistance on capacity-cost trade off of RS. They evaluatecost efficiency against number of RS per cell for variousbackhaul distances. Cost efficiency function proposed in[126] is used for this objective that is proportional tospectral efficiency (that is indirectly function of distance)and inversely proportional to the RS costs. The authorsfurther investigate the impact of progressive deploymentof RS. That is, unlike previous case where all RS are as-sumed to be deployed at once, authors assume RS mightbe deployed gradually e.g., one each year. To evaluatethis scenario authors, use measure called ACSI (AverageCustomer Satisfaction Index, a term from economy), thatquantifies user satisfaction after an upgrade relative toprevious network construction. Results suggest that interms of user satisfaction four relays are optimal.In [127], the authors carry out cost/revenue analysis ofWiMAX in presence of relays where revenue generated ismodeled as function of capacity produced. They analysethe impact of location of RS, frequency reuse topologiesand number of sectors on the cost/revenue optimizationresults show that trisector-ed BSs in topologies withrelays enable the operators to achieve more profitablereuse configurations than with omnidirectional BSs andno relays. In [125], the authors investigate the possibleenergy gains of evolving a mobile network through a

joint pico deployment and macro upgrade solution overa period of eight years. Besides the network energyconsumption, energy efficiency in Mbps/kW is also an-alyzed. Outcomes of cost analysis in terms of total costof ownership are shown for different deployment optionsconsidered. Using previous year of the evolution analysis,it is shown that deploying more pico sites reduces theenergy consumption of the network, by a maximum of30 percent. With regards to the energy efficiency, highdeployment of pico sites allowed the network to carry 16percent more traffic for the same amount of energy. This,however, results in an increase in operational costs.

VI. KEY CHALLENGES IN PLANING EMERGINGHETEROGENEOUS AND ULTRA DENSE NETWORKS

In addition to the challenges and constraints alreadyidentified in section II and III in the context of classicand holistic cellular planning, modern day CP faces newchallenges that stem from heterogeneity of the network,or more specifically advent of small cells. In this section,we discuss the challenges that are acting as Achilles heelfor small cells and HetNets planning.

A. UL and DL symmetryA peculiar feature of HetNets CP is pronounced uplinkdownlink asymmetry [128] that is generally neglected inmost academic research studies on HetNets. Althoughthis asymmetry exists in microcells only networks aswell, the difference between uplink and downlink ispotentially much larger in a HetNets. The reason forthis pronounced asymmetry in HetNets is as follows:



14

In the downlink, transmit power disparities of 20dB+exist between macrocells and small cells while this is nottrue for the uplink case wherein all transmitting UEs areroughly equal in terms of transmission power. In otherwords, in uplink direction, a small cell and a macro-cell appear to be same for transmitting UE. Therefore,from UEs perspective, for the downlink, macrocells havevery large coverage areas as compared to small cellswhile in uplink, coverage areas are roughly equal. Asa result, cell association based on maximum receivedsignal strength (Max-RSS) strategy may yield differentoptimal cells for uplink and downlink e.g., UEs that areconnected to macrocell due to better downlink receptionwould likely be better off by associating with near-bysmall cells in the uplink. Not allowing this asymmetricassociation might lead to sub-optimal performance. Onthe other hand, allowing independent traffic sources ineach direction raises new challenges for the core networkand also for the UEs QoS e.g., cell edge UE may havepoor SINR in one direction but not in the other. Thisdifferent cell association in uplink/downlink will result indifferent interference models and resulting SINRs in thetwo links e.g., UEs sharing same BS will be orthogonalto one another on the downlink while they may interferewith each other on the uplink if they are transmittingto different base stations. This calls for new two-waychannel models to be investigated and incorporated in theCP as the channel gains and SINR in the two directionsmay be almost uncorrelated especially if they are routedfrom different base stations.

B. BackhaulUltra-dense deployment of small cells will create addi-tional challenges for the transport of backhaul traffic. Cur-rent [129] research studies on HetNets utilize small num-ber of small cells to improve SINR of wireless links inlimited hotspot areas wherein a relatively small backhaultraffic originating from small cells can be forwarded intothe core network through conventional backhaul links ofcellular networks. These studies [129] focus on gains ofthe wireless front haul and neglect any possible backhaulbottlenecks. This assumption is generally correct for well-planned conventional macrocell only cellular networks.But this assumption breaks down for HetNets where smallcells are ultra-densely deployed, as in such deployments itmay become a key problem to forward massive traffic intothe core network through existing backhauls. It is nowbelieved that the full benefits of dense HetNets can berealized only if they are supported by the careful backhaulplanning [130].Femtocells deployed in homes by the subscribers gen-erally utilize digital subscriber line (DSL) broadbandconnection for the backhaul that can quickly becomebottleneck particularly in the Uplink [128]. IP trafficthrough traditional internet service providers (ISPs) isused to connect the femtocells with the core network.This demands high QoS requirements from broadband

connections. Several things need to be considered likewhether the broadband support QoS or traffic prioritiza-tion or is the connection throttled or traffic-shaped byISP. The probable high latency in broadband backhaulcan pose serious problems in coordination of resourceallocation or handoffs information with other cells. Uti-lizing untrusted IP network for backhaul poses serioussecurity issues as well. Macrocells enjoy commercialgrade security which is absent in small cells as they are indirect reach of the subscribers. As a result, small cells andpublic IP networks can be utilized to launch distributedattack on cellular network. Security planning challengesbecome another challenge for these unplanned networks.

C. CP for HetNets with Energy Efficiency ConstraintsEnergy Efficiency (EE) is emerging as one of the mainchallenges in rolling out HetNets. In this section wedescribe what makes EE a key constraint to be consideredwhile planning HetNet deployments.1) Planning for EE from Network perspective: Firstfactor that makes EE more significant in HetNet planningcompared to old macro cell only network planning isthe sheer increase in number of cells. A large portionof the energy dissipated in a cellular system is actuallyconsumed at the base stations (BSs) [131]. Although thesmall cells have a relatively lower power consumptionprofile, ultra-dense deployment can lead to high aggre-gated energy consumption. From EE perspective, smallcells are beneficial only when they are deployed in ideallocations where data requirements are high or macrocell performance is low [132]. On the contrary, smallcells in control of subscribers like femtocells may not bebeneficial in terms of aggregate EE as these small cellsare operational at all times of the day. Even in the absenceof users in their coverage, a substantial amount of circuitenergy is drawn by these nodes. Therefore, switchingcertain base stations off in light traffic conditions, is anefficient technique to save energy in wireless networks[131]. However, to implement such technique CP has tobe carried out in way that switching off those certaincells does not create coverage holes. Another alternativeis to put certain base stations into the sleep (dormant)mode. However, such dormant cells need to be preemp-tively activated when user devices are moving into theircoverage and new capacity is needed. For this to happen,the network must be able to wake the dormant smallcell before handing over traffic to the dormant cell. Thisrequires CP such that all sleep enabled cells have somesort of fast signaling connectivity with neighboring cellseither on front haul or back haul.One simpler approach is shutting down almost all themodules of a small cell based on a fixed timer configuredbased on statistical traffic cycle [132]. However, imple-mentation of such scheme requires carrying out whatis called multi-modal CP which in addition to spatialvariation in traffic has to consider temporal variationin traffic. This kind of CP is even more challenging



15

and has yet to be investigated. Another newly conceivedconstraint related to EE is CP where cell sites rely onrenewable energy source. In this type of planning inaddition to spatio temporal variation of traffic, spatiotemporal availability of the renewable energy source hasto be taken into account in the CP optimization problem[133].2) Planning for EE from UE Perspective: From UEsperspective, energy consumption becomes an issue inultra-dense deployment especially if small cells utilizeseparate frequency bands/RAT. UEs must periodicallyscan for nearby small cells for traffic offloading oppor-tunity that can result in significant energy consumptionfor the UE. Therefore, energy efficient discovery of smallcells becomes a problem in carrier frequency separateddeployment and a balanced inter-frequency small celldiscovery (ISCD) interval needs to be optimized. On onehand, low ISCD periodicity (i.e. high scanning frequency)can result in increased small cell offloading opportunity,thus enhancing the capacity and coverage. However, thiscan also lead to higher UE power consumption due tothe high scanning frequency and lower transmit power ofsmall cells. On the other hand, high ISCD periodicity (i.e.low scanning frequency) can lead to the UE missing smallcell off-loading opportunity, thus resulting in a potentialdecrease in capacity.In a recent study on ISCD [134], it has been shown thatfor given cell density and UE mobility there exists anoptimal ISCD frequency in terms of EE. As UE batterylife is already a bottle neck in era of smart phones, thisfinding needs to be incorporated in CP for HetNets todetermine optimal cell densities, not only from capacityperspective but EE perspective.CP for emerging HetNets, need to take all of the afore-mentioned EE constraints into account, which make ita drastically different problem compared to the onesstudied extensively in literature for macro cell planningwith focus on coverage and capacity only.

D. CP with Deployment location Constraints in HetNets1) BS placement Optimization in HetNets: Planning ofsmall cells was investigated in [135] assuming macrocelllocations are fixed. In [59], the BS placement opti-mization was performed jointly for macrocell and smallcell BSs in a non uniform user density scenario. Aheuristic approach based on simulated annealing wasadopted, taking into account intercell interference, dy-namic resource management, and joint uplink/downlinkperformance. The authors demonstrate the efficiency ofHetNet planning in a hotspot scenario, shown in Fig. 7,simulated using a Gaussian user distribution assumingfour macrocell BSs and 64 small cell BSs. In fact, thesimulated annealing approach move the small cells fromtheir rectangular grid positions to a Gaussian deployment,following that of the user distribution. Such hotspot areasare characterised with temporary traffic surges, such asfootball stadiums, where the user density is very high

around a football match and very low otherwise. For thisreason, the HetNet deployment scenario is convenient,since small cells can be switched on and off accordingto the varying traffic.2) Sensitivity to small cell deployment location in Het-Nets: While planning, conventional cellular networksconsisting only the macro cells, even if the macro cellsare not deployed at the ideal location but somewhatnear the optimal location, the larger radius of the macrocell, and the ability to tweak antenna tilts and azimuthscompensates the difference between optimal and theactual location. This tolerance for difference betweenactual and optimal location in case of small cells de-creases due to their small coverage areas and relativelyinflexible antennas. As a result, contrary to popular belief,physical location of the small cell requires more preciseengineering than macro cells. A slight difference betweenthe optimal and actual location of small cell due to thephysical limitations or real estate constraints can actuallydegrade network performance as small cell location canhave large impact on interference pattern and mobilityrelated performance. Therefore, it becomes imperative tomake optimal decisions in HetNet planning for small celllocations to achieve efficient performance of the network.3) CP with RS Positioning Considerations in HetNets:Most studies that investigate RS based deployments ofHetNets generally assume RS locations to be prefixedby some arbitrary criterion. On the contrary to harnessthe full advantage of RS its location is expected tobe more impromptu than BS and hence the impact oflocation of RS on the performance of the system has to beinvestigated. In [136], the authors address this challengeby building on previous work in [137] and present an ana-lytical study of RS positioning. The impact of RS locationselection and cell selection on the system performanceare evaluated in a single interferer scenario. Closed-formexpressions for the link SIR, link rate, and end-to-enduser rate distributions are obtained. Similarly, in [138]authors have highlighted that existing theoretical analysison RS performance assessment has primarily focused onGaussian relay channels, and the analysis of interference-limited relay deployment has been confined to simulationbased approaches. In their paper, they take the initiative toprovide analytical closed form expression to asses systemcapacity as function of the optimal location and numberof relays, and resource sharing between relay and base-stations. The paper shows that the optimal deploymentparameters are pre-dominantly a function of the saturationcapacity, path loss exponent and transmit powers.

E. CP with Off-loading Considerations in HetNetsIn [135], the authors attempt to optimally plan smallcell locations for offloading traffic from macro to smallcells in a HetNet LTE cellular network. The challenge iscomplicated further by interference coupling between cellloads in a non- linear manner. A search algorithm leadingto near-optimal solutions is proposed. Its objective is



16

to select, from a set of candidate locations, up to agiven number of small cell sites in a deployed macrocell network. In addition, an approach for numericallyconstructing a tight linear approximation is proposed, inorder to enable the use of mixed integer linear program-ming to gauge optimality.

F. Planning with Multi-RATBeyond these early pioneering works on HetNets plan-ning discussed in previous subsections, detailed networkplanning in the presence of HetNets still needs a morethorough and detailed investigation. Other more complexscenarios, such as planning a network in the presence offemtocells [139], that can be considered as small cells de-ployed without operator control, make the problem morechallenging. Another relevant scenario is the planningwith multiple radio access technologies (RAT), whereother technologies, such as WiFi, can be used to offloadcellular traffic. This multi-RAT operation can be uncon-trolled, similar to the case of femtocell deployments, oroperator-controlled, where the mobile operators deployWiFi access points to offload part of the cellular traffic.

VII. CHALLENGES AND OPPORTUNITIES INPLANNING FUTURE CELLULAR NETWORKS: WHAT IT

WILL TAKE TO PLAN 5G NETWORK?In the wake of 5G, CP is faced with numerous challenges,some evolving from 4G such as HetNets, carrier ag-gregation, inter-cell interference coordination and CoMP,others that are characteristic of 5G, such as the C-RAN, D2D, M2M communication, mmWave and Mas-sive MIMO based deployments. Fig. 6 shows a typical 5Gdeployment consisting of a macro-cell, under-laid with aheterogeneous mix of small cells including: micro-cells,pico-cells, RS, remote radio heads (RRH), femtocells,CDSA, D-MUD and D2D. In this section, we discusshow the adaptation of these new technologies in 5G andbeyond may affect the CP paradigm

A. Planning with Cloud-RAN (C-RAN)Dense deployment of small cells requires centralisedcoordination to avoid inter-cell interference and provideintelligent resource allocation in response to spatio tem-porally varying traffic. The Cloud or Centralised RANarchitecture, is thus considered a prime enabler to ultra-dense networks as it allows the required coordination.Basically, the C-RAN consists of breaking of the tra-ditional eNB functions and migrating them towards thecentralised processor. As shown in Fig. 6, the centralisednode holds the base band functions, and is called the baseband unit (BBU). The BBU is then connected to a manylow complexity access point, which often only consist ofradio and analog/digital functions, called the remote radiohead (RRH). Hence, from a radio point of view, the C-RAN has solved the radio access bottleneck in a flexible,scalable and adaptive manner due to the ease of resource

allocation among RRHs and addition or relocation ofRRHs. Moreover, owing to their low complexity, RRHsare low cost, robust, and small in size, thus, result in thereduction of the CAPEX of deployment and the OPEXof maintenance and premises rent. Nonetheless, a newbottleneck is born with the C-RAN; that is the linkconnecting the RRH to the BBU, referred to as fronthaul.The emerging C-RAN presents a categoric shift in bothcoverage and capacity planning, while extending the CPtargets to the front haul. Coverage planning becomes cell-less, since adaptive and variable sets of RRHs would nowform virtual cells, replacing the traditional base stations.Capacity planning also evolves from being cell-centricto BBU-centric or User centric, consequently, improvingresource usage efficiency. As for the front haul, it hashenceforth become an integral part of the virtual C-RANcell, hence, the corresponding CP approach.1) CP with Radio Gain and Front-haul cost constraints inC-RAN: Authors in [140] look at the trade-off betweenthe radio gains and front haul cost for different levelsof function migration, otherwise referred to as functionalsplit. Essentially, the C-RAN CP requires joint planningof the radio sites and the front-haul, as in [141], whichlooks at finding the RRH locations with a passive opticalfiber network (PON) for the fronthaul. The topic ofthe paper is, thus, the infrastructure deployment andlayout planning problem under the C-RAN architecture.It is formulated as a generic integer linear programming(ILP) model which aims at minimising the deploymentcost, by identifying the locations of RRHs and opticalwavelength division multiplexers (WDM) and their cor-responding association relations, with the constraint ofsatisfying the coverage requirement. The optimisationframework proves to be solvable and scalable as validatedthrough various case studies. Moreover, the results showsignificant gains, when CoMP is used in the C-RANarchitecture, in terms of higher capacity and reliabilityat lower cost.2) CP with Joint Resource Management constraints inC-RAN: Authors in [142] look a novel framework forjoint resource management in a HetNet with multi-RATand C-RAN. The framework consists of categorizingvarious functionalities of the radio access and the fron-thaul (PON-based) depending on the time requirementto conduct the management actions. Self-organizationand cognitive capabilities are also incorporated in theframework, which could be applied to various phases ofthe network’s life such as planning, deployment, opti-mization, etcAuthors in [143] also analyze the system capacity ina C-RAN architecture, comparing two different CoMPoptions with factional frequency reuse (FFR). A multipleinput single output (single user) scenario is generatedusing joint transmission, and MIMO scenario (two users)is created with beamforming, both assuming two RRHs.The authors demonstrate an extra 6dB downlink capacitygain with coordinated beamforming, however, at the ex-pense of additional computational power for user pairing



17

Fig. 6. 5G networks are envisioned to be multi-layered and multi-RAT consisting of macrocells, micro-cells, pico-cells, and relays, with device to devicecommunication, mmWave, massive MIMO and cloud-RAN

and selection.3) CP with User-Centric Cells in C-RAN: Authors in[144] revamp the common understanding of cellularstructure by proposing a user-centric virtual cell, formedby the user at its center and a cluster of RRH around it.Tight cooperation between these cells is possible, allow-ing efficient power allocation. The system interference isfirst modeled based on the mean and variance, then theresults are applied to find the optimum cell radius thatwould maximize the downlink system capacity.

B. Planning in the Presence of M2M and IoTThe introduction of smart cities and the Internet ofThings (IoT) in which homes, smart vehicles, sensingsystems, and mundane objects are endowed with high-speed machine-to-machine (M2M) communication capa-bilities is seen as the major technological challenge forthe next decade [145]. While traditional M2M commu-nications has relied on short-range technologies such asBluetooth or ZigBee, moving toward large-scale M2Msmart cities requires broader interconnection and com-munications among machine type devices which is madepossible by enabling M2M communications over thereliable cellular network infrastructure [146]. However,realizing this vision is contingent upon transforming thecellular infrastructure into a scalable and efficient systemcapable of sustaining the diverse challenges of M2Mcommunications [145], [146], [147].

Consequently, novel cellular planning approaches are re-quired for networks with M2M services. According tothe authors knowledge, the most of recent work on CPdoes not account for M2M/IoT deployments. M2M/IoTservice service characteristics are typically different fromtraditional human-to-human (H2H) services; M2M/IoTservices are distinguished in most applications by lowcost, low mobility, delay tolerance (except urgent securityand health cases), large number of devices (e.g., upto 30,000 smart meters per cell), generally small andinfrequent data transmission, as defined in [148]. Thesevariations raise pertinent questions about cellular net-work planning with M2M/IoT services, and pose diversechallenges in accommodating both M2M/IoT and humanto human (H2H) traffic classes fairly and efficiently. Arecent study item by 3GPP [149] gives an insight onM2M/IoT device characteristics that are relevant to net-work planning, such as: single receive antenna, reducedtransmit power, reduced peak data rate of up to 1 Mbps,device noise figure of 9 dB, etc. In addition to the devicecharacteristic constraints, M2M/IoT mobility behaviorsand quality of service requirements vary greatly perM2M/IoT application. M2M/IoT over cellular networksallows to cost effectively and efficiently connect heteroge-neous M2M/IoT devices such as vehicles, smart meters,sensors, and surveillance apparatus, among others. How-ever, reaping the benefits of M2M/IoT deployment overcellular requires overcoming major technical challenges



18

0 1 2 3 4 50

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

-1 0 1 2 3 4 5 6-1

0

1

2

3

4

5

6

-1 0 1 2 3 4 5 6-1

0

1

2

3

4

5

6

-1 0 1 2 3 4 5 6-2

-1

0

1

2

3

4

5

6

Fig. 7. BS deployment (HetNet: 4 macro BSs and 64 small cell BSs) for a Gaussian user distribution over 25 km2 with a density of 40 users/km2. Upper left:Initial deployment. Upper right: Optimized deployment (Simulated Annealing). Lower left: Superposition of the two deployments. Lower right: deploymentswith the distribution of user terminals in the network (black ‘x”s).

in CP while taking into account architectural, operational,and economic perspectives [145], [150], [151], [152].1) Architecture level challenges: At the architecturallevel, M2M communications over cellular systems willsignificantly increase the heterogeneity of the wirelesslandscape in terms of device types and traffic classes.Modeling, analyzing, and managing such heterogeneousM2M systems and incorporating those models in CP thusbecomes essential [153]. On the one hand, planning ofnew nodes, such as M2M traffic aggregators or road sideunits must be deployed and integrated into the CP whichtraditionally has been focused on BS planning. On theother hand, expanding the network via new base stationsor optimized frequency planning must now account forscattered, large-scale, and diverse M2M traffic, that mustseamlessly co-exist with conventional cellular services.2) Operational challenges: From an operational point ofview, the introduction of M2M will dramatically increasethe amount of data circulating in the cellular network.This data explosion will naturally strain the alreadyresource-constrained infrastructure, thus introducing ma-jor challenges for resource management and optimization[145], [150], [151], [152], [148] that include: 1) manag-ing constrained resources (power, time, frequency) forlarge data volumes, 2) handling excessive M2M-servicedependent signaling that modern-day cellular systems arenot designed to sustain, 3) meeting heterogeneous QoSconstraints of diverse M2M services with little disrup-

tion to legacy H2H communications, 4) handling thehigh-speed mobility and dynamics incurred by vehicularM2M traffic, and 5) maintaining a self-organizing andcost-effective operation in a dense and heterogeneousnetwork environment. In addition to these constraints,planning of an M2M enabling cellular network will alsorequire consideration of features such as data aggregationfor sensor-based M2M traffic, direct vehicle-to-vehiclecommunications, and coordinated communication for dis-tributed M2M services.

3) Economics and business challenges: At the economiclevel, CP for M2M services requires new business modelsthat allow to seize the various opportunities broughtforward by M2M communications [153], [154]. One keyissue is to develop market models for inclusion in CP toanalyze the interactions between several key players inthe M2M domain that include mobile operators, M2Mproviders, and possibly governmental agencies [154]. Inaddition, pricing will constitute an important factor inCP for M2M. There is a need to develop and incorporatemodels for pricing mechanisms for M2M services. Thekey challenge here is that, pricing is largely intertwinedwith both network planning and resource management,as it is a key determinant of how and which cellularresources are being used by the different M2M providers.



19

C. CP in the Presence of D2DDevice-to-device (D2D) communications have been re-ceiving significant research attention recently, due to theirplanned incorporation in Release 12 of LTE-Advanced(LTE-A). D2D communications in LTE-A would allowa device to use the cellular spectrum in order to beconnected directly to another device. Consequently, atransfer of large data amounts (e.g. multimedia) can occurthrough a direct connection over short distances. Thisshort range (SR) D2D transfer permits to offload sometraffic from the cellular network, since it does not needto use the network itself. D2D communications can takeplace in one of three following modes:1) D2D terminals can use dedicated resources assignedto them by the cellular network.

2) They can reuse the same resources of the cellularnetwork.

3) They can form an underlay network [155], [156],[157].

In the following we discuss the CP challenges, introduction ofD2D puts forth.1) Fairness and Interference minimization: In an underlay

scenario the main challenge with D2D communication is tokeep the interference with the primary cellular network at tol-erable levels. In [155], a one-to-one reuse problem is adopted,where each D2D connection has to reuse a channel used by acellular connection. The solution minimizing the interferenceis obtained using the Hungarian Algorithm. A joint schedulingand resource allocation scheme is proposed in [156] for asimilar underlay scenario. The authors of [156] investigate atradeoff between system throughput and user fairness throughthe definition of a fairness coefficient. In [157], the interfer-ence between the D2D network and the cellular network iscontrolled by keeping a minimum distance between a cellulartransmitter and D2D receiver. Round Robin scheduling is usedto ensure fairness. However, in the approach of [157], an extraoverhead is incurred since devices need to report their locationinformation to the BS.2) Spatial Reuse: : Maximizing the spatial reuse while us-

ing D2D communications on dedicated or same channel issomething that has to be considered in CP. From networkperspective, small cells offer more aggressive spatial reuse.But with D2D in picture problem becomes twisted by thefact that small cells are more vulnerable to D2D interferencedue to close proximity to device and low power, comparedto macro cells. Therefore, D2D considerate CP for HetNetsis challenging problem which remains to be investigated. Forpure macro cell based networks reuse maximization problemis relatively more tractable. For example, a D2D resourceallocation scheme for maximizing spatial reuse is proposedin [158], where the BS allocates D2D channels in a relativelyslow time scale whereas the mobile terminals (MTs) involvedin a direct D2D link can adjust the modulation and codingscheme (MCS) level in a relatively fast time scale.3) CP with D2D and M2M Interplay Considerations:

Network planning with D2D considerations alone is an openproblem. This problem is further aggravated by the fact that

M2M and D2D can have opposing effects on the networkoperation: M2M adds additional traffic to be supported by thecellular network, whereas D2D communications could offloadsome traffic from the cellular BSs. Thus, the network planningprocess needs also to take into account the interplay betweenD2D and M2M when they coexist in future cellular networks.

D. CP with Control Data Separation Architecture (CDSA)An undesired byproduct of inevitable densification of future

network is that there will be a huge signaling overloadspecifically during scenarios such as mobility and handover,if conventional signaling procedures are used. As investigatedrecently [159], a better way to plan for next generation 5Gnetworks is to leverage CDSA. The main idea of the CDSAoriginates from the fact that only a small amount of signalingis required to enable ubiquitous coverage [160]. On the otherhand, data transmission and its related signaling are neededon demand only when there are active user equipment (UE).This calls for a two-layer RAN architecture with a logicalseparation between:

• Network access and data transmission functionalities.• Idle mode and active mode• Cell-specific/broadcast-type and UE-specific/unicast-typesignaling.

In CDSA, a continuous and reliable coverage layer will beprovided by control base station (CBS) at low frequencybands, where the large footprint ensures robust connectivityand mobility. The data plane (DP) is supported by flexible,adaptive, high capacity and energy efficient data base stations(DBSs) that provide data transmission along with the nec-essary signaling. As shown conceptually in Fig. 6, all UEsare anchored to the CBS, while active UEs are associatedwith both the CBS and the DBS in a dual connection mode[161]. CDSA offers a range of benefits such as better energyefficiency and system capacity and resource efficient supportfor mobility as well as M2M/IoT. However, the concept ofCDSA is still in early stages and specifying functionalitiesof each plane is not trivial and is an open research problem.Examples of recent work that investigate this problem include[162][159].Expectedly, CDSA will have a major impact on the way

cellular systems are planned. Specifically, it will expand thedimensions of the solution space of the CP by requiringplanning of two different nodes DBS and CBS. Additionally,the distinct requirements of coverage and capacity of bothDBS and CBS, delay between DBS and CBS, proximitybetween DBS and CBS, will impose new constraints on DBSand CBS location, power, coverage and capacity planning.

E. CP with mmWave based cellsPromise of mmWave stems from two factors: 1) abundance

of spectrum, 2) noise limited operation regime thanks to highpropagation loss and thus short range which opens the doorsfor even denser deployment. However, the very same blessingassociated with mmWave, i.e., a short range, is also a curse.Unlike sub 5GHz deployments where densification is a choice,



20

and level of densification can be tailored to meet capacitydemands as coverage is not a major constraint due to lowpropagation losses, mmWave based deployments have to beextremely dense to provide a barely acceptable level of contin-uous coverage. Several recent studies show [43] a maximumrange of 100-200m, in line of sight (LoS) conditions. Thismeans if mmWaves based small cells are adapted in futurenetworks they will have to be adapted as complimentary sourceof capacity, while primary source of coverage has to be ahigh frequency (HF) based deployment of macro and smallcells. One possibility to do so, as recently proposed in [159]is to exploit CDSA such that mmWave is used for DBS andHF spectrum is used for CBS. This immediately makes thepandora of CP challenges discussed in context of HetNetsas well as CDSA relevant to mmWave based CP. Additionalnew challenge will be incorporation of the fact that mmWaverequires LoS, and mmWave based cells might offer highlydirective antennas. This may require planning of DBS withthe consideration of cell less deployment where coverage willfollow users in the form of narrowly focused beams, as suchno fixed cell foot prints will exist. This will require a majorshift from traditional CP, where cell foot print and their abilityto cover the whole area of interest plays the pivotal role in CPproblem formulations and solution search.

F. CP with Massive MIMO based cellsThe major challenge in planning massive MIMO enabled

base stations stems from the fact that the large antenna arraygain complicates the max-RSS based cell association problemfurther in HetNets. As per [35], even when macro BS reducesits power to same level as that of small cell, the user has ahigher probability to still get connected to the macro due toa large gain of massive MIMO macrocell. As a result, thisgain can force the massive MIMO macrocell to carry mostof the data traffic in HetNets, resulting in a significant loadimbalance between the macrocells and small cell. The secondmain challenge in CP with massive MIMO is accommodationof two conflicting objectives:Two Conflicting Objective in CP with Massive MIMO:There are two opposing forces at work, while deploying

massive MIMO in HetNets. Massive MIMO lends its gainfrom channel diversity. However, in ultra-dense small celldeployment, small cells owing to their small coverage areamay result in large spatial correlation of the channels limitingdegrees of freedom available to Massive MIMO thereby un-dermining their gain. As networks becomes denser the numberof active users per cell will decrease and the need for massiveMIMO may decrease. Other factors such as cost, energyand backhaul need to be taken into account for association/offloading decisions.Therefore, planning of future networks have to strike a

balance that might exist in the two extremes of all macrocells with massive MIMO, and HetNets with massive MIMOonly on cells with size above a certain threshold. Investigationof this optimal cell size while taking into account the userdensity, number of antennas per site, channel types and TCOis a research problem that demands extensive study.

VIII. CONCLUSIONSA plethora of new technologies need to be incorporated in

future cellular networks to meet unprecedented traffic demandsand to provide newly conceived services such as IoT/M2M.These technologies include HetNets, CoMP, D2D, CDSA,mmWave and massive MIMO. The adaptation of these tech-nologies means that one of the oldest but still not fully maturedresearch area in cellular networks, i.e. cell planning needs amajor paradigm shift. This article serves as comprehensivereference to kick start the research in CP for 5G networks forboth academic and industry based researchers.To this end, in this article we provided timely analysis of

this paradigm shift needed in CP. We start with a tutorial ofCP to identify the input and outputs of typical CP problem andto characterize its computational complexity. We then provideconcise recap of past attempts on different variants of classicCP problem. This is followed by a analysis of recent devel-opments in CP to incorporate e.g., EE, traffic uncertainties andCoMP. We then provide an overview of recent advancementsmade in modelling tools to make CP problem more tractableand/or realistic. We then provide a comprehensive analysis ofchallenges that cellular industry still faces in planning HetNets,along with promising approaches to address these challenges.We conclude this article with a detailed discussion challengesand research opportunities in CP for 5G and beyond that stemfrom introduction of IoT/M2M, D2D, CDSA, mmWave andmassive MIMO.

ACKNOWLEDGMENTThis work was supported primarily by the Computer and

Network Systems Program of the National Science Founda-tion under Grant No. 1559483. Any Opinions, findings andconclusions or recommendations expressed in this material arethose of the author(s) and do not necessarily reflect those ofthe National Science Foundation.

REFERENCES[1] Z. Fluhr and E. Nussbaum, “Switching plan for a cellular mobile

telephone system,” IEEE Transactions on Communications, vol. 21,no. 11, pp. 1281 – 1286, Nov. 1973.

[2] J. Wells, “Cellular system design using the expansion cell layoutmethod,” IEEE Transactions on Vehicular Technology, vol. 33, no. 2,pp. 58 – 66, May 1984.

[3] A. Gamst, E.-G. Zinn, R. Beck, and R. Simon, “Cellular radio networkplanning,” IEEE Aerospace and Electronic Systems Magazine, vol. 1,no. 2, pp. 8 –11, Feb. 1986.

[4] S. Chia, “Design and optimisation for cellular access network,” Elec-tronics Communication Engineering Journal, vol. 8, no. 6, pp. 269–277, Dec 1996.

[5] M. Frullone, G. Riva, P. Grazioso, and G. Falciasecca, “Advancedplanning criteria for cellular systems,” IEEE Personal Communications,vol. 3, no. 6, pp. 10 –15, Dec 1996.

[6] E. Amaldi, A. Capone, and F. Malucelli, “Planning UMTS base stationlocation: optimization models with power control and algorithms,”IEEE Transactions on Wireless Communications, vol. 2, no. 5, pp. 939– 952, Sept. 2003.

[7] F. Perez-Fontan and J. Rabanos, “Educational cellular radio networkplanning software tool,” IEEE Transactions on Education, vol. 41,no. 3, pp. 203 –215, Aug. 1998.

[8] K. Lieska, E. Laitinen, and J. Lahteenmaki, “Radio coverage op-timization with genetic algorithms,” in The 9th IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Communications,vol. 1, Sept. 1998, pp. 318 –322.



21

[9] S. Dehghan, D. Lister, R. Owen, and P. Jones, “W-CDMA capacityand planning issues,” Electronics Communication Engineering Journal,vol. 12, no. 3, pp. 101 –118, Jun. 2000.

[10] A. Ahmad, “A CDMA network architecture using optimized sectoring,”IEEE Transactions on Vehicular Technology, vol. 51, no. 3, pp. 404–410, May 2002.

[11] A. Imran, A. Zoha, and A. Abu-Dayya, “Challenges in 5g: how toempower son with big data for enabling 5g,” IEEE Network, vol. 28,no. 6, pp. 27–33, Nov 2014.

[12] R. Akl, M. Hegde, M. Naraghi-Pour, and P. Min, “Multicell CDMAnetwork design,” IEEE Transactions on Vehicular Technology, vol. 50,no. 3, pp. 711 –722, May 2001.

[13] P. Gould, “Radio planning of third generation networks in urbanareas,” Third International Conference on 3G Mobile CommunicationTechnologies, vol. 2002, no. CP489, pp. 64–68, May 2002.

[14] S. Hanly and R. Mathar, “On the optimal base-station density forCDMA cellular networks,” IEEE Transactions on Communications,vol. 50, no. 8, pp. 1274 – 1281, Aug. 2002.

[15] S. Hurley, “Planning effective cellular mobile radio networks,” IEEETransactions on Vehicular Technology, vol. 51, no. 2, pp. 243 –253,Mar. 2002.

[16] J. Munyaneza, A. Kurien, and B. Van Wyk, “Optimization of an-tenna placement in 3G networks using genetic algorithms,” in ThirdInternational Conference on Broadband Communications, InformationTechnology Biomedical Applications, Nov. 2008, pp. 30 –37.

[17] C. Edwards, “The future is femto - [comms femtocells],” EngineeringTechnology, vol. 3, no. 15, pp. 70 –73, 6 2008.

[18] Befemto project: http://www.ict-befemto.eu/.[19] L.-C. Wang and C.-J. Yeh, “3-Cell network MIMO architectures with

sectorization and fractional frequency reuse,” IEEE Journal on SelectedAreas in Communications, vol. 29, no. 6, pp. 1185 –1199, Jun. 2011.

[20] “FP7 EU preject: EARTH (Energy Aware Radio and neTwork Tech-nologies), https://www.ict-earth.eu/.”

[21] O. Aliu, A. Imran, M. Imran, and B. Evans, “A survey of self organi-sation in future cellular networks,” IEEE Communications Surveys andTutorials, vol. 15, no. 1, pp. 336–361, First quarter 2013.

[22] M. Jaber, Z. Dawy, N. Akl, and E. Yaacoub, “Tutorial on lte/lte-acellular network dimensioning using iterative statistical analysis,” IEEECommunications Surveys Tutorials, vol. 18, no. 2, pp. 1355–1383,Secondquarter 2016.

[23] Z. Dawy, A. Husseini, E. Yaacoub, and L. Al-Kanj, “A wirelesscommunications laboratory on cellular network planning,” IEEE Trans-actions on Education, vol. 53, no. 4, pp. 653–661, Nov. 2010.

[24] E. Zitzler, L. Thiele, M. Laumanns, C. Fonseca, and V. da Fonseca,“Performance assessment of multiobjective optimizers: an analysis andreview,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2,pp. 117 – 132, Apr. 2003.

[25] Y. Wu and S. Pierre, “Base station positioning in third generationmobile networks,” in IEEE Canadian Conference on Electrical andComputer Engineering, CCECE, vol. 1, May 2003, pp. 31 – 34.

[26] A. Imran, M. A. Imran, and R. Tafazolli, “A novel self organizingframework for adaptive frequency reuse and deployment in futurecellular networks,” in Proc. IEEE 21st Int Personal Indoor and MobileRadio Communications Symp, (PIMRC’10), 2010, pp. 2354–2359.

[27] S. Jamaa, Z. Altman, J. Picard, and B. Fourestie, “Multi-objectivestrategies for automatic cell planning of UMTS networks,” in IEEE59th Vehicular Technology Conference, VTC-Spring, vol. 4, May 2004,pp. 2420 – 2424.

[28] C. Maple, L. Guo, and J. Zhang, “Parallel genetic algorithms for thirdgeneration mobile network planning,” in International Conference onParallel Computing in Electrical Engineering, PARELEC, Sept. 2004,pp. 229 – 236.

[29] F. Gordejuela-Sanchez, A. Juttner, and J. Zhang, “A multiobjectiveoptimization framework for IEEE 802.16e network design and perfor-mance analysis,” IEEE Journal on Selected Areas in Communications,vol. 27, no. 2, pp. 202 –216, Feb 2009.

[30] A. Al Rawi, B. Sharif, and C. Tsimenidis, “Pareto-metaheuristic multi-objective network optimization for OFDMA-based systems,” IEEE6th International Conference on Wireless and Mobile Computing,Networking and Communications (WiMob), pp. 331 –336, Oct. 2010.

[31] F. Gordejuela-Sanchez and J. Zhang, “LTE access network planningand optimization: A service-oriented and technology-specific perspec-tive,” in IEEE Global Telecommunications Conference, GLOBECOM,30 2009-Dec. 4 2009, pp. 1 –5.

[32] M. St-Hilaire, “Topological planning and design of UMTS mobilenetworks: a survey,” Wirel. Commun. Mob. Comput., vol. 9, no. 7,pp. 948–958, Jul. 2009.

[33] L. Ribeiro and L. DaSilva, “A framework for the dimensioning ofbroadband mobile networks supporting wireless internet services,”IEEE Wireless Communications, vol. 9, no. 3, pp. 6 – 13, Jun. 2002.

[34] K. Tutschku and P. Tran-Gia, “Spatial traffic estimation and character-ization for mobile communication network design,” IEEE Journal onSelected Areas in Communications, vol. 16, no. 5, pp. 804 –811, Jun.1998.

[35] K. Tutschku, “Demand-based radio network planning of cellular mobilecommunication systems,” in Seventeenth Annual Joint Conference ofthe IEEE Computer and Communications Societies, INFOCOM ’98,vol. 3, Mar. 1998, pp. 1054 –1061.

[36] R. Mathar and M. Schmeink, “Optimal base station positioning andchannel assignment for 3G mobile networks by integer programming,”Annals of Operations Research, vol. 107, Issue 1-4, pp. 225–236, 2001.

[37] R. Whitaker and S. Hurley, “Evolution of planning for wirelesscommunication systems,” in Proceedings of the 36th Annual HawaiiInternational Conference on System Sciences, Jan. 2003.

[38] R. Pattuelli and V. Zingarelli, “Precision of the estimation of areacoverage by planning tools in cellular systems,” IEEE Personal Com-munications, vol. 7, no. 3, pp. 50 –53, Jun. 2000.

[39] C. Takahashi, Z. Zhengqing Yun, M. Iskander, G. Poilasne, V. Pathak,and J. Fabrega, “Propagation-prediction and site-planning software forwireless communication systems,” IEEE Antennas and PropagationMagazine, vol. 49, no. 2, pp. 52 –60, Apr. 2007.

[40] U. Okumura, “Field strength and its variablity in UHF and VHF land-mobile radio service,” Rev. Elec. Commun. Lab., vol. 16, 1968.

[41] M. Hata, “Empirical formula for propagation loss in land mobile radioservices,” IEEE Transactions on Vehicular Technology, vol. 29, pp.317–325, 1980.

[42] “COST 231 final report, digital mobile radio towards future generationsystems,,” Available online: http:/www.lx.it.pt/cost231, 1999.

[43] T.S.Rappaport, S.Sun, R.Mayzus, H.Zhao, Y.Azar, K.Wang, G.N.Wong,J.K.Schulz, M.Samimi, and F.Gutierrez, “Millimeter wave mobile com-munication for 5g cellular: It will work !” in Proceedings of the IEEEAccess , 2013.

[44] R. Collmann, “Evaluation of methods for determining the mobile trafficdistribution in cellular radio networks,” IEEE Transactions on VehicularTechnology, vol. 50, no. 6, pp. 1629 –1635, Nov. 2001.

[45] M. Galota, C. Glasser, S. Reith, and H. Vollmer, “A polynomial-timeapproximation scheme for base station positioning in UMTS networks,”in Proceedings of the 5th international workshop on Discrete algo-rithms and methods for mobile computing and communications, ser.DIALM ’01. New York, NY, USA: ACM, 2001, pp. 52–59.

[46] E. Amaldi, A. Capone, F. Malucelli, and F. Signori, “UMTS radio plan-ning: optimizing base station configuration,” in IEEE 56th VehicularTechnology Conference,VTC-Fall., vol. 2, 2002, pp. 768 – 772.

[47] N. Weicker, G. Szabo, K. Weicker, and P. Widmayer, “Evolutionarymultiobjective optimization for base station transmitter placement withfrequency assignment,” IEEE Transactions on Evolutionary Computa-tion, vol. 7, no. 2, pp. 189 – 203, Apr. 2003.

[48] D. Tsilimantos, D. Kaklamani, and G. Tsoulos, “Particle swarm opti-mization for UMTS WCDMA network planning,” in 3rd InternationalSymposium onWireless Pervasive Computing, ISWPC, May 2008, pp.283 –287.

[49] R. Mathar and T. Niessen, “Optimum positioning of basestations for cellular radio networks,” Wireless Networks,vol. 6, no. 6, pp. 421–428, Dec. 2000. [Online]. Available:http://dx.doi.org/10.1023/A:1019263308849

[50] A. Eisenblatter and H.-F. Geerd, “Wireless network design: solution-oriented modeling and mathematical optimization,” IEEE WirelessCommunications, vol. 13, no. 6, pp. 8 –14, Dec. 2006.

[51] K. Guruprasad, “Generalized Voronoi partition: A new tool for optimalplacement of base stations,” in IEEE 5th International Conferenceon Advanced Networks and Telecommunication Systems (ANTS), Dec.2011, pp. 1 –3.

[52] A. Imran, E. Yaacoub, Z. Dawy, and A. Abu-Dayya, “A mathematicalmodeling approach and a novel solution for sector azimuth angleplanning,” in Proceedings of the IEEE Mediterranean ElectrotechnicalConference (MELECON), Beirut, Lebanon, Apr. 2014.

[53] M. Aldajani, “Convolution-based placement of wireless base stationsin urban environment,” IEEE Transactions on Vehicular Technology,vol. 57, no. 6, pp. 3843 –3848, Nov. 2008.

[54] N. Lev-Tov and D. Peleg, “Exact algorithms and approximationschemes for base station placement problems,” in Proceedings of the8th Scandinavian Workshop on Algorithm Theory, ser. SWAT ’02.London, UK: Springer-Verlag, 2002, pp. 90–99. [Online]. Available:http://dl.acm.org/citation.cfm?id=645901.672620



22

[55] A. Imran, M. Imran, and R. Tafazolli, “Relay station access linkspectral efficiency optimization through SO of Macro BS tilts,” IEEECommunications Letters, vol. 15, pp. 1326 – 1328, 2011.

[56] Y. Qi, M. Imran, and R. Tafazolli, “Energy-aware adaptive sectorisationin LTE systems,” in IEEE 22nd International Symposium on PersonalIndoor and Mobile Radio Communications (PIMRC), Sept. 2011, pp.2402 –2406.

[57] A. Imran, M. A. Imran, A.-u. Quddus, and R. Tafazolli, “Distributedspectral efficiency optimization at hotspots through self organisation ofBS tilts,” in IEEE GLOBECOM Workshops, Dec. 2011, pp. 570 –574.

[58] C. Dimopoulos and A. Zalzala, “Recent developments in evolutionarycomputation for manufacturing optimization: problems, solutions, andcomparisons,” IEEE Transactions on Computation, vol. 4, no. 2, pp.93 –113, Jul. 2000.

[59] E. Yaacoub and Z. Dawy, “LTE radio network planning with HetNets:BS placement optimization using simulated annealing,” in Proceedingsof the IEEE Mediterranean Electrotechnical Conference (MELECON),Beirut, Lebanon, Apr. 2014.

[60] H. P. Lin, R. T. Juang, D. B. Lin, C. Y. Ke, and Y. Wang, “Cell planningscheme for wcdma systems using genetic algorithm and measuredbackground noise floor,” IEE Proceedings - Communications, vol. 151,no. 6, pp. 595–600, Dec 2004.

[61] F. Gu, H. Liu, and M. Li, “Evolutionary algorithm for the radioplanning and coverage optimization of 3G cellular networks,” inInternational Conference on Computational Intelligence and Security,CIS ’09., vol. 2, Dec. 2009, pp. 109 –113.

[62] H. Yang, J. Wang, X. Song, Y. Yang, and M. Wang, “Wireless basestations planning based on GIS and genetic algorithms,” in 19thInternational Conference on Geoinformatics, Jun. 2011, pp. 1 –5.

[63] H. Elkamchouchi, H. Elragal, and M. Makar, “Cellular radio networkplanning using particle swarm optimization,” in Radio Science Confer-ence, 2007. NRSC 2007. National, Mar. 2007, pp. 1 –8.

[64] A. Awada, B. Wegmann, I. Viering, and A. Klein, “Optimizing the radionetwork parameters of the long term evolution system using Taguchi’smethod,” IEEE Transactions on Vehicular Technology, vol. 60, no. 8,pp. 3825 –3839, Oct. 2011.

[65] V. Berrocal-Plaza, M. Vega-Rodriguez, J. Gomez-Pulido, andJ. Sanchez-Perez, “Artificial bee bolony algorithm applied to WiMAXnetwork planning problem,” in 11th International Conference on In-telligent Systems Design and Applications (ISDA), Nov. 2011, pp. 504–509.

[66] C. Lee and H. Kang, “Cell planning with capacity expansion inmobile communications: a tabu search approach,” IEEE Transactionson Vehicular Technology, vol. 49, no. 5, pp. 1678 –1691, Sept. 2000.

[67] I. Toros and P. Fazekas, “An energy efficient cellular mobile networkplanning algorithm,” in IEEE 73rd Vehicular Technology Conference(VTC Spring), May 2011, pp. 1 –5.

[68] B. Chamaret, S. Josselin, P. Kuonen, M. Pizarroso, B. Salas-Manzanedo, S. Ubeda, and D. Wagner, “Radio network optimizationwith maximum independent set search,” in IEEE 47th Vehicular Tech-nology Conference, vol. 2, May. 1997, pp. 770 –774.

[69] Y. Wu and S. Pierre, “A new hybrid constraint-based approach for 3Gnetwork planning,” IEEE Communications Letters, vol. 8, no. 5, pp.277 – 279, May 2004.

[70] E. Amaldi, A. Capone, and F. Malucelli, “Optimizing base station sitingin UMTS networks,” in IEEE 53rd Vehicular Technology Conference,VTC Spring, vol. 4, 2001, pp. 2828 –2832.

[71] E. Amaldi, A. Capone, F. Malucelli, and F. Signori, “A mathematicalprogramming approach for w-cdma radio planning with uplink anddownlink constraints,” in Vehicular Technology Conference, 2003. VTC2003-Fall. 2003 IEEE 58th, vol. 2, Oct 2003, pp. 806–810 Vol.2.

[72] E. Amaldi, A. Capone, and F. Malucelli, “Optimizing UMTS radio cov-erage via base station configuration,” in The 13th IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Communications,vol. 1, Sept. 2002, pp. 315 – 319.

[73] E. Amaldi, A. Capone, F. Malucelli, and F. Signori, “Optimizationmodels and algorithms for downlink UMTS radio planning,” in IEEEWireless Communications and Networking, WCNC, vol. 2, Mar. 2003,pp. 827 –831.

[74] F. Athley, “On base station antenna beamwidth for sectorized WCDMAsystems,” in IEEE 64th Vehicular Technology Conference, VTC Fall,Sept. 2006, pp. 1 –5.

[75] S. Hurley, “Planning effective cellular mobile radio networks,” IEEETransactions on Vehicular Technology,, vol. 51, no. 2, pp. 243 –253,Mar. 2002.

[76] S. Thiel, P. Giuliani, L. Ibbetson, and D. Lister, “An automated UMTSsite selection tool,” in Third International Conference on 3G Mobile

Communication Technologies, (Conf. Publ. No. 489), May 2002, pp.69 – 73.

[77] D. Abusch-Magder, “Novel algorithms for reducing cell sites during atechnology upgrade and network overlay,” in IEEE Wireless Commu-nications and Networking Conference, vol. 3, Mar. 2005, pp. 1726 –1732.

[78] A. Molina, G. Athanasiadou, and A. Nix, “The automatic locationof base-stations for optimised cellular coverage: a new combinatorialapproach,” in IEEE 49th Vehicular Technology Conference, vol. 1, Jul.1999, pp. 606 –610.

[79] J. Zhang, L. Guo, and J. Wu, “An integrated approach for UTRANplanning and optimization,” in IEEE 59th Vehicular Technology Con-ference, VTC Spring, vol. 4, May 2004, pp. 2360 – 2364.

[80] S. Sohn and G.-S. Jo, “Optimization of base stations positioning inmobile networks,” in Proceedings of the 2006 international conferenceon Computational Science and Its Applications - Volume Part II, ser.ICCSA’06. Berlin, Heidelberg: Springer-Verlag, 2006, pp. 779–787.[Online]. Available: http://dx.doi.org/10.1007/11751588-81

[81] Y. H. Chew, A. Tan, B. S. Yeo, and C. Wang, “A cell planning approachin non-isotropic transmission medium with the capability of supportingcellular networks downsizing,” in IEEE 64th Vehicular TechnologyConference, VTC Fall, Sept. 2006, pp. 1 –5.

[82] T. Theodoros and V. Kostantinos, “WiMAX network planning andsystem’s performance evaluation,” in IEEE Wireless Communicationsand Networking Conference, WCNC, Mar. 2007, pp. 1948 –1953.

[83] S. Priem-Mendes, J. Gomez-Pulido, M. Vega-Rodriguez, A. Pereira,and J. Perez, “Fast wide area network design optimisation using differ-ential evolution,” in International Conference on Advanced EngineeringComputing and Applications in Sciences, ADVCOMP, Nov. 2007, pp.3 –10.

[84] Y. Yu, S. Murphy, and L. Murphy, “A clustering approach to planningbase station and relay station locations in IEEE 802.16j multi-hop relaynetworks,” in IEEE International Conference on Communications, ICC’08., May 2008, pp. 2586 –2591.

[85] L. Zeng, L. Wang, and C. Ding, “Site selection for wireless base stationbased on map partitioning,” in 4th International Conference on WirelessCommunications, Networking and Mobile Computing, WiCOM’08.,Oct. 2008, pp. 1 –4.

[86] A. Kamar, S. Nawaz, M. Patwary, M. Abdel-Maguid, and S.-U.-R.Qureshi, “Optimized algorithm for cellular network planning basedon terrain and demand analysis,” in 2nd International Conference onComputer Technology and Development (ICCTD), Nov. 2010, pp. 359–364.

[87] L. Hu, I. Kovacs, P. Mogensen, O. Klein, and W. Stormer, “Optimalnew site deployment algorithm for heterogeneous cellular networks,”in IEEE Vehicular Technology Conference (VTC Fall), Sept. 2011, pp.1 –5.

[88] A. Khalek, L. Al-Kanj, Z. Dawy, and G. Turkiyyah, “Site placementand site selection algorithms for UMTS radio planning with qualityconstraints,” in IEEE 17th International Conference on Telecommuni-cations (ICT), Apr. 2010, pp. 375 –381.

[89] A. Abdel Khalek, L. Al-Kanj, Z. Dawy, and G. Turkiyyah, “Optimiza-tion models and algorithms for joint Uplink/Downlink UMTS radionetwork planning with SIR-based power control,” IEEE Transactionson Vehicular Technology, vol. 60, no. 4, pp. 1612 –1625, May 2011.

[90] E. Oh and B. Krishnamachari, “Energy savings through dynamic basestation switching in cellular wireless access networks,” in IEEE GlobalTelecommunications Conference (GLOBECOM), Dec. 2010, pp. 1 –5.

[91] E. Oh, B. Krishnamachari, X. Liu, and Z. Niu, “Toward dynamicenergy-efficient operation of cellular network infrastructure,” IEEECommunications Magazine, vol. 49, no. 6, pp. 56 –61, Jun. 2011.

[92] P. Gonzalez-Brevis, J. Gondzio, Y. Fan, H. Poor, J. Thompson,I. Krikidis, and P.-J. Chung, “Base station location optimization forminimal energy consumption in wireless networks,” in IEEE 73rdVehicular Technology Conference (VTC Spring), May 2011, pp. 1 –5.

[93] B. Rengarajan, G. Rizzo, and M. Marsan, “Bounds on QoS-constrainedenergy savings in cellular access networks with sleep modes,” in 23rdInternational Teletraffic Congress (ITC), Sept. 2011, pp. 47 –54.

[94] M. Marsan, L. Chiaraviglio, D. Ciullo, and M. Meo, “Optimal energysavings in cellular access networks,” in IEEE International Conferenceon Communications Workshops, ICC, Jun. 2009, pp. 1 –5.

[95] Z. Niu, Y. Wu, J. Gong, and Z. Yang, “Cell zooming for cost-efficientgreen cellular networks,” IEEE Communications Magazine, vol. 48,no. 11, pp. 74 –79, Nov. 2010.

[96] F. Richter, G. Fettweis, M. Gruber, and O. Blume, “Micro basestations in load constrained cellular mobile radio networks,” in IEEE



23

21st International Symposium on Personal, Indoor and Mobile RadioCommunications Workshops (PIMRC Workshops), Sept. 2010, pp. 357–362.

[97] W. El-Beaino, A. M. El-Hajj, and Z. Dawy, “A proactive approachfor LTE radio network planning with green considerations,” in 19thInternational Conference on Telecommunications (ICT), Apr. 2012, pp.1 –5.

[98] M. Katsigiannis and H. Hammainen, “Energy and cost efficient radioaccess network deployments; Case Finland,” in 4th Joint IFIP Wirelessand Mobile Networking Conference (WMNC), Oct. 2011, pp. 1 –8.

[99] Y. Qi, M. Imran, and R. Tafazolli, “On the energy aware deploymentstrategy in cellular systems,” in IEEE 21st International Symposiumon Personal, Indoor and Mobile Radio Communications Workshops(PIMRC Workshops), Sept. 2010, pp. 363 –367.

[100] M. A. Marsan and M. Meo, “Energy efficient management oftwo cellular access networks,” SIGMETRICS Perform. Eval. Rev.,vol. 37, no. 4, pp. 69–73, Mar. 2010. [Online]. Available:http://doi.acm.org/10.1145/1773394.1773406

[101] S. Boiardi, A. Capone, and B. Sanso, “Planning for energy-awarewireless networks,” IEEE Communications Magazine, vol. 52, no. 2,pp. 156–162, Feb. 2014.

[102] A. Koster, M. Kutschka, and C. Raack, “Towards robust network designusing integer linear programming techniques,” in Proceedings of the 6thEURO-NF Conference on Next Generation Internet (NGI), pp. 1–8, Jun.2010.

[103] Y. H. Chew, R. Mo, and B. S. Yeo, “Inclusion of mobility in cellplanning: Multi-period joint optimization,” in IEEE 70th VehicularTechnology Conference Fall (VTC Fall), Sept. 2009, pp. 1 –4.

[104] S. Yang and F. A. Kuipers, “Traffic uncertainty models in networkplanning,” IEEE Communications Magazine, vol. 52, no. 2, pp. 172–177, Feb.2014.

[105] S. E. Terblanche, R. Wessaly, and J. M. Hattingh, “Lagrangian re-laxation as a solution approach to solving the survivable multi-hournetwork design problem,” in Proc. of the South African Telecommuni-ations and Networks Applications Conference - SATNAC, 2007.

[106] G. Oriolo, “Domination between traffic matrices,” Mathematics ofOperations Research,, vol. 33, pp. 91–96, 2008.

[107] W. Ben-Ameur and H. Kerivin, “New economical virtual privatenetworks,” Communications of the ACM, vol. 46, no. 6, pp. 69–73,Jun. 2003.

[108] N. G. Duffield, P. Goyal, A. G. Greenberg, P. P. Mishra, K. K.Ramakrishnan, and J. E. van der Merive, “A flexible model for resourcemanagement in virtual private networks,” in Proceedings of the ACMSIGCOMM, pp. 95–108, 1999.

[109] A. L. Soyster, “Convex programming with set-inclusive constraintsand applications to inexact linear programming,” Operations Research,vol. 21, pp. 1154–1157, 1973.

[110] T. Bauschert, C. Busing, F. DAndreagiovanni, A. M. C. A. Koster,M. Kutschka, and U. Steglich, “Network planning under demand un-certainty with robust optimization,” IEEE Communications Magazine,vol. 52, no. 2, pp. 178–185, Feb. 2014.

[111] G. Classen, A. Koster, and A. Schmeink, “Robust planning of greenwireless networks,” in 5th International Conference on NetworkGames, Control and Optimization (NetGCooP),, Oct. 2011, pp. 1 –5.

[112] I. Garcia, N. Kusashima, K. Sakaguchi, K. Araki, S. Kaneko,and Y. Kishi, “Impact of base station cooperation on cellplanning,” EURASIP Journal on Wireless Communications andNetworking, vol. 2010, no. 1, 2010. [Online]. Available:http://jwcn.eurasipjournals.com/content/2010/1/406749

[113] D. Cao, S. Zhou, C. Zhang, and Z. Niu, “Energy saving performancecomparison of coordinated multi-point transmission and wireless relay-ing,” in IEEE Global Telecommunications Conference (GLOBECOM),Dec. 2010, pp. 1 –5.

[114] Z. Niu, S. Zhou, Y. Hua, Q. Zhang, and D. Cao, “Energy-aware networkplanning for wireless cellular system with inter-cell cooperation,” IEEETransactions on Wireless Communications, vol. 11, no. 4, pp. 1412 –1423, Feb. 2012.

[115] Q. Zhang, “Bridging the gap between dynamic and static methods forcell planning,” IEEE Transactions on Vehicular Technology, vol. 50,no. 5, pp. 1224 –1230, Sep. 2001.

[116] R. Giuliano and F. Mazzenga, “Exponential effective SINR approx-imations for OFDM/OFDMA-based cellular system planning,” IEEETransactions on Wireless Communications, vol. 8, no. 9, pp. 4434 –4439, september 2009.

[117] K. Majewski, U. Turke, X. Huang, and B. Bonk, “Analytical cell loadassessment in OFDM radio networks,” in IEEE 18th International

Symposium on Personal, Indoor and Mobile Radio Communications,PIMRC, Sept. 2007, pp. 1 –5.

[118] I. Siomina and D. Yuan, “Analysis of cell load coupling for LTEnetwork planning and optimization,” IEEE Transactions on WirelessCommunications, vol. 11, no. 6, pp. 2287 –2297, Jun. 2012.

[119] I. Siomina, A. Furuskar, and G. Fodor, “A mathematical frameworkfor statistical QoS and capacity studies in OFDM networks,” in IEEE20th International Symposium on Personal, Indoor and Mobile RadioCommunications, Sept. 2009, pp. 2772 –2776.

[120] L. Hu, I. Z. Kovacs, P. Mogensen, O. Klein, and W. Stormer, “Optimalnew site deployment algorithm for heterogeneous cellular networks,” inVehicular Technology Conference (VTC Fall), 2011 IEEE, Sept 2011,pp. 1–5.

[121] F. J. Velez, M. del Camino Noguera, O. Holland, and H. Aghvami,“Fixed WiMAX profit maximisation with energy saving through relaysleep modes and cell zooming,” Journal of Green Engineering, vol. 1,no. 4, pp. 355–381, Jun. 2011.

[122] H. Tabassum, F. Yilmaz, Z. Dawy, and M.-S. Alouini, “A framework foruplink intercell interference modeling with channel-based scheduling,”IEEE Transactions on Wireless Communications, vol. 12, no. 1, pp.206–217, Jan. 2013.

[123] H. Tabassum, F. Yilmaz, Z. Dawy, and M. S. Alouini, “A statisticalmodel of uplink inter-cell interference with slow and fast power controlmechanisms,” IEEE Transactions on Communications, vol. 61, no. 9,pp. 3953–3966, September 2013.

[124] R. Taplin, D. Ryan, S. Allen, S. Hurley, and N. Thomas, “Algorithmsfor the automatic design of WiMAX networks,” in The InternationalConference on Next Generation Mobile Applications, Services andTechnologies, NGMAST, Sept. 2007, pp. 322 –327.

[125] M. Werner, P. Moberg, P. Skillermark, M. Naden, W. Warzanskyj,P. Jesus, and C. Silva, “Cost assessment and optimization methodsfor multi-node radio access networks,” in Vehicular Technology Con-ference, 2008. VTC Spring 2008. IEEE, May 2008, pp. 2601–2605.

[126] G. Micallef, P. Mogensen, H.-O. Scheck, and E. Lang, “Energy efficientevolution of mobile networks: Macro-only upgrades vs. a joint-picodeployment strategy,” in IEEE 73rd Vehicular Technology Conference(VTC Spring), May 2011, pp. 1 –5.

[127] W. El-Beaino, A. M. El-Hajj, and Z. Dawy, “A proactive approach forlte radio network planning with green considerations,” in Telecommu-nications (ICT), 2012 19th International Conference on, April 2012,pp. 1–5.

[128] J. G. Andrews, “Seven ways that hetnets are a cellular paradigm shift,”IEEE Communications Magazine, vol. 51, no. 3, pp. 136–144, March2013.

[129] X. Ge, H. Cheng, M. Guizani, and T. Han, “5g wireless backhaulnetworks: challenges and research advances,” IEEE Network, vol. 28,no. 6, pp. 6–11, Nov 2014.

[130] N. Bhushan, J. Li, D. Malladi, R. Gilmore, D. Brenner, A. Damn-janovic, R. T. Sukhavasi, C. Patel, and S. Geirhofer, “Network den-sification: the dominant theme for wireless evolution into 5g,” IEEECommunications Magazine, vol. 52, no. 2, pp. 82–89, February 2014.

[131] T. Han and N. Ansari, “On greening cellular networks via multicellcooperation,” IEEE Wireless Communications, vol. 20, no. 1, pp. 82–89, February 2013.

[132] S. Navaratnarajah, A. Saeed, M. Dianati, and M. A. Imran, “Energyefficiency in heterogeneous wireless access networks,” IEEE WirelessCommunications, vol. 20, no. 5, pp. 37–43, October 2013.

[133] G. Piro, M. Miozzo, G. Forte, N. Baldo, L. A. Grieco, G. Boggia, andP. Dini, “Hetnets powered by renewable energy sources: Sustainablenext-generation cellular networks,” IEEE Internet Computing, vol. 17,no. 1, pp. 32–39, Jan 2013.

[134] O. Onireti, A. Imran, M. Imran, and R. Tafazolli, “Energy efficientinter-frequency small cell discovery in heterogeneous networks,” IEEETransactions on Vehicular Technology, vol. PP, no. 99, pp. 1–1, 2015.

[135] I. Siomina and D. Yuan, “Optimization Approaches for Planning SmallCell Locations in Load-Coupled Heterogeneous LTE Networks,” inProceedings of IEEE Int. Symp. Personal, Indoor and Mobile RadioCommunications, PIMRC, pp. 2904–2908, Sept. 2013.

[136] A. Bou Saleh, O. Bulakci, J. Hmlinen, S. Redana, and B. Raaf,“Analysis of the impact of site planning on the performance of relaydeployments,” IEEE Transactions on Vehicular Technology, vol. 61,no. 7, pp. 3139 –3150, Sept. 2012.

[137] O. Bulakci, S. Redana, B. Raaf, and J. Hamalainen, “Performanceenhancement in LTE-Advanced relay networks via relay site planning,”in IEEE 71st Vehicular Technology Conference (VTC Spring), May2010, pp. 1 –5.



24

[138] W. Guo and T. O’Farrell, “Relay deployment in cellular networks:Planning and optimization,” IEEE Journal on Selected Areas in Com-munications, vol. 31, no. 8, pp. 1597 – 1606, Oct. 2013.

[139] J. Hoydis and M. Debbah, “Green, cost-effective, flexible, small cellnetworks,” IEEE ComSoc MMTC E-Letter Special Issue on “Multime-dia Over Femto Cells”, vol. 5, no. 5, Sept. 2010.

[140] A. Maeder, M. Lalam, A. de Domenico, E. Pateromichelakis,D. Wubben, J. Bartelt, R. Fritzsche, and P. Rost, “Towards a flexiblefunctional split for Cloud-RAN networks,” European Conference onNetworks and Communications (EuCNC), Jun. 2014.

[141] A. Zakrzewska, A. Avramova, H. Christiansen, Y. Yan, A. Checko,A. Dogadaev, S. Ruepp, M. Berger, and L. Dittmann, “A frame-work for joint optical-wireless resource management in multi-RAT,heterogeneous mobile networks,” in IEEE International Conference onCommunications Workshops (ICC), Jun. 2013, pp. 895–899.

[142] B. Lin, X. Pan, R. He, and S. Li, “Joint wireless-optical infrastructuredeployment and layout planning for Cloud-Radio Access Networks,”in International Wireless Communications and Mobile ComputingConference (IWCMC), Aug 2014, pp. 1027–1032.

[143] Y. He, E. Dutkiewicz, G. Fang, and J. Shi, “Downlink capacity incloud radio access networks with fractional frequency reuse,” in Inter-national Symposium on Wireless Personal Multimedia Communications(WPMC), Sept. 2014, pp. 424–428.

[144] Y. Zhang and Y. J. Zhang, “User-centric virtual cell design for CloudRadio Access Networks,” in IEEE 15th International Workshop onSignal Processing Advances in Wireless Communications (SPAWC),Jun. 2014, pp. 249–253.

[145] S.-Y. Lien, K.-C. Chen, and Y. Lin, “Toward ubiquitous massiveaccesses in 3GPP Machine-to-Machine communications,” IEEE Com-munications Magazine, vol. 49, no. 4, pp. 66–74, Apr. 2011.

[146] T. Taleb and A. Kunz, “Machine type communications in 3GPPnetworks: Potential, challenges, and solutions,” IEEE CommunicationsMagazine, vol. 50, no. 3, pp. 178–184, Mar. 2012.

[147] A. Ksentini, Y. Hadjadj-Aoul, and T. Taleb, “Cellular-based machine-to-machine: Overload control,” IEEE Network, vol. 26, no. 6, pp. 54–60, Jan. 2013.

[148] 3rd Generation Partnership Project (3GPP), “Service requirementsfor machine-type communications (MTC), Technical Specification TS22.368.”

[149] ——, “Study on provision of low-cost machine-type communications(MTC) user equipments (UEs) based on LTE, Tech. Rep. TR 36.888V12.0.0, 2013-06.”

[150] M. Hasan, E. Hossain, and D. Niyato, “Random access for machine-to-machine communication in LTE-Advanced networks: Issues andapproaches,” IEEE Communications Magazine, vol. 51, no. 6, pp. 86–93, Jun. 2013.

[151] K. Zheng, F. Hu, W. Wang, W. Xiang, and M. Dohler, “Radioresource allocation in LTE-Advanced cellular networks with M2Mcommunications,” IEEE Communications Magazine, vol. 50, no. 7, pp.184–192, Jul. 2012.

[152] C. Y. Ho and C.-Y. Huang, “Energy-saving massive access control andresource allocation schemes for M2M communications in OFDMAcellular networks,” IEEE Wireless Communications Letters, vol. 1,no. 3, pp. 209–212, Jun. 2012.

[153] R. Schneiderman, “Machine-to-machine connectivity market is boom-ing,” IEEE Signal Processing Magazine, vol. 30, no. 4, pp. 10–13, Jul.2013.

[154] V. Goncalves and P. Dobbelaere, “Market, standardization, and regu-lation development in Machine-to-Machine communications,” in Pro-ceedings of Int. Conf. on Software, Telecommunications, and ComputerNetworks (SoftCOM), Split, Croatia, Sept. 2012.

[155] C. Ma, J. Yue, H. Yu, H. Luo, W. Zhou, and X. Sun, “An interferencecoordination mechanism based on resource allocation for network con-trolled Device-to-Device communication,” in Proceedings of IEEE/CICICCC, Xi’an, China, Aug. 2013.

[156] F. Wang, L. Song, Z. Han, Q. Zhao, and X. Wang, “Joint scheduling andresource allocation for Device-to-Device underlay communication,” inProceedings of IEEE WCNC, Shanghai, China, Apr. 2013.

[157] H. Wang and X. Chu, “Distance-constrained resource-sharing criteriafor Device-to-Device communications underlaying cellular networks,”Electronics Letters, vol. 48, no. 9, pp. 528–530, 2012.

[158] D. H. Lee, K. W. Choi, W. S. Jeon, and D. G. Jeong, “Resource al-location scheme for Device-to-Device communication for maximizingspatial reuse,” in Proceedings of IEEE WCNC, Shanghai, China, Apr.2013.

[159] A. Mohamed, O. Onireti, M. A. Imran, A. Imran, and R. Tafazolli,“Control-data separation architecture for cellular radio access networks:

A survey and outlook,” IEEE Communications Surveys Tutorials,vol. 18, no. 1, pp. 446–465, Firstquarter 2016.

[160] A. Capone, A. F. dos Santos, I. Filippini, and B. Gloss, “Lookingbeyond green cellular networks,” in Wireless On-demand NetworkSystems and Services (WONS), 2012 9th Annual Conference on, Jan2012, pp. 127–130.

[161] H. Ishii, Y. Kishiyama, and H. Takahashi, “A novel architecture forlte-b :c-plane/u-plane split and phantom cell concept,” in 2012 IEEEGlobecom Workshops, Dec 2012, pp. 624–630.

[162] X. Xu, G. He, S. Zhang, Y. Chen, and S. Xu, “On functionalityseparation for green mobile networks: concept study over lte,” IEEECommunications Magazine, vol. 51, no. 5, pp. 82–90, May 2013.

Azar Taufique received his B.Sc. (Honors) degreein electrical engineering from University of Engi-neering and Technology (UET) Lahore,Pakistan andMasters in telecommunications engineering fromUniversity of Texas at Dallas (UTD) in 2007 and2010 respectively. He worked as a telecommunica-tions technical trainer teaching 4G LTE courses tomore than 2500+ professionals at Verizon, T-Mobile,Sprint, ATT etc. in North America. His telecommu-nications blog (www.TechTrained.com) is read byhundreds of ICT professionals weekly. Currently, he

is pursuing his Ph.D.at BSON lab (www.bsonlab.com) at the University ofOklahoma,Tulsa. His research interests are in mobility management and newarchitectures for mobility management support in 5G.

Mona Jaber (S14) received her B.E. degree incomputer and communications engineering fromthe American University of Beirut (AUB), Beirut,Lebanon, in 1996. She worked as a telecommunica-tion consultant in various international firms with afocus on radio design of cellular networks includingGSM, GPRS, UMTS, and HSPA. She received herM.E. in electrical and computer engineering fromAUB in 2014. Currently, she is pursuing her Ph.D.at the University of Surrey, 5G Innovation Centre(5GIC) researching on 5G backhaul solutions. Her

research interests are wireless communications, cellular technologies, back-haul and fronthaul solutions, and self-optimization techniques.



25

Ali Imran (M15) received the B.Sc. degree in Elec-trical Engineering from the University of Engineer-ing and Technology, Lahore, Pakistan, in 2005 andthe M.Sc. degree (with distinction) in mobile andsatellite communications and the Ph.D. degree fromthe University of Surrey, Guildford, U.K., in 2007and 2011, respectively. He is an Assistant Professorin Telecommunications at the University of Okla-homa, Tulsa, OK, USA where he is the foundingdirector of Big Data and Artificial Intelligence (AI)Enabled Self Organizing (BSON) Research Center

(www.bosnlab.com). He has been leading several multinational projects onSelf Organizing Cellular Networks (SON) such as QSON (www.qson.org),for which he has secured research grants of over 2 million USD in last fouryears as lead principal investigator. Currently he is leading two NSF fundedProjects on 5G SON amounting to over 750K USD. He has authored over40 peer-reviewed articles and presented a number of tutorials at internationalforums, such as the IEEE International Conference on Communications, theIEEE Wireless Communications and Networking Conference, the EuropeanWireless Conference, and the International Conference on Cognitive RadioOriented Wireless Networks, on his topics of interest. His research interestsinclude self-organizing networks, radio resource management, and big-dataanalytics. Dr. Imran is an Associate Fellow of the Higher Education Academy(AFHEA), U.K., and a member of the Advisory Board to the Special TechnicalCommunity on Big Data of the IEEE Computer Society.

Zaher Dawy received the B.E. degree in computerand communications engineering from the AmericanUniversity of Beirut (AUB), Beirut, Lebanon, in1998 and the M.E. and Dr.-Ing. degrees in com-munications engineering from Munich University ofTechnology (TUM), Munich, Germany, in 2000 and2004, respectively. Since 2004, he has been with theDepartment of Electrical and Computer Engineering,AUB, where he is currently a Professor. His researchand teaching interests include wireless communi-cations, cellular technologies, context-aware mobile

computing, mobile solutions for smart cities, computational biology, andbiomedical engineering. Dr. Dawy is Editor for IEEE Communications Sur-veys and Tutorials, IEEE Transactions on Communications, IEEE Transactionson Wireless Communications, and Elsevier Physical Communications. Hehas also served as Executive Editor for Wiley Transactions on EmergingTelecommunications Technologies (ETT) from 2011 to 2014. He receivedAbdul Hameed Shoman Award for Young Arab Researchers in 2012, IEEECommunications Society 2011 Outstanding Young Researcher Award inEurope, Middle East, and Africa Region, AUB Teaching Excellence Awardin 2008, Best Graduate Award from TUM in 2000, Youth and KnowledgeSiemens Scholarship for Distinguished Students in 1999, and DistinguishedGraduate Medal of Excellence from Hariri Foundation in 1998

Elias Yaacoub received the B.E. degree in ElectricalEngineering from the Lebanese University in 2002,the M.E. degree in Computer and CommunicationsEngineering from the American University of Beirut(AUB) in 2005, and the PhD degree in Electricaland Computer Engineering from AUB in 2010. Heworked as a Research Assistant in the AmericanUniversity of Beirut from 2004 to 2005, and in theMunich University of Technology in Spring 2005.From 2005 to 2007, he worked as a Telecommuni-cations Engineer with Dar Al-Handasah, Shair and

Partners. From November 2010 till December 2014, he worked as a ResearchScientist / RD Expert at the Qatar Mobility Innovations Center (QMIC).Afterwards, he joined Strategic Decisions Group (SDG) where he worked asa Consultant till February 2016. He is currently an Associate Professor at theArab Open University (AOU). His research interests include Wireless Com-munications, Resource Allocation in Wireless Networks, Intercell InterferenceMitigation Techniques, Antenna Theory, Sensor Networks, and Physical LayerSecurity..

planning wireless cellular networks of future: outlook ...1 planning wireless cellular networks of...

Documents