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Semi-Static Cell Differentiation And IntegrationWith Dynamic BBU-RRH Mapping In Cloud Radio

Access NetworkM.Khan, Student Member, IEEE, Zainab H. Fakhri, H.S. Al-Raweshidy, Senior Member, IEEE

Abstract—In this paper, a Self-Organising Cloud Radio AccessNetwork is proposed, which dynamically adapt to varying net-work capacity demands. A load prediction model is consideredfor provisioning and allocation of Base Band Units (BBUs) andRemote Radio Heads (RRHs). The density of active BBUs andRRHs is scaled based on the concept of cell differentiation andintegration (CDI) aiming efficient resource utilisation withoutsacrificing the overall QoS. A CDI algorithm is proposed inwhich a semi-static CDI and dynamic BBU-RRH mapping forload balancing are performed jointly. Network load balance isformulated as a linear integer-based optimisation problem withconstraints.The semi-static part of CDI algorithm selects properBBUs and RRHs for activation/deactivation after a fixed CDI cy-cle, and the dynamic part performs proper BBU to RRH mappingfor network load balancing aiming maximum Quality of Service(QoS) with minimum possible handovers. A Discrete ParticleSwarm Optimisation (DPSO) is developed as an EvolutionaryAlgorithm (EA) to solve network load balancing optimisationproblem. The performance of DPSO is tested based on twoproblem scenarios and compared to Genetic Algorithm (GA) andthe Exhaustive Search (ES) algorithm. The DPSO is observed todeliver optimum performance for small-scale networks and nearoptimum performance for large-scale networks. The DPSO hasless complexity and is much faster than GA and ES algorithms.Computational results of a CDI-enabled C-RAN demonstratesignificant throughput improvement compared to a fixed C-RAN,i.e., an average throughput increase of 45.53% and 42.102%, andan average blocked users reduction of 23.149%, and 20.903% isexperienced for Proportional Fair (PF) and Round Robin (RR)schedulers, respectively.

Index Terms - Base Band Unit (BBU), Cloud Radio AccessNetwork (C-RAN), Particle Swarm Optimisation (PSO), RemoteRadio Head (RRH), Self-Organising Network (SON).

I. INTRODUCTIONThe surging volume of mobile data traffic witnessed in the

recent years is triggered by the dramatic growth in smartmobile devices, diverse mobile internet enabled applications,and ever increasing wireless access demands. It is predictedthat the global mobile devices and connections will increaseup to 20 billion or even 50 billion by 2020 [1]. One promisingdirection is to densify the access networks (especially at datatraffic hot-spots) with small cells. However, network densifica-tion comes with even bigger challenges, i.e., the considerableincrease in capital (CAPEX) and operational (OPEX) expen-diture, a greater number of un necessary handovers amongsmall cells, traffic load imbalances, and under-utilised networkresources. Mobile Network Operators (MNOs) require inno-vative methods and solutions beyond traditional performanceupgrades to extract maximum returns on investment whilemaintaining high levels of network QoS.

In general, a network may not function at its best perfor-mance levels if its resources are utilised inefficiently. Networkresources are often under-utilised during unbalanced traffic sit-uations, particularly when some network cells may suffer fromheavy loads causing a high number of blocked users, whileothers remain lightly loaded with their resources underutilised.Therefore, it is crucial to achieving self-optimisation in thenetwork on varying traffic environment. Inter-cell optimisationis a critical optimisation problem in Self Organising Networks(SON) for the Third Generation Partnership Project (3GPP)[2], [3].

Moreover, SON architectures can be divided into threetypes - a) centralised, b) decentralised and, c) hybrid. Inthe decentralised and hybrid SON architectures, the SONalgorithm partially runs on the network management leveland partially in the network elements. Coordination of dif-ferent SON functions, possibly having conflicting goals andoperating on various time scales, is more challenging than inthe centralised architecture [4]. In the centralised structure,a central Network Management System (NMS) or a SONserver decides the network optimisation algorithms and theeNodeB parameter configuration [5]. The centralised SONarchitecture is more manageable regarding the implementationof SON algorithms compared to distributed and Hybrid SONarchitectures. It enables the SON algorithms to jointly optimisemultiple network parameters, therefore, allowing a globallytuned system. However, the centralised SON server in thisapproach requires strict latency and delay requirements regard-ing system KPIs and UE measurements for SON parameterupdates, which restricts the applicability of a purely centralisedSON architecture.

However, Cloud Radio Access Network (C-RAN) is apromising centralised network architecture that can supportsuper-dense small cells deployment. C-RAN is consideredto meet the challenges mentioned above and has attractedconsiderable attention by both academia and MNOs and isa key enabler of Next Generation Mobile Networks (5G) [6]–[8]. The C-RAN architecture consists of the following mainparts:

• Several BBUs aggregated into a BBU cloud/pool forcentralised management and processing.

• Distributed RRHs in a given geographical area.• The connection between BBUs and RRHs (also ref-

ered to as front-haul) via an optical transport network

Unlike conventional cellular networks, where the base sta-

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tions are not always in peak time and often work in idle stateswith their resources not fully utilised, in C-RAN, suitableresource allocation schemes can dynamically adjust the logicalconnection between BBUs and RRHs. It is necessary for asystem to optimise its resources according to varying trafficenvironments. In C-RAN the problem of resource wastage isovercome by dynamically allocating the shared and centralisedBBUs resources to the RRHs. Moreover, significant costand energy savings can be achieved by dynamically scalingthe BBUs concerning varying traffic caused by uneven userdistribution in the network [9]. C-RAN is feasible to realisethe coordinated control between multiple cells by centralisedmanagement.

Although the main features in SONs include self-configuration, self-optimisation, and self-healing. However,this paper emphasises on self-optimisation technique in C-RAN concerning network performance improvement. The pri-mary focus is to model a multi-objective optimisation problemalong with several other criteria necessary to tailor the opti-misation objective according to specific system requirements.C-RAN combined with Self-optimising ability can provideMNOs with a flexible network regarding network dimension-ing, adaptation to non-uniform traffic and efficient utilisationof network resources.. However, before a full commercial C-RAN deployment, several challenges need to be addressed.Firstly, the front-haul technology used must support enoughbandwidth for delivering delay sensitive signals (i.e., the 1ms physical layer processing requirement of LTE). Secondly,the proper BBU-RRH assignment in C-RAN to not onlysupport collaboration technology like Cooperative MultipointProcessing (CoMP) but also enabling load balancing in thenetwork. Moreover, significant energy savings can be achievedif the RRHs and BBUs are turned on/off in such a way thatthe QoS of the network is not degraded.

In this context, a two-stage design is proposed in thispaper for efficient resource utilisation in a self-optimised C-RAN with real time BBU-RRH mapping. Network resourcesare utilised based on the concept of Cell Differentiation andIntegration (CDI) which allows a cell(s) to split into multiplesmall cells and vice versa in response to a measured loadinformation in one or more cells in the network. CDI allowsC-RAN to adapt to varying capacity demands through resourceprovisioning and allocation. Resource provisioning not onlyresizes the number of BBUs in the pool to meet the fluctuatingtraffic demands but also scales the density of active RRHsrequired to serve a given geographical area. In the first stage,the optimum number of BBUs is computed to serve the loaddemand, and the RRHs are activated or deactivated based onthe concept of CDI to handle network traffic load. In thispaper, the number of BBUs required to serve the system loadat a given time is computed based on a prediction modelcalled Wiener process [10]. In the second phase, the properBBU-RRH mapping is identified to avoid unbalanced networkscenarios while maintaining high levels of QoS. The secondstage in this paper is modelled as an integer based linearoptimisation problem with constraints.

The rest of the paper is organised as follows: Section IIpresents a survey of related work; Section III presents the Self-

Optimising C-RAN framework; Section IV presents the systemmodel; Section V presents a model of load prediction and BBUestimation; Section VI illustrates the formulation for dynamicRRH-sector allocation problem. Section VII defines the CDIalgorithm. Computational results are discussed in Section VIII.Finally, the paper is concluded in Section IX.

II. RELATED WORK

Numerous studies and methods on self-optimisation havesuggested addressing the problem of load balancing in cellularnetworks via SON. When a traffic imbalance is detectedamong cells, operation parameters are autonomously adjustedsuch as antenna angle (Antenna tilt) [11] and/or handoverparameters [12] to reduce the coverage area to achieve Mo-bility Load Balancing (MLB) [13]. In MLB, the handoverthresholds are adjusted following traffic conditions whichresult in expansion or contraction of virtual transfer areasamong adjacent cells and thereby reducing or increasing usersin the cells. However, incorrect handover parameter adjustmentcan cause additional handovers in the network which oftenleads to handover ping-pongs/delays and radio link failures.Mobility Robustness Optimisation (MRO) [14] is a SONfunction which aims to eliminate link failures and reduceunnecessary handovers caused by incorrect handover parame-ters. Power adaptation for load balancing is another techniqueto effectively change the cell coverage area which in returnchanges the association of all users in the coverage area. InLTE, Cell Range Expansion (CRE) [15] is a technique whichallows Low Power Nodes (LPN) to expand their coverage areaand take in users from the Macro Cell. Usually, users associateto the cell which provides the strongest signal. However, inCRE users connect to the LPNs despite receiving the strongestsignal from the Macro cell. A comprehensive survey on self-organisation in future cellular networks, which includes adetailed description of the schemes mentioned above alongwith hybrid approaches and other existing SON load balancingmethods in the literature are provided in [16].

Moreover, the benefits of Artificial Intelligence (AI) tech-niques while designing load balancing SON algorithms areinevitable. Among numerous AI techniques, the Genetic Algo-rithm (GA) [17], [18] and Swarm intelligence [19] are the mostembraced learning algorithms inspired the process of geneevolution and the natural actions of swarms of ants, a shoal offish, a flock of birds etc, respectively.Many algorithms havebeen designed to mimic the behaviour of natural organisms,however, Particle Swarm Optimisation (PSO) [20] remains thebackbone of swarm intelligence on which all other algorithmsare built. Both GA and PSO are widely discussed in studiesrelated to network planning, interference management, routingand coverage optimisation problems [21]–[24].

On the other hand, a number of research studies on en-abling technologies for C-RAN exist. Here, some related stud-ies on BBU-RRH mapping along with RRH-UE associationare briefly described. In [25], the authors propose a cross-layer framework for downlink multi-hop C-RAN to improvethroughput performance by optimising both physical and net-work layer resources. Also, RRHs beamforming vectors, user

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RRH association, and network coding based routing are opti-mised in an overall design. In [26], the authors attempt to solvea joint RRH and precoding optimisation problem which aimsto minimise network power consumption in a MIMO baseduser-centric C-RAN. In line with this work, the authors of [27]propose a weighted minimum mean square error (WMMSE)approach to solving the network-wide beam-forming vectoroptimisation problem for RRH-UE clusters formation. TheBBU scheduling is then formulated as a bin packing problemfor energy efficient BBU utilisation in a heterogeneous C-RAN environment. A dynamic BBU-RRH mapping schemeis proposed in [28] using a borrow-and-lend approach inC-RAN. Overloaded BBUs switch their supported RRHs tounderutilised BBUs for a balanced network load and enhancedthroughput. The authors of [29] proposed a lightweight, scal-able framework that utilises optimal transmission strategiesvia BBU-RRH reconfiguration to cater dynamic user trafficprofiles. [30] describes the traffic adaptation and energy savingpotential of TDD-based heterogeneous C-RAN by adjustingthe logical connections between BBUs and RRHs. The authorsof [31] recently investigated an RRH clustering design andproposed a spectrum allocation genetic algorithm (SAGA) toimprove network QoS via efficient resource utilisation.

Regarding other related work, research initiatives are takento develop Network Function Virtualisation (NFV) and Soft-ware Defined Network (SDN) solutions for C-RAN [32]–[34].NFV is an architectural framework that provides a virtualisednetwork infrastructure, functions and NFV orchestrator forcontrol and management [35]. However, SDN is a conceptrelated to NFV. SDN decouples data and control plane toenable directly programmable control plane while abstract-ing underlying physical infrastructure from applications andservices [36]. Although SDN and NFV are not the primefocus of this paper, they are presented in this section forcompleteness of the C-RAN introduction. Moreover, [8], [37]provides a comprehensive survey on C-RAN and highlights thechallenges, advantages, and implementation issues regardingdifferent deployment scenarios. Also, an in-depth review of theprinciples, technologies and applications of C-RAN describinginnovative concepts regarding many physical layer, resourceallocation, and network challenges together with their potentialsolutions are highlighted in [38].

To sum up, the existing resource allocation mechanismsin C-RAN does not take full advantage of the concept ofcentralised BBU pool. This paper extends the scope of C-RANby introducing a concept of CDI with dynamic BBU-RRHmapping for load balancing and efficient resource utilisation.The system model in this article allows combining SON andC-RAN for a more centrally managed network operations. Theproposed model is suitable for the framework of softwaredefined front-haul with optical switching for C-RAN [29].However, this paper only focuses on the centralised-SONaspect of the structure.

III. SELF-OPTIMISING CLOUD RADIO ACCESS NETWORKFRAMEWORK

In this paper, the self-organising framework proposed inthe author’s previous work [39] is utilised. The framework is

applicable for short and long term dynamics of C-RAN andmaximises the overall QoS of the system while considering anetwork load balance. Network QoS is maximised based ondesired system KPIs. Note that, several performance indicators(KPIs) can be considered to measure the network QoS, so theframework is modelled as a general multi-objective optimi-sation problem including several criteria. Many other criteriamay be included to tailor other optimisation objectives subjectto specific operator policy requirements.

𝐒𝐰𝐢𝐭

𝐜𝐡 𝐜

𝐨𝐧𝐭𝐫

𝐨𝐥

…

𝐁𝐁𝐔 𝐜𝐥𝐨𝐮𝐝

𝐒𝐎𝐍 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐫

𝐊𝐏𝐈 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐞𝐝 𝐁𝐁𝐔𝐬

𝐒𝐰𝐢𝐭𝐜𝐡 − 𝐅𝐚𝐛𝐫𝐢𝐜

𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧

𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧

𝐂𝐮𝐫𝐫𝐞𝐧𝐭

𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

Fig. 1: Genetric concept of a Self organisation in C-RAN

Fig. 1 shows a generic concept of a self-organisation in C-RAN. The BBU cloud consists of aggregated BBUs pool and isconnected to the RRHs via an optical transport network whichmay include a switch fabric (including a network of switches,optical splitters, multiplexers) [40] and low latency, highbandwidth fibre optic links as shown in Fig. 1. Note that, thereare various possibilities of front-haul deployment in C-RAN[41]. The self-organisation concept is explained in phases asshown in Fig. 1, where a SON server/controller inside theBBU pool realises the self-organising concept. The observationand analysis phases are utilised to detect the performance ofcurrent network deployment (BBU-RRH configuration), andthen an optimal implementation is identified for performancecomparison. KPIs are used to monitor network status forcurrent and optimal deployment settings. Based on the chosenKPIs, an algorithm decides the best system configuration, andfinally, the new topology (BBU-RRH setting) is enforced inthe execution phase (if necessary). The primary objective ofSON server/controller in the proposed C-RAN architecture isas follows: (i) to compile required metric by discovering thestatus of each KPI and (ii) to produce a decision and enforce it.The BBUs feed the system KPIs to the main multi-objectivedecision-making algorithm hosted by SON server/controller.The weights or priority levels are then applied to each KPI fordecision making. The corresponding weight of a KPI definesits preference value and is set according to network operator’spreferences.

Fig. 2 provides a logical block diagram for a multiple objec-tive decision making performed by the SON server/controller.

4

…

…

System KPIs

Multi-Objective Decision

Making Algorithm

Operator’s

preferences

Criteria weight

selection

Decision

…

Fig. 2: Block diagram of multi-objective decision making logicfor SON server

The BBUs feed the system KPIs to the main multi-objectivedecision-making algorithm hosted by SON server/controller.The weights or priority levels are then applied to each KPI fordecision making. The corresponding weight of a KPI definesits preference value and is set according to network operator’spreferences.

IV. SYSTEM MODEL

A. C-RAN Architecture

A self-optimised C-RAN (SOCRAN) architecture is pre-sented in Fig. 3. The BBUs are decoupled from the RRH andmigrated to a centralised BBU-pool, whereas the RRHs withsimple RF transmission functions are left on the cell sites.A SON controller inside the BBU cloud monitors the BBU-pool resource utilisation as well as controls the front-haul.Each RRH is connected to the BBU pool via optical transportnetwork (front-haul). Fig. 3 shows that each cell is coveredby seven RRHs. In contrast, the same geographical area isserved by a single high-power base station at extreme lowload traffic conditions. As the traffic load reaches the resourcelimitation of the high-power base station, the geographical areadifferentiates into C equally sized small cells by activating theRRHs deployed. Furthermore, each of the C cells can furtherdifferentiate into c more small cells by activating the RRHsdeployed within the cell to accommodate capacity demands.The term cell and RRH are used interchangeably throughoutthis paper since it is assumed that an RRH can serve onlyone cell at a particular time t. Moreover, each RRH can beserved by only one BBU at a given time instance. The actualnumber of RRHs required in the network is determined bythe coverage area, users density, and other environment-relatedfactors, however, in this paper, both C and c are consideredto be seven as a reasonable example.

In this paper, the concept of CDI is supported by consideringthree tiers of RRHs deployment in the network as shown inFig. 4. Tier-3 RRH deployment imitates a single high-powerBase station serving a Macro cell as in traditional cellularsystems. Tier-2 and 1 represents a structure with universalfrequency reuse, where each cell is surrounded by a continuoustier of 6 + E and 6 × [1 + j] + E cells, respectively. WhereE represents the number of other external macro cells, and jaccounts for the level of differentiation, e.g., level 1 representsany one of the tier-2 cells further differentiated; level 2 showsany two of tier-2 cells are further differentiated and so on as

…

𝐈𝐧 − 𝐚𝐜𝐭𝐢𝐯𝐞 𝐑𝐑𝐇

𝐒𝐰

𝐢𝐭𝐜

𝐡 𝐜

𝐨𝐧

𝐭𝐫𝐨

𝐥

𝐁𝐁𝐔 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭

𝐁𝐁𝐔 𝐜𝐥𝐨𝐮𝐝

𝐒𝐎𝐍 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐥𝐞𝐫

𝐁𝐁𝐔

𝐎𝐩𝐭𝐢𝐜𝐚𝐥 𝐅𝐢𝐛𝐫𝐞

𝐀𝐜𝐭𝐢𝐯𝐞 𝐑𝐑𝐇

Fig. 3: Structure of a Cloud Radio Access Network representedas SON

shown in Fig. 4. A set Si = {RRHi1,RRHi2, ...,RRHic} ismaintained for each cell Ci in tier-2 RRH deployment, whichcontains a group of RRHs responsible for differentiating cellCi into c small cells provided that the sum of transmit powersof all RRHs covers Ci coverage area. The transmission powerof the upper tier RRHs is split between the lower tier RRHs(including the original RRH). The SON server monitors allBBUs in the pool for traffic information and is responsiblefor cell differentiation and integration with proper BBU-RRHconfigurations, whereas the optical switch is in charge ofrealising the settings via server commands.

B. System model constraints

This paper presents a centralised-SON architecture for C-RAN, a central server/controller collects reports about theBBUs and their users and informs the BBUs about the con-figuration along with switching the BBU-RRH configurationvia server commands. Fine time-scale operations that happenwithin a few milliseconds, such as user scheduling in theframe, are executed by the BBUs themselves since they cannotbe realised in the same report-configure-inform cycle dueto latency limitations posed by the interfaces to/from thecontroller. Since the complete deployment of C-RAN has topass through certain stages to extract its full potential [42], thefinal stage involves BBU resource pooling via NFV. In thissense, centralised SON can be seen as a direct extension of

5

Active High Power BS

Tier 1

(RRH Deployment structure with different

levels of cell differentiation)

Tier 2

(RRH Deployment)

structure)

v

Active RRH

Tier 3

(Single High Power BS deployment)

Level 1

Level 2

Level 3

Level 4

Level 5

Level 6

Level 7

Fig. 4: Cell differentiation and integration with multiple tiersof RRH deployment.

Software Define Networks control/data plane separation prin-ciple, where resources can be virtualised thereby leveragingfull benefits of C-RAN. The proposed System model allows formore efficient resource utilisation through centralised controlacross aggregated BBU resources. However, the model isconstrained in the following ways:• Since the SON server/controller is in charge of

monitoring the BBU-cloud, the whole network maycollapse in case of server/controller failure.

• Coarse time-scales may limit the optimisation pro-cess due to interface-latency between SON controllerand the BBUs, along with the front-haul latency.

• The front-haul must support enough bandwidth fordelivering delay sensitive signals, and the switchingelements used to effect the BBU-RRH configurationsmust not affect the sub-frames time scale (i.e., 1ms)

• The sub-frame processing delay on a link betweenRRHs and BBU should be kept below 1 ms, to meetHARQ requirements [42]

Potential solutions to the challenges/limitations mentionedabove are discussed in [43] and [44]. Furthermore, the strategicdeployment of RRHs to support the proposed concept may ormay not be feasible in real-time environments. The inclusionof random RRHs distribution with different cell sizes andshapes and the indefinite network extension in all directionsis a more realistic approach. However, such an approach givesrise several other challenges such as RRHs being located veryclose to each other, increased inter-cell interference at celledges, irregular cell shapes and sizes, and coverage holeswithin a geographical area among others. Such challenges

need to be addressed within the CDI algorithm if a randomRRH deployment is considered. Especially by considering allpossibilities during cell differentiation and integration.

C. Channel model

In this paper, Guaranteed Bit Rate (GBR) users with QoSrequirements are considered. The frequency reuse factor is 1,and the time-frequency resources are equal for all BBUs. Thebasic unit of time-frequency resources that can be allocatedto users is known as the Physical Resource Block (PRB).Let M and N represent the number of BBUs and RRHs inthe network, respectively, such that Kin represents the totalnumber of users in cell i served by RRH n. Each userreports Channel Quality Information (CQI) to its serving BBUevery two subframes (i.e., 2 milli-seconds) for proper PRBassignment. The channel model considered in this paper is acomposite fading channel which involves path-loss and bothsmall and large scale fading, given as:

Hkin = h∗kin lkin[AD−δkin

](1)

where h∗kin and lkin represent the small and large scale fadingchannel between the RRH n and user k in cell i, respectively.The small scale fading is assumed to be a Rayleigh randomvariables with a distribution envelop of zero-mean and unity-variance Gaussian process. AD−δkin reflects the path-loss be-tween RRH n and user k in cell i, where A is a constantwhich depends on the carrier frequency fc and Dkin is thedistance between user k and RRH n in cell i and a path-loss exponent of δ. The large scale fading is assumed to belognormal random variable with a standard deviation of 10dBand is typically modelled with a probability density functionof [45]:

ρ(l) =ζ√

2πσllexp

[− (10log10l − µl)

2

2σ2l

](2)

where ζ = 10/ln 10, and µl and σl are the mean and thestandard deviation of l, both expressed in decibels.

The instantaneous Signal-to-Interference-and-Noise-Ratio γbased on CQI received from user k in cell i served by RRHn at the pth PRB of subframe τ is expressed as

γkin,p(τ) =Hkin,p(τ)Pin,p(τ)

N0 +∑j∈C

∑a∈c,a6=n Hkja,p(τ)Pja,p(τ)

(3)

where Pin,p(τ) and Hkin,p(τ) are the transmit power and chan-nel gain between the serving RRH n of user k in cell i at pth

PRB of subframe τ . N0 is the power of Additive White Gaus-sian Noise per PRB and

∑j∈C

∑a∈c,a6=n Hkja,p

(τ)Pja,p(τ)represents the interference power from all other RRHs a incells j except the serving RRH n of user k on subframe τ incell i. The average spectral efficiency of a user k served byRRH n in cell i at any time instance t is given as

ϑkin(t) =1

RB.Nτ

∑τ∈[t−1,t]

∑p∈RB

log2 [1 + γkin,p(τ)]

(4)

6

where RB is the total number of PRBs assigned to a BBUand Nτ is the number of sub-frames in a load balancingcycle. We assume that the dynamic load balancing cycle is2 seconds (i.e., 2000 subframes in 1 cycle). To keep up withthe requirement throughput φk, the number of PRBs requiredby user kin at a time period t can be calculated by multiplyingthe achievable user throughput to the PRB bandwidth (i.e., 180KHz per PRB)

NkRB(t) =

⌈φk(t)

pBW.ϑkin(t)

⌉(5)

where pBW represents the bandwidth of a PRB and d.e isthe ceil function.

V. LOAD PREDICTION AND BBU ESTIMATION

This paper proposes a semi-static cell differentiation andintegration scheme, which determines the number of BBUsand RRHs to be activated/deactivated depending on loaddistribution across the network to accommodate capacity de-mands. The BBUs and RRHs needed to handle traffic load isdetermined at the end of each cell differentiation or integration(CDI) cycle. The CDI cycle is a constant time interval afterwhich a decision on required number of active BBUs andRRHs is made. A CDI cycle of 60 seconds is considered in thispaper. However, to avoid constant scaling of BBUs due to loadfluctuations, future load demand change is predicted to calcu-late the number of required BBUs. A decreasing predictedload might require scaling down the BBUs and integration ofmultiple cells into a single cell (i.e., from lower to higher tierRRH structure). However, an increasing predicted load mightscale up the BBUs and further differentiation of cells (i.e.,from higher to lower tier RRH structure).

A. Load Prediction Based on Wiener Processes

Let ηm(t) be the load of a BBU at time period (t), whichis represented as

ηm(t) =

∑Kk=1 Im,k(t)NkRB(t)

RB(6)

Where Im,k is a binary indicator such that Im,k = 1 ifuser k is served by BBUm. However, an important constraint∑Mm=1 Im,k = 1,∀k defines that each user k is served by only

one BBU at time period t. Note that, all BBUs are assignedthe same number of PRBs. Another important constraint is that∑Kk=1 Im,k(t)NkRB(t) ≤ RB,∀m, which states that the number

of PRBs assigned to users served by the same BBU shouldnot exceed the BBU PRB limitation. The total actual load onthe network at time t is represented as the aggregated load oneach active BBU at time t, which is given by

η(t) =

M∑m=1

ηm(t) (7)

The predicted total load on the network at time t + ∆t ismodelled as a stochastic process based on Wiener Processes.The idea is to utilise accurate records of current and past loadvalues to predict future load demands [10]. Let the total load

on the network at time t and t + ∆t is given by η(t) andη(t+ ∆t) respectively, then

η(t+ ∆t) = η(t) + ∆η(t) (8)

where ∆η(t) represents the load variation between time t andt+ ∆t and can be modelled as

∆η(t) = µ∆t+ εδ√

∆t (9)

where ε is standard normal random variable with zero meanand standard 1. ∆η(t) is a normally distributed randomvariable with mean µ∆t and standard deviation δ

√∆t. The µ

and δ in (9) are refered to as expected drift-rate and standarddeviation rate of ∆η(t) respectively, since for any arbitrarytime interval ∆t the mean µ∆t and standard deviation δ

√∆t

are directly calculated from µ and δ.The values of µ and δ in (9) can be estimated by taking

previous s sample values of η i.e., [t, t − τ ], [t − τ , t −2τ ], ..., [t − (s − 1)τ , t − sτ ]. Where τ is the sampling timeinterval. The estimator µ of µ and δ of δ are given as:

µ =

∑si=0 (η (t− iτ)− η (t− iτ − τ))

sτ(10)

δ =1√τ

√∑si=1 (η (t− iτ)− η (t− iτ − τ)− µτ)

2

s(11)

From (10) and (11), ∆η(t) in (9) is calculated and the totalnetwork load η(t + ∆t) in (8) is predicted for the next timet+ ∆t.

B. Number of BBUs required in the network

The required number of BBUs to serve the offered trafficload at a particular time t can be calculated using predictednetwork load η(t+ ∆t) and actual load η(t), such that:

No. of BBUs =

dη(t+ ∆t)e if η(t) ≤ η(t+ ∆t) < M|M| if η(t+ ∆t) > Mdη(t)e if η(t+ ∆t) < η(t) < M

(12)where M is the total number of BBUs in the BBU pool and thenotation d.e is the ceil function. Moreover, the load contributedby an active RRHn in the network is given by

ηRRHin(t) =

K∑k=1

Ik,in(t)NkRB(t) (13)

where Ik,in(t) is a binary variable which indicates the associ-ation of user k with RRH n of cell i at time t. Ik,in(t) equalsto 1 if user k is associated with RRH n at time t in cell i.

VI. DYNAMIC BBU-RRH CONFIGURATION ANDFORMULATION

For a SOCRAN architecture shown in Fig. 3, it is essentialto balance the network load amongst the active BBUs byproper BBU-RRH configuration. After each CDI cycle, thenetwork may reconfigure itself by scaling the BBUs and RRHswith respect to traffic load. However, during the process, theRRH to BBU mapping might not satisfy the QoS requirement.

7

Therefore, If the BBU-RRH configuration at time t is knownthen it is necessary to adjust the BBU-RRH configurationat time t + 1 to adaptively balance the variance in trafficdemands. Note that, the time between t and t + 1 is longerthan that of a subframe (i.e., one millisecond) and is calledthe load balancing cycle. A user location indicator vectoru = {u1, u2, ..., uK} is defined which shows users associationwith RRHs such that uk = {rin|rin ∈ Z+ : i, n = 1, 2, 3, ...C},where uk = rin if user k is associated with RRH n ofcell i. To indicate RRHs association with BBUs, a vectorr = {r11, r12, ..., rin} is defined, where rin ∈ {1, 2, ...,M}and rin = m indicates RRH n of cell Ci is being served byBBU m. Whereas, rin = 0 indicates that RRH n of cell Ciis not active. If the user location indicator vector u is given,then the problem is to identify the new RRH allocation vectorr.

Network performance determined by Key Performance Indi-cators (KPIs) indicates its QoS. Based on these KPIs, the SONserver identifies optimum BBU-RRH conguration by utilisingthe existing number of active BBUs and RRHs, to achievea highly stable network with highest achievable QoS withrespect to load demand. Following are the important KPIsconsidered for BBU-RRH mapping problem;

A. Key Performance Indicator for Load Fairness Index

In this paper a Jains fairness index ψ is monitored, whichdetermines the level of load balancing in the network at aparticular time and is evaluated by using the load distributionin all cells. The Jains fairness index [46] at time t can bedefined as

ψ(t) =

(∑Mm=1 ηm(t)

)2

|M|(∑M

m=1 η2m(t)

) (14)

where |M| is the required number of active BBUs computed in(12). The range of ψ is in interval [ 1

M , 1], with higher valuerepresenting a highly balanced load distribution amongst allactive BBUs. Therefore, maximising ψ is one of the objectivesof this work to achieve a highly balanced load in the C-RAN.

B. Key Performance Indicator for Network Throughput

To compute an optimal BBU-RRH setting with highersystem capacity, maximising network throughput is consideredas a second objective in this paper. The practical capacity ofuser k served by RRH n at in cell i at time t is given as

Ckin(t) =∑p∈Nk

RB

pBW.log2 (1 + aγkin,p(τ)) (15)

where pBW denotes the bandwidth per PRB (i.e., 180 KHz)and a denotes the bit error rate (BER) and is defined by a =−1.5/ln(5BER) and BER is set to 10−6. The overall networkthroughput at time t can be expressed as

ξ(t) =

M∑m=1

K∑k=1

Im,k(t)Ckin,p(t) (16)

where Im,k(t) notifies if the user k is being served by BBUm at time (t) i.e., Im,k = 1 if user k is served by BBU m.

Note that, serving a user k at a given sub-frame τ by BBUm depends on the scheduler employed. This paper considersProportional Fair (PF) AND Round Robin (RR) schedulingfor the optimisation process. Moreover, the overall throughputξ at time t is normalised before reusing it in the optimisationprocedure.

C. Key Performance Indicator for Handovers

Network transition to a new BBU-RRH configuration mayrequire significantly forced handovers. An increased numberof forced handovers in the system is undesirable and leads toperformance degradation. Allocating an RRH to a new BBUsat a particular time results in forced handovers of all usersassociated with the RRH. Since inter-BBU handovers not onlyinvolves BBUs but a signalling overhead between the ServingGateway (S-GW) and Mobility Management Entity (MME),therefore, it is desirable to achieve a new optimum BBU-RRHconfiguration with a minimum required handovers. A handoverindex h(t) is monitored as a third objective for load balancingproblem and is given as

h(t) =1

2

(∑Mm=1

∑Kk=1 |Im,k(t)− I◦m,k(t)|

K

)(17)

where I◦m,k(t) is a binary variable that indicates a user’s as-sociation in previous BBU-RRH configuration i.e., I◦m,k(t)=1,if user k is served by BBU m in previous BBU-RRH config-uration.

For proper BBU-RRH configuration, a QoS function isneeded which is the weighted combination of KPIs definedin (14), (16), and (17). The multiple objectives are combinedinto a single QoS objective function. This paper representsQoS as the following maximisation problem with constraints:

Max QoS(t) = αψ(t) + βξ(t)− (1− α− β)h(t)

s.t. C1 :

K∑k=1

Im,k(t)NkRB ≤ RB,∀m ∈ {1, 2, ...M}

C2 :

M∑m=1

Im,k(t) = 1,∀k ∈ {1, 2, ...,K}

(18)

Both α and β are control parameters of the QoS function. Themain objective is to maximise the QoS function.

VII. CELL DIFFERENTIATION AND INTEGRATION (CDI)ALGORITHM

According to the intuitive analysis above, a CDI algorithmis proposed in this section. The CDI algorithm executes propercell differentiation, and integration of a geographical area andprovides a balanced network by identifying correct BBU-RRH configuration. The block diagram of CDI algorithmis shown in Fig. 5. Network information is collected in thefirst step and analysed for proper cell differentiation andintegration. The information includes, user location indicatoru, load contributed by each RRH, BBU-RRH mapping vectorr, actual network load, predicted load, and the required number

8

of BBUs. The algorithm seeks to utilise the network resourcesefficiently by calculating the necessary number of BBUs andRRHs to serve capacity demands at the end of each CDI cycle.Apart from a single BBU required to serve load requirements,proper BBU-RRH configuration is adjusted at the end ofoptimisation step by comparing the analysed and optimisedQoS values. For the optimisation part of the algorithm, aDiscrete Particle Swarm Optimisation (DPSO) algorithm isdeveloped as an Evolutionary Algorithm (EA) to solve theBBU-RRH configuration problem and is explained in the nextsection. The optimisation process continues until the CDI cycleis completed. Note that, the CDI algorithm shown in Fig. 5 istriggered at the beginning of each CDI cycle.

The pseudo-codes for semi-static cell differentiation andintegration are given in Algorithm 1 and Algorithm 2, re-spectively.Both algorithms execute the scaling of RRHs andBBUs with respect to network load distribution. However, animportant consideration is the first association of RRHs tothe required number of BBUs during cell differentiation andintegration. Algorithm 3 and 4 are supporting algorithms forAlgorithm 1, and 2, respectively, which covers all possiblecases of initial BBU-RRH assignment during differentiationor integration along with cases where the number of BBUsare increased, decreased or remain unchanged. The initialBBU-RRH mapping is important for efficiently utilising theavailable BBU resources so as to prevent high blockingrate before a proper BBU-RRH mapping is identified in theoptimisation step. Therefore, the blocking rate of the networkat time t can be measured as

Blocking rate =

[1−

(∑Mm=1

∑Kk=1 Im,k(t)

)K

]× 100 (19)

where Im,k(t) as discussed earlier, is a binary indicator suchthat Im,k = 1, if user k is served by BBU m at time t. Notethat, users are served based on the choice of scheduler used bya BBU. Moreover, the amount of resource shortage (or PRBshortage) in the network based on users PRB demand can beestimated as follows

Resource Shortage =

M∑m=1

max[(ηm(t)− 1), 0

]× 100 (20)

where ηm(t) is the load on BBU m at time t defined in(6). Note that, the CDI algorithm triggers Algorithm 1 andAlgorithm 2 sequentially, i.e., Algorithm 2 is triggered imme-diately after the Algorithm 1 is executed. In the interest ofsimplicity and understanding, the CDI algorithm is separatedinto different pseudo-codes.

A. Discrete Particle Swarm Optimisation (DPSO)

Particle Swarm Optimisation (PSO) is a robust optimisationtechnique inspired by social behaviour of flocking organisms.PSO method uses Swarm Intelligence for solving global opti-misation problems [47]. PSO utilises a population (or swarm)of particles, where each particle represents a solution, namely

𝐍𝐨

𝐘𝐞𝐬

𝐘𝐞𝐬

𝐍𝐨 𝐘𝐞𝐬

𝐍𝐨 𝐈𝐬 𝐐𝐨𝐒 𝐨𝐟 𝐧𝐞𝐰 𝐁𝐁𝐔− 𝐑𝐑𝐇 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐞𝐭𝐭𝐞𝐫 𝐭𝐡𝐚𝐧

𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐁𝐁𝐔− 𝐑𝐑𝐇 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧?

𝐀𝐧𝐚𝐥𝐲𝐬𝐞 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐜𝐞𝐥𝐥 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝

𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝟏 𝐚𝐧𝐝 𝟐

𝐈𝐬 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐁𝐁𝐔 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐭𝐨 𝐬𝐞𝐫𝐯𝐞

𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐥𝐨𝐚𝐝 𝐝𝐞𝐦𝐚𝐧𝐝𝐬?

𝐀𝐧𝐚𝐥𝐲𝐬𝐞 𝐐𝐨𝐒 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐜𝐮𝐫𝐫𝐞𝐧𝐭 𝐁𝐁𝐔

− 𝐑𝐑𝐇 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧

𝐐𝐨𝐒 = 𝛂𝛙 𝐭 − 𝛃𝛈𝐚𝐯𝐞 𝐭 − 𝟏− 𝛂− 𝛃 𝐡 𝐭

𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐞 𝐐𝐨𝐒 𝐢𝐧 𝐂− 𝐑𝐀𝐍 𝐚𝐧𝐝 𝐟𝐢𝐧𝐝 𝐧𝐞𝐰 𝐁𝐁𝐔− 𝐑𝐑𝐇

𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐮𝐬𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝟑

𝐑𝐞− 𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞 𝐭𝐡𝐞 𝐧𝐞𝐭𝐰𝐨𝐫𝐤

𝐂𝐃𝐈 𝐜𝐲𝐜𝐥𝐞 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝?

𝐬𝐭𝐚𝐫𝐭 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐛𝐲 𝐜𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐧𝐠

−𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐥𝐨𝐚𝐝 𝛈 𝐭

−𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐍𝐨. 𝐨𝐟 𝐁𝐁𝐔𝐬

−𝐍𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐏𝐑𝐁𝐬 𝐩𝐞𝐫 𝐁𝐁𝐔 |𝐏𝐑𝐁|

−𝐔𝐬𝐞𝐫 𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 𝐢𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫 𝐮

−𝐁𝐁𝐔 − 𝐑𝐑𝐇 𝐦𝐚𝐩𝐩𝐢𝐧𝐠 𝐯𝐞𝐜𝐭𝐨𝐫 𝐫

𝐑𝐞−𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞 𝐭𝐡𝐞 𝐧𝐞𝐭𝐰𝐨𝐫𝐤

𝐄𝐧𝐝

Fig. 5: Block Diagram of CDI Algorithm for one CDI cycle

a BBU-RRH association vector r. As the QoS representedin (18) is considered as the main objective function (orfitness function), PSO seeks to maximise the QoS functionby finding the best solution vector {r11, r12, ..., rin}. PSOoperates on a group of particles (or solutions) to probe thesolution space in a random way with different velocities. Thevector {r11, r12, ..., rin} is viewed as the particle position in then-dimensional solution space while discovering the optimalsolution can be viewed as particles probing the solution tosearch for the optimum position. To direct the particles to theirbest fitness values, the velocity of each particle is changedstochastically at each iteration. The velocity update of eachparticle j depends on the historical best position experience(pbest) of the particle itself and the best position experienceof neighbouring particles, i.e., the global best position (gbest).Therefore, every particle in the swarm tends to direct itselftowards the best solution at each iteration. Since the solutionvector r is real-valued, the standard PSO algorithm can not beapplied directly to solve the discrete optimisation problem.Therefore, a Discrete PSO is developed to solve the QoSmaximisation problem defined in (18). The DPSO algorithmis described in Fig. 6 and the following steps:

Step 1: Generate initial population R0 with popula-tion/swarm size of |∆|. Where R0 consists of N-bit particles(BBU-RRH mapping solutions). Where N is taken accordingto the number of active RRHs in the network and the su-perscript 0 represents the initial iteration number I = 0. Thebest position of each particle pbest0j = r0

j , 1 ≤ j ≤ |∆| areinitialised with a random velocity of V0

j for each particle.Step 2: Calculate the fitness values for each particle (BBU-

RRH mapping solution) in the current swarm/population usingthe fitness function F defined as QoS in equation (18) and

9

Algorithm 1: Pseudo-code for Semi-static Cell Differen-tiation with number of BBU requirement

Input : Current network load η (t) from (7)Predicted load η (t+ ∆t) from (8)BBU-RRH mapping vector rRequired number of BBUs from (12)

1 if No. of active BBUs =1 then2 if η (t+ ∆t) ≥ |PRB| then3 -Activate required No. of BBUs4 -Differentiate cell into tier-2 RRH structure by

BBU-RRH mapping using Algorithm 35 -Update BBU-RRH mapping vector r6 for i=1 to C do7 -Select set Si8 -Compute ηRRHij

(t) from (13)9 if ηRRHi1

(t) > |PRB| then10 - R← Si {Add Si to R}11 -Diffentiate cell Ci according to

Algorithm 3.12 -Update BBU-RRH mapping vector r.13 end14 end15 else16 -No cell differentiation required.17 -Tier-3 RRH structure remains.18 end19 else20 if No. of active BBUs ≤ No. of required BBUs then21 if All RRHs deployed in the network are active

then22 -Activate the required No. of BBUs.23 -Update BBU-RRH mapping vector r24 else25 -Activate required number of BBUs26 for i= 1 to C do27 -Select set Si28 -Compute ηRRHi1(t) from (13)29 if ηRRHi1

(t) > |PRB| then30 - R← Si {Add Si to List R}31 -Differentiate cell Ci using Algorithm

332 -Update BBU-RRH mapping vector r33 end34 end35 end36 end37 end

identify the global best position achieved i.e., gbest0 =argmax1≤j≤|∆|

F(pbestIj).

Step 3: Update particle j position by updating its velocity.The velocity update equation is given as

vIj = wvI−1

j + c1ε1

(pbestI

j − xIj

)+ c2ε2(gbestI

j − xIj)

1 ≤ j ≤ |∆|(21)

𝐍𝐨 𝐘𝐞𝐬

𝐍𝐨 𝐘𝐞𝐬

𝐍𝐨

𝐘𝐞𝐬

𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐢𝐧𝐢𝐭𝐢𝐚𝐥 𝐒𝐰𝐚𝐫𝐦,

(𝐫𝟏,𝐈 𝐫𝟐,

𝐈 … , 𝐫|∆|𝐈 ), 𝐈 = 𝟎

𝐩𝐛𝐞𝐬𝐭𝐣𝐈 = 𝐫𝐣

𝐈

1 ≤ 𝒋 ≤ |∆|

𝐐𝐨𝐒 & 𝐊𝐏𝐈𝐬 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐟𝐨𝐫 𝐚𝐥𝐥 |∆| 𝐩𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬

(𝐁𝐁𝐔 − 𝐑𝐑𝐇 𝐌𝐚𝐩𝐩𝐢𝐧𝐠𝐬)

𝐠𝐛𝐞𝐬𝐭𝐈 = 𝐚𝐫𝐠𝐦𝐚𝐱1≤𝐣≤|∆|

𝐅(𝐩𝐛𝐞𝐬𝐭𝐣𝐈)

𝐯𝐣𝐈 = 𝑤𝐯𝐣

𝐈−1 + 𝐜1𝛆1(𝐩𝐛𝐞𝐬𝐭𝐣𝐈 − 𝐱𝐣

𝐈) + 𝐜2𝛆2(𝐠𝐛𝐞𝐬𝐭𝐈 − 𝐱𝐣𝐈)

𝐱𝐣𝐈+1 = 𝐱𝐣

𝐈 + 𝐯𝐣𝐈

𝐅𝐨𝐫 1 ≤ 𝐣 ≤ |∆|

𝐔𝐩𝐝𝐚𝐭𝐞 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐜𝐨𝐮𝐧𝐭𝐞𝐫

𝐈 = 𝐈 + 𝟏

𝐜𝐨𝐧𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐜𝐫𝐢𝐭𝐞𝐫𝐢𝐨𝐧

𝐬𝐚𝐭𝐢𝐬𝐟𝐢𝐞𝐝?

𝐄𝐧𝐝

𝐒𝐭𝐚𝐫𝐭

𝐅(𝐫𝐣𝐈) ≥ 𝐅(𝐫𝐣

𝐈−1) ?

𝐩𝐛𝐞𝐬𝐭𝐣𝐈 = 𝐫𝐣

𝐈 𝐩𝐛𝐞𝐬𝐭𝐣𝐈 = 𝐩𝐛𝐞𝐬𝐭𝐣

𝐈−1

𝐦𝐚𝐱1≤𝐣≤|∆|

𝐅(𝐩𝐛𝐞𝐬𝐭𝐣𝐈) ≥ 𝐅(𝐠𝐛𝐞𝐬𝐭𝐈−1) ?

𝐠𝐛𝐞𝐬𝐭𝐈 = 𝐚𝐫𝐠𝐦𝐚𝐱1≤𝐣≤|∆|

𝐅(𝐩𝐛𝐞𝐬𝐭𝐣𝐈) 𝐠𝐛𝐞𝐬𝐭𝐣

𝐈 = 𝐠𝐛𝐞𝐬𝐭𝐣𝐈−1

𝐐𝐨𝐒 & 𝐊𝐏𝐈𝐬 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐟𝐨𝐫 𝐚𝐥𝐥 |∆| 𝐮𝐩𝐝𝐚𝐭𝐞𝐝 𝐩𝐚𝐫𝐭𝐢𝐜𝐥𝐞𝐬

(𝐁𝐁𝐔 − 𝐑𝐑𝐇 𝐌𝐚𝐩𝐩𝐢𝐧𝐠𝐬)

1 ≤ 𝐣 ≤ |∆|

Fig. 6: Block Diagram of DPSO algorithm

where xIj is the current position of particle j in iteration I and

ε1, ε2 are random numbers chosen between the range [0− 1].Both c1 and c2 are acceleration constants that pulls the particletowards best position. Values in the range 0-5 are chosen for c1and c2. The inertial weight w represents the effect of precedingvelocity on the updated velocity. Larger and smaller value of ware used for global exploration and local search expedition inthe search-space, respectively. However, choosing an optimumvalue for w can assist a balanced proportion between globaland local exploration of the search space. Usually valuesbetween 0-1 are selected for w [48]. A value of 0.9 for wis selected in this paper. The new position of particle j for thenext iteration I + 1 will be:

xI+1j = xI

j + vIj (22)

Step 4: Update the iteration counter (I = I + 1). If theconvergence criteria is satisfied then end else go to step 5.

Step 5: Update particle j,s personal best position as

pbestIj =

{pbestI−1

j if F(rIj) ≤ F(pbestI−1)

rI−1 if F(rIj) > F(pbestI−1

j )(23)

Step 6: Update global best position achieved by:

gbestI =

argmax1≤j≤|∆|

F(pbestIj) if F(pbestI

j) > F(gbestI−1)

gbestI−1 otherwise(24)

Step 7: Repeat all steps starting from step 1.

VIII. COMPUTATIONAL RESULTS AND ANALYSIS

Before going to a more thorough analysis of the proposedCDI concept, the performance of DPSO is tested in BBU-RRHconfiguration problems.

10

Algorithm 2: Pseudo-code for Semi-static Cell Integrationwith number of BBU requirementInput : Current network load η (t) from (7)

Predicted load η (t+ ∆t) from (8)BBU-RRH mapping vector rRequired number of BBUs from (12)

1 if No. of active BBUs =1 then2 -No cell integration required.3 -A high-power BS serves the geographical area.4 else5 if No.of required BBUs=1 then6 -Integrate all cells into tier-3 RRH structure, i.e.,

a high power BS should serve the geographicalarea.

7 -Switch-off remaining BBUs.8 -Update BBU-RRH mapping vector r.9 else

10 for i=1 to C do11 -Select set Si12 for j=1 to end of Si do13 -Compute load ηRRHij

(t) from (13)14 -Sum=Sum+ηRRHij

(t)15 end16 if Sum ≤ PRB then17 -Integrate all cells by switching-off all

RRHs in set Si except RRHi1.18 -Offload RRHs to required number of

BBUs according to Algorithm 4.19 -Update BBU-RRH mapping vector r.20 end21 end22 -Run Algorithm 4

{Case of BBU reduction and no integration}23 end24 end

A. Performance of DPSO algorithm

The performance of DPSO is demonstrated over two dif-ferent problem scenarios, P1, P2, and compared with theknown Genetic algorithm (GA) [49] and ES algorithm. P1

network scenario consists of 5 active BBUs and 19 activeRRHs including two differentiated cells (Tier 1, level 2, RRHstructure). Whereas, P2 scenario includes 5 active BBUs and49 active RRHs (Tier 1, level 7, RRH structure). The aimis to analyse the performance DPSO optimisation algorithmfor small and large networks. User distribution within eachcell is uniform where 6 and 25 users are considered for non-dense and high dense cells, respectively. One fifth of theRRH supported cells are chosen randomly to have high-densityusers.

For Monte Carlo analysis, the DPSO, GA and ES algorithmsare repeated 50 times with 50 different initial BBU-RRHsettings for both problem scenarios and the results obtainedare averaged. The load fairness index, normalised networkthroughput, and handover index are represented in Figs 7b,8b and 8a, respectively, over 200 iterations for both P1 and

TABLE I: Computational Results for DPSO, GA and ES

P1 (19 RRH) P2 (49 RRH)

Quality of ServiceDPSO 0.599142 0.588970

GA 0.599142 0.568473ES 0.599142 0.592940

Load Fairness IndexDPSO 0.984797 0.971681

GA 0.984797 0.969936ES 0.984797 0.975680

Network throughputDPSO 0.958 0.9515

GA 0.958 0.941499ES 0.958 0.95598

Handover IndexDPSO 0.380957 0.387010

GA 0.380957 0.387021ES 0.380957 0.383659

Iterations

0 50 100 150 200

Qu

ali

ty

of S

erv

ice

(Q

oS

)

0.3

0.35

0.4

0.45

0.5

0.55

0.6

GA: 19 RRHs, 5 BBUs

DPSO: 19 RRHs, 5 BBUs

ES: 19 RRHs, 5 BBUs

G: 49 RRHs, 5 BBUs

DPSO: 49 RRHs, 5 BBUs

ES: 49 RRHs, 5 BBUs

(a)

Iterations

0 50 100 150 200

Lo

ad

Fa

irn

es

s I

nd

ex

0.7

0.75

0.8

0.85

0.9

0.95

1

GA: 19 RRHs, 5 BBUs

DPSO: 19 RRHs, 5 BBUs

ES: 19 RRHs, 5 BBUs

GA: 49 RRHs, 5 BBUs

DPSO: 49 RRHs, 5 BBUs

ES: 49 RRHs, 5 BBUs

(b)

Fig. 7: (a) Quality of Service and (b) Load Fairness Index forDPSO, GA, and ES

P2. The optimum values shown in the figures and Table.I, areachieved by exhaustively searching for all possible solutions(NM) using ES algorithm, which helps in demonstrating theimprovement in each iteration of the DPSO and GA algo-rithms. Note that, ES algorithm is independent of the numberof iterations.

Fig. 7a shows that both DPSO GA algorithm converges tothe optimum solution in P1 with a Convergence Rate (CR)of 0.970 and 0.8150, respectively. Where CR is defined asthe number of times, the DPSO finds a best or optimumsolution during the entire number of iterations. This impliesthat over 200 iterations, the optimum solution is achieved195 times by DPSO and 163 times by GA for P1. For P2,the CR of DPSO and GA algorithm is 0.2750 and 0.2550,respectively. However, the optimum solution is not achievedby both algorithms over 200 generations. DPSO algorithmachieves the best value 56 times whereas GA achieves the bestvalue 52 times i.e., after 145 iterations and 145×|∆| fitnessevaluations for DPSO, and after 149 iterations and 149×|∆|fitness evaluations for GA. Both DPSO and GA still achieve99.3012% and 95.8735% of the optimum value found by ESalgorithm after an enormous 549 (MN) fitness evaluations.

Fig. 7b shows that the DPSO and GA algorithm convergeto the optimum load fairness index value after 13th and 16th

iterations in P1. However, in P2, the optimum value can not be

11

found over 200 iterations. The best load fairness index valueachieved by DPSO is after 164 iterations and 164×|∆| fit-ness evaluations. Whereas 170 iterations and 170×|∆| fitnessevaluations by GA. Which is 99.59% and 99.4113% of theoptimum value found by ES algorithm. ES algorithm performs549 fitness evaluations to find the optimum value which is aconsiderable amount of fitness evaluations.

Figs 8b and 8a displays the convergence of DPSO andGA algorithms to the optimum value for normalised networkthroughput and handover index, respectively, for both P1 andP2 . In P1, the optimum value for handover index and networkthroughput is achieved after 38 and 10 iterations by DPSO andafter 41 and 13 iterations by GA, respectively. For P2, bothDPSO and GA could not find the optimum value over 200iterations. However, the best possible value achieved by DPSOand GA for network throughput is 99.537% and 98.49361% ofthe optimum value found by ES algorithm, respectively. Thebest value achieved for handover index by DPSO and GA are99.1343% and 99.1315% of the optimum value, respectively.The ES algorithm finds the optimum value after performing549 enormous fitness evaluations whereas the DPSO algorithmperforms 114×|∆| and 145×|∆| to find the best value fornetwork throughput and handover index, respectively. Notethat, the α and β control parameters in (18) are selected byperforming an exhaustive search (ES) algorithm to identify theoptimal BBU-RRH setting for P1. Both α and β values areorderly set to 0, 0.1, ..., 1 with a constraint α+β ≤ 1 as shownin Fig. 9. An optimal BBU-RRH setting is found using ESalgorithm for each pair of α and β. It is observed that settinga higher value for load fairness index (until α = 0.8) notonly reduces the resource shortage but also improves networkbalance. Setting values for α > 0.8 results into improperBBU-RRH mapping which implies that maximising networkload balance is overly considered compared to maximisingnetwork throughput and minimising handovers, resulting intoan increased resource shortage. This paper considers α = 0.8and β = 0.1 which means assigning a 10% weight to handoverminimisation.

B. Complexity comparison

The computation complexity of DPSO and GA algorithmcompared to ES algorithm is presented by comparing thenumber of fitness evaluations carried out by both algorithms.For ES algorithm, the computational complexity is O

(|N ||M |

)where N and M are the number of RRHs and BBUs, respec-tively. The required fitness evaluations for N RRHs and MBBUs in the network for ES algorithm is |N ||M |. However,the computational complexity of DPSO and GA is O (|∆|I).The number of fitness evaluations at each iteration of DPSOand GA algorithm depends on the swarm/population size |∆|.Therefore, the required fitness evaluations for DPSO are |∆I|.Note that, the ES algorithm finds the optimum value afterevaluating an enormous number of fitness functions which is|N||M|−|∆|I times more than the fitness evaluations of DPSOand GA at the Ith iteration/generation.

In P1, the optimum BBU-RRH configuration is achievedafter 37 × 2 × 102 (I × |∆|) fitness evaluations by GA

Iterations

0 50 100 150 200

Ha

nd

ov

er I

nd

ex

0.38

0.385

0.39

0.395

0.4

0.405

0.41

0.415

0.42

0.425

GA: 19 RRHs, 5 BBUs

DPSO: 19 RRHs, 5 BBUs

ES: 19 RRHs, 5 BBUs

GA: 49 RRHs, 5 BBUs

DPSO: 49 RRHs, 5 BBUs

ES: 49 RRHs, 5 BBUs

(a)

Iterations

0 50 100 150 200

No

rm

ali

se

d a

ve

ra

ge

ne

tw

ork

th

ro

ug

hp

ut

0.65

0.7

0.75

0.8

0.85

0.9

0.95

1

GA: 19 RRHs, 5 BBUs

DPSO: 19 RRHs, 5 BBUs

ES: 19 RRHs, 5 BBUs

GA: 49 RRHs, 5 BBUs

DPSO: 49 RRHs, 5 BBUs

ES: 49 RRHs, 5 BBUs

(b)

Fig. 8: (a) Average handovers and (b) average networkthroughput for DPSO, GA and ES.

00.1

0.20.3

0.40.5

0.6

α

0.70.8

0.91

10.9

0.80.7

β

0.60.5

0.40.3

0.20.1

0

15

0

5

10

Res

ou

rce

sho

rtag

e [%

]

Fig. 9: Resource shortage for different α and β.

and 5 × 2 × 102 (I × |∆|) whereas the ES algorithm findsthe optimum BBU-RRH configuration after 519 (|N||M|) fit-ness evaluations. The ES algorithm performs 1.9073 × 1016

(|N||M| − |∆|I) extra fitness function evaluations than thenumber of fitness evaluations performed at 5th iteration ofDPSO and 14.114×1016 more fitness function evaluations thanthe number of fitness evaluations performed at 37th generationof GA.

In. P2, the near optimum BBU-RRH configuration is foundafter 144 × 2 × 102 (I × |∆|) fitness evaluations by DPSOand after 148 × 2 × 102 (I × |∆|) fitness evaluations by GA.The ES however, performs 549 fitness evaluations to find theoptimum solution. Note that, the optimum solution found byES algorithm required 1.7764 × 1034 (|N||M| − |∆|I) morefitness evaluations than DPSO and GA, which is too enormous.

C. Performance analysis of CDI algorithm

To make the simulation more realistic, the user arrivals inFig. 10b follows a Poisson process with rate λ. However, dueto the dynamic spatial and temporal nature of user traffic,

12

Time [Hours]

00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:0000:00

Ra

te

fu

nc

tio

n,

f(t)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

(a)

Time [Hours]

00:0002:0004:0006:0008:0010:0012:0014:0016:0018:0020:0022:0000:00

Ne

tw

ork

Lo

ad

0

5

10

15

20

25

30

35

40

Initial load before CDI

Predicted load

Actual load after CDI

(b)

Fig. 10: (a) Rate function for time in-homogeneous userarrivals and (b) Actual vs Predicted network load with respectto time

the user arrival is modelled as a time-inhomogeneous pro-cess. This is achieved by multiplying the time-homogeneousPoisson process with traffic intensity parameter λ and the ratefunction f(t) shown in Fig. 10a. The rate function is unit-lessand reshapes the traffic from constant intensity to an analogoustime varying profile that reflects typical traffic patterns in areal cellular network. If users arrive in the system followinga Poisson process with intensity λ users/min, with a constantservice time of h (60 sec), then the number of users at timet is calculated as K(t) = χhf(t). Where χ ∼ Poiss (λ) isa random variable with mean λ (i.e., λ = 200). Moreover,different data rate requirements are assumed for end usersbased on 3GPP standard simulation parameters [50] i.e., 4-25kbps for audio, 32-384 kbps for video, 28.8 kbps for data, and60 kbps for real-time gaming services. A reliable prediction ofnetwork load is achieved if the collected load samples are 25 orlarger [51]. In this paper, network load prediction is performedevery ∆t = 60 sec. However, the actual network load issampled every τ = 2.4 sec. The initial load prediction startsafter a ’training period’ of 1 minute. This allows the predictionmodel to have enough first samples to estimate µ and δ for∆η. The CDI algorithm proposed in this paper determinesthe number of BBUs and RRHs required to handle trafficdemands. Fig. 11 shows an actual number of active BBUs andRRHs with respect to time, based on uniform user distributionand network load shown in Fig. 10b. The path- loss modelsfor micro and pico-user at a distance D from transmitter andfrequency 2GHz are given as PLmic(dB) = 34.53+38log10D,and PLpico(dB) = 140.7+36.7log10D [52], [53], respectively.It is considered that the RRHs support micro cells in tier-2RRH structure.

The RRH-BBU association vector r = {r11, r12, ..., rin}is maintained and updated after each CDI cycle only if thenumber of BBUs or RRHs are scaled and also if the BBU-RRH mapping is re-configured. Newly activated RRHs andBBUs in the network are mapped according to Algorithm 3

00:0

002

:00

04:0

006

:00

08:0

010

:00

12:0

014

:00

16:0

018

:00

20:0

022

:00

00:0

00

10

20

30

40

50

Active BBUsActive RRHs

Time[hours]

Nu

mb

er o

f act

ive

net

wo

rk r

eso

urc

es

Fig. 11: Number of active BBUs and RRHs with respect tonetwork load/time

and 4. In this paper, a maximum of 49 RRHs are deployedin the network to support semi-static cell differentiation andintegration. The BBU pool consists of 5 BBUs that can beactivated and deactivated according to load demand. The initialBBU-RRH mapping at the beginning of a CDI cycle mightdegrade the network QoS with unnecessarily blocked users.Therefore, dynamic BBU-RRH mapping is proposed as anoptimisation problem to identify proper BBU-RRH mapping.The CDI algorithm utilises DPSO to find optimum BBU-RRH configuration to overcome network QoS degradation andminimising unnecessarily blocked users.

For a more thorough analysis, the proposed CDI enabledC-RAN (CDI-CRAN) concept is compared to a fixed C-RANscenario (F-CRAN). The BBU cloud holds five BBUs in bothcases. However, the F-CRAN scenario does not support celldifferentiation or integration, and only 7 RRHs serves theentire macrocell coverage area. However, the dynamic BBU-RRH mapping is enabled in the F-CRAN scenario whichshows 57 possible BBU-RRH mapping solutions to choosefrom at the beginning of each CDI cycle. However, the numberof possible BBU-RRH mapping solutions for CDI scenario atthe start of each CDI cycle is MN, where M and N representsthe number of active BBUs and RRHs, respectively. Moreover,an increasing user arrival is considered in the network withrandom data rates requirement as explained earlier. However,a Monte-Carlo analysis is performed, where uniformly dis-tributed users are envisaged for each instance, and the averageof all distributions are taken into account regarding networkload, throughput, blocked users, and resource shortage analy-sis. Figs 12a and 12b shows the comparative performances re-garding average blocked users and average network throughputfor F-CRAN and CDI-CRAN with Proportional Fair (PF) andRound Robin (RR) scheduling techniques. The CDI algorithmincludes 2 phases, i.e., the cell integration/differentiation phaseand the BBU-RRH optimisation phase. The optimisation phasein both F-CRAN and CDI-C-RAN is tested with both GA andDPSO algorithm in the second phase and the results achievedare compared.

The simulation results demonstrate the advantage of usingCDI-CRAN instead of an F-CRAN setting. When an F-

13

Algorithm 3: Initial RRH association to active BBUsduring cell differentiation.Input : List A of newly activated BBUs

List R containing sets of RRHs supporting celldifferentiation

1 if A is not empty then2 for m=1 to No. of active BBUs do3 -Compute ηm(t) from (6)4 if ηm(t) ≤ lower limit then5 A← BBUm{Add BBUm to List A}6 end7 end8 I=1;9 while not the end of List R do

10 -Select Ith set from list R11 m = 1;12 for j=1 to end of set Si do13 if m> |A| then14 m = 115 end16 BBUm ← RRHij{Map RRHij to BBUm

except R1j}17 m = m+ 1;18 end19 I=I+1;20 end21 else22 for m=1 to No. of active BBUs do23 -Compute ηm(t) from (6)24 if lower limit ≤ ηm(t) ≤ Upper limit then25 A← BBUm{Add BBUm to A}26 end27 end28 if A is still empty then29 A ← All active BBUs30 end31 -Sort A in increasing order of BBU loads32 I=1;33 while not the end of List R do34 -Select Ith set from List R35 m = 1;36 for j=1 to end of set Si do37 if m> |A| then38 m=1;39 end40 BBUm ← RRHij{Map RRHij to BBUm

except RRH1j}41 end42 I=I+1;43 end44 end

CRAN is considered, the average blocked users in the networkare much higher with significantly lower average throughput,using any scheduling technique, as shown in Figs 12a and 12b,provided that the dynamic BBU-RRH mapping is enabled

Number of users

0 200 400 600 800 1000

Av

era

ge

blo

ck

ed

us

ers

0

100

200

300

400

500

600

700

800

900

1000

F-CRAN with GA(PF)

F-CRAN with DPSO(PF)

CDI-CRAN with GA(PF)

CDI-CRAN with DPSO(PF)

F-CRAN with GA(RR)

F-CRAN with DPSO(RR)

CDI-CRAN with GA(RR)

CDI-CRAN with DPSO(RR)

(a)

Number of users0 200 400 600 800 1000

Averag

e N

etw

ork T

hro

ug

hp

ut

×108

0

0.5

1

1.5

2

2.5 F-CRAN with GA(PF)

F-CRAN with DPSO(PF)

CDI-CRAN with GA(PF)

CDI-CRAN with DPSO(PF)

F-CRAN with GA(RR)

F-CRAN with DPSO(RR)

CDI-CRAN with GA(RR)

CDI-CRAN with DPSO(RR)

(b)

Fig. 12: (a) Average blocked users and (b) Average networkthroughput for fixed and CDI-enabled C-RAN.

in both scenarios. However, an interesting observation isthe significant drop in the averaged blocked users and thenecessary increase in average network throughput in CDI-CRAN compared to F-CRAN. This indicates that during celldifferentiation, an overloaded cell divides into multiple smallscells. This not only reduces the user to RRH distances butalso the PRB demands resulting from high SINR. Note that,cell differentiation increases the number of RRH interferers inthe network. However, RRHs served by the same BBU doesnot contribute to the overall interference experienced by usersserved by the same BBU.

From the results shown in Fig. 12b, it is observed thatthe average network throughput increases by 44.56% in theCDI-CRAN compared to F-CRAN, both enabled with GA (asan optimisation algorithm) and PF schedulers. Whereas withDPSO algorithm and PF schedulers, an increase of 45.53% isobserved. However, with RR schedulers the average networkthroughput increases by 40.68% and 42.102% in CDI-CRANwith GA and DPSO, respectively. Moreover, the averagethroughput difference between GA and DPSO algorithm ina CDI-CRAN, with PF and RR scheduling is 4.022% and4.12%, respectively.

Fig. 12a shows that with PF scheduling in both CDI-CRAN and F-CRAN, about 52.29% and 23.149% reductionin the average number of blocked users is observed in CDI-CRAN hosting GA and DPSO, respectively. However, withRR scheduler, the average blocked users decline seen in CDI-C-RAN compared to F-CRAN is 17.489% and 20.903% forGA and DPSO, respectively. Table.II shows that the averageresource shortage drastically decreases in the CDI CRAN,compared to F-CRAN provided that both F-CRAN and CDI-CRAN have an equal amount of available resources (i.e., 5BBUs, 5×100 PRBs). A 76.4% decrease in average PRBshortage is estimated with CDI-enabled C-RAN compared tofixed C-RAN for both GA and DPSO. Moreover, the overallblocking rate experienced during the course of simulation isalso highlighted in Table.II.

14

TABLE II: Comparison results for fixed and CDI-enabled C-RAN

BlockingRate[%]

ResourceShortage

GA DPSO GA DPSOPF RR PF RR

F-CRAN 35.99 81.66 35.34 81.10 16.44× 103 16.42× 103

CDI-CRAN 26.87 67.33 25.809 62.13 38.79× 102 38.47× 102

IX. CONCLUSION

The concept of cell differentiation and integration in C-RANis examined with an objective to utilise network resourcesefficiently without degrading the overall network QoS. A loadprediction model is considered for pro-active network scalingof BBUs and RRHs. Network load balance is maintainedvia proper BBU-RRH mapping and is formulated as a linearconstrained integer programming problem. In this regard,a CDI algorithm is developed for C-RAN and tested forcomparison with an F-CRAN setting. Computational resultsshow a significant increase in average network throughput anda noticeable decrease in average blocked users and averageresource (PRBs) shortage. The CDI algorithm hosts a DPSOalgorithm which is developed to find optimum BBU-RRHconfiguration dynamically. The performance of DPSO is testedand compared to GA and ES. Using two benchmark problems,the DPSO delivered noticeably faster convergence comparedto GA and ES, which makes the CDI algorithm more reliablefor a self-organised C-RAN.

REFERENCES

[1] B. Haberland, F. Derakhshan, H. Grob-Lipski, R. Klotsche, W. Rehm,P. Schefczik, and M. Soellner, “Radio base stations in the cloud,” BellLabs Technical Journal, vol. 18, no. 1, pp. 129–152, 2013.

[2] E. U. T. R. A. Network, “Self-configuring and self-optimizing network(SON) use cases and solutions,” Third Generation Partnership Project(3GPP) specification TR 36.902 V9.3.1, vol. 36, 2011.

[3] F. Ahmed, A. A. Dowhuszko, and O. Tirkkonen, “Network optimizationmethods for self-organization of future cellular networks: Models andalgorithms,” Self-Organized Mobile Communication Technologies andTechniques for Network Optimization, pp. 35–65, 2016.

[4] O. Østerbø and O. Grøndalen, “Benefits of Self-Organizing Networks(SON) for mobile operators,” Journal of Computer Networksand Communications, vol. 2012, 2012. [Online]. Available: http://dx.doi.org/10.1155/2012/862527

[5] L. Jorguseski, A. Pais, F. Gunnarsson, A. Centonza, and C. Willcock,“Self-organizing networks in 3GPP: standardization and future trends,”IEEE Communications Magazine, vol. 52, no. 12, pp. 28–34, December2014.

[6] I. Chih-Lin, C. Rowell, S. Han, Z. Xu, G. Li, and Z. Pan, “Toward greenand soft: a 5G perspective,” IEEE Communications Magazine, vol. 52,no. 2, pp. 66–73, 2014.

[7] C. Liu, L. Zhang, M. Zhu, J. Wang, L. Cheng, and G.-K. Chang, “Anovel multi-service small-cell cloud radio access network for mobilebackhaul and computing based on radio-over-fiber technologies,” Jour-nal of Lightwave Technology, vol. 31, no. 17, pp. 2869–2875, 2013.

[8] J. Wu, Z. Zhang, Y. Hong, and Y. Wen, “Cloud radio access network(C-RAN): a primer,” Network, IEEE, vol. 29, no. 1, pp. 35–41, Jan 2015.

[9] M. Khan, R. Alhumaima, and H. Al-Raweshidy, “Reducing energyconsumption by dynamic resource allocation in C-RAN,” in Networksand Communications (EuCNC), 2015 European Conference on. IEEE,2015, pp. 169–174.

[10] T. Zhang, E. van den Berg, J. Chennikara, P. Agrawal, J.-C. Chen,and T. Kodama, “Local predictive resource reservation for handoff inmultimedia wireless IP networks,” IEEE Journal on selected areas inCommunications, vol. 19, no. 10, pp. 1931–1941, 2001.

Algorithm 4: Initial RRH association to active BBUsduring cell integration.

Input : List A of No. of active BBUsNo. of required BBUsBBU-RRH mapping vector r

1 if No. of required BBUs < |A| then2 for m=1 to |A| do3 for i=1 to C do4 for j=1 to c do5 -Select RRHij from BBU-RRH vector r6 if RRHij = m then7 Zm ← RRHij8 {Zm is a List of RRHs handled by

BBUm}9 end

10 end11 end12 end13 -Sort List A in decreasing order of BBU loads14 for m=1 to end of List A do15 if m 6= |No. of required BBUs| then16 A← BBUm{A is a List of required BBUs}17 else18 B ← BBUm19 {B is a List of BBUs to be switched off}20 end21 end22 -Sort List A in increasing order of BBU loads23 for i=1 to end of List B do24 -Select ith BBU from List B25 -Select List Zm of the ith BBU26 m=1;27 for j=1 to end of Zm do28 if m> |A| then29 m=1;30 end31 -Select RRH at jth index in List Zm32 -Select BBU at mth index of List A33 -BBUm ← RRHj

34 m++35 end36 end37 -Switch off all BBUs in List B38 end

[11] N. Dandanov, H. Al-Shatri, A. Klein, and V. Poulkov, “Dynamic Self-Optimization of the Antenna Tilt for Best Trade-off Between Coverageand Capacity in Mobile Networks,” Wireless Personal Communications,vol. 92, no. 1, pp. 251–278, 2017.

[12] P. Muoz, R. Barco, and I. de la Bandera, “On the Potential of HandoverParameter Optimization for Self-Organizing Networks,” IEEE Transac-tions on Vehicular Technology, vol. 62, no. 5, pp. 1895–1905, Jun 2013.

[13] J. M. Ruiz-Avils, M. Toril, S. Luna-Ramrez, V. Buenestado, and M. A.Regueira, “Analysis of Limitations of Mobility Load Balancing in a LiveLTE System,” IEEE Wireless Communications Letters, vol. 4, no. 4, pp.417–420, Aug 2015.

[14] M. Huang and J. Chen, “A conflict avoidance scheme between mobilityload balancing and mobility robustness optimization in self-organizingnetworks,” Wireless Networks, pp. 1–11, 2016. [Online]. Available:

15

http://dx.doi.org/10.1007/s11276-016-1331-y[15] Y. Dhungana and C. Tellambura, “Multichannel Analysis of Cell Range

Expansion and Resource Partitioning in Two-Tier Heterogeneous Cellu-lar Networks,” IEEE Transactions on Wireless Communications, vol. 15,no. 3, pp. 2394–2406, March 2016.

[16] O. G. Aliu, A. Imran, M. A. Imran, and B. Evans, “A survey of selforganisation in future cellular networks,” IEEE Communications Surveys& Tutorials, vol. 15, no. 1, pp. 336–361, 2013.

[17] M. Srinivas and L. M. Patnaik, “Genetic algorithms: A survey,” com-puter, vol. 27, no. 6, pp. 17–26, 1994.

[18] A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P. N. Suganthan, andQ. Zhang, “Multiobjective evolutionary algorithms: A survey ofthe state of the art,” Swarm and Evolutionary Computation,vol. 1, no. 1, pp. 32 – 49, 2011. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2210650211000058

[19] Z. Zhang, K. Long, J. Wang, and F. Dressler, “On Swarm IntelligenceInspired Self-Organized Networking: Its Bionic Mechanisms, DesigningPrinciples and Optimization Approaches,” IEEE Communications Sur-veys Tutorials, vol. 16, no. 1, pp. 513–537, First 2014.

[20] J. Kennedy, “Particle Swarm Optimization,” in Encyclopedia of machinelearning. Springer, 2011, pp. 760–766.

[21] A. Vasilakos, M. P. Saltouros, A. F. Atlassis, and W. Pedrycz, “Opti-mizing QoS routing in hierarchical ATM networks using computationalintelligence techniques,” IEEE Transactions on Systems, Man, andCybernetics, Part C (Applications and Reviews), vol. 33, no. 3, pp. 297–312, Aug 2003.

[22] N. Phan, T. Bui, H. Jiang, P. Li, Z. Pan, and N. Liu, “Coverageoptimization of LTE networks based on antenna tilt adjusting consideringnetwork load,” China Communications, vol. 14, no. 5, pp. 48–58, May2017.

[23] Y. Adediran, H. Lasisi, and O. Okedere, “Interference managementtechniques in cellular networks: A review,” Cogent Engineering, vol. 4,no. 1, p. 1294133, 2017.

[24] R. Sachan, T. J. Choi, and C. W. Ahn, “A Genetic Algorithm withLocation Intelligence Method for Energy Optimization in 5G WirelessNetworks,” Discrete Dynamics in Nature and Society, vol. 2016, 2016.

[25] L. Liu and W. Yu, “Cross-Layer Design for Downlink Multihop CloudRadio Access Networks With Network Coding,” IEEE Transactions onSignal Processing, vol. 65, no. 7, pp. 1728–1740, April 2017.

[26] C. PAN, H. Zhu, N. Gomes, and J. Wang, “Joint Precoding and RRHselection for User-centric Green MIMO C-RAN,” IEEE Transactions onWireless Communications, vol. PP, no. 99, pp. 1–1, 2017.

[27] K. Wang, W. Zhou, and S. Mao, “On Joint BBU/RRH ResourceAllocation in Heterogeneous Cloud-RANs,” IEEE Internet of ThingsJournal, vol. PP, no. 99, pp. 1–1, 2017.

[28] Y. S. Chen, W. L. Chiang, and M. C. Shih, “A Dynamic BBU-RRHMapping Scheme Using Borrow-and-Lend Approach in Cloud RadioAccess Networks,” IEEE Systems Journal, vol. PP, no. 99, pp. 1–12,2017.

[29] K. Sundaresan, M. Y. Arslan, S. Singh, S. Rangarajan, and S. V.Krishnamurthy, “FluidNet: a flexible cloud-based radio access networkfor small cells,” IEEE/ACM Transactions on Networking, vol. 24, no. 2,pp. 915–928, 2016.

[30] Z. Yu, K. Wang, H. Ji, X. Li, and H. Zhang, “Dynamic resourceallocation in TDD-based heterogeneous cloud radio access networks,”China Communications, vol. 13, no. 6, pp. 1–11, June 2016.

[31] K. Lin, W. Wang, Y. Zhang, and L. Peng, “Green Spectrum Assign-ment in Secure Cloud Radio Network with Cluster Formation,” IEEETransactions on Sustainable Computing, vol. PP, no. 99, pp. 1–1, 2017.

[32] W. He, J. Gong, X. Su, J. Zeng, X. Xu, and L. Xiao, “SDN-EnabledC-RAN? An Intelligent Radio Access Network Architecture,” in NewAdvances in Information Systems and Technologies. Springer, 2016,pp. 311–316.

[33] C. Yang, Z. Chen, B. Xia, and J. Wang, “When ICN meets C-RAN forHetNets: an SDN approach,” IEEE Communications Magazine, vol. 53,no. 11, pp. 118–125, November 2015.

[34] O. Simeone, A. Maeder, M. Peng, O. Sahin, and W. Yu, “Cloud radioaccess network: Virtualizing wireless access for dense heterogeneoussystems,” Journal of Communications and Networks, vol. 18, no. 2, pp.135–149, April 2016.

[35] J. G. Herrera and J. F. Botero, “Resource Allocation in NFV: AComprehensive Survey,” IEEE Transactions on Network and ServiceManagement, vol. 13, no. 3, pp. 518–532, Sept 2016.

[36] R. Cziva, S. Jout, D. Stapleton, F. P. Tso, and D. P. Pezaros, “SDN-Based Virtual Machine Management for Cloud Data Centers,” IEEETransactions on Network and Service Management, vol. 13, no. 2, pp.212–225, June 2016.

[37] M. Peng, Y. Sun, X. Li, Z. Mao, and C. Wang, “Recent advances incloud radio access networks: System architectures, key techniques, andopen issues,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3,pp. 2282–2308, 2016.

[38] T. Q. Quek, M. Peng, O. Simeone, and W. Yu, Cloud radio accessnetworks: Principles, technologies, and applications. CambridgeUniversity Press, 2017.

[39] M. Khan, R. S. Alhumaima, and H. S. Al-Raweshidy, “QoS-AwareDynamic RRH Allocation in a Self-Optimised Cloud Radio Access Net-work with RRH Proximity Constraint,” IEEE Transactions on Networkand Service Management, vol. PP, no. 99, pp. 1–1, 2017.

[40] K. Sundaresan, M. Y. Arslan, S. Singh, S. Rangarajan, and S. V.Krishnamurthy, “FluidNet: a flexible cloud-based radio access networkfor small cells,” IEEE/ACM Transactions on Networking, vol. 24, no. 2,pp. 915–928, 2016.

[41] M. Peng, C. Wang, V. Lau, and H. V. Poor, “Fronthaul-constrainedcloud radio access networks: Insights and challenges,” IEEE WirelessCommunications, vol. 22, no. 2, pp. 152–160, 2015.

[42] A. Checko, H. L. Christiansen, Y. Yan, L. Scolari, G. Kardaras, M. S.Berger, and L. Dittmann, “Cloud RAN for Mobile Networks; A Tech-nology Overview,” IEEE Communications Surveys Tutorials, vol. 17,no. 1, pp. 405–426, Firstquarter 2015.

[43] M. Y. Arslan, K. Sundaresan, and S. Rangarajan, “Software-definednetworking in cellular radio access networks: potential and challenges,”IEEE Communications Magazine, vol. 53, no. 1, pp. 150–156, January2015.

[44] K. Tsagkaris, G. Poulios, P. Demestichas, A. Tall, Z. Altman, andC. Destre, “An open framework for programmable, self-managed radioaccess networks,” IEEE Communications Magazine, vol. 53, no. 7, pp.154–161, 2015.

[45] M. K. Simon and M.-S. Alouini, Digital communication over fadingchannels. John Wiley & Sons, 2005, vol. 95.

[46] S. Sheikh, R. Wolhuter, and H. A. Engelbrecht, “An Adaptive Con-gestion Control and Fairness Scheduling Strategy for Wireless MeshNetworks,” in Computational Intelligence, 2015 IEEE Symposium Serieson. IEEE, 2015, pp. 1174–1181.

[47] A. Kaveh, “Particle swarm optimization,” in Advances in MetaheuristicAlgorithms for Optimal Design of Structures. Springer, 2017, pp. 11–43.

[48] J. Vesterstrom and R. Thomsen, “A comparative study of differentialevolution, particle swarm optimization, and evolutionary algorithms onnumerical benchmark problems,” in Evolutionary Computation, 2004.CEC2004. Congress on, vol. 2. IEEE, 2004, pp. 1980–1987.

[49] M. Srinivas and L. M. Patnaik, “Genetic algorithms: a survey,” Com-puter, vol. 27, no. 6, pp. 17–26, June 1994.

[50] 3GPP, “Technical Specification Group Services and System As-pects;Services and service capabilities (Release 13) ,” 3rd GenerationPartnership Project (3GPP), TS V13.0.0, Dec. 2015.

[51] T. Zhang, E. van den Berg, J. Chennikara, P. Agrawal, J.-C. Chen,and T. Kodama, “Local predictive resource reservation for handoff inmultimedia wireless IP networks,” IEEE Journal on Selected Areas inCommunications, vol. 19, no. 10, pp. 1931–1941, Oct 2001.

[52] 3GPP, “Technical Specification Group Radio Access Network;Spatialchannel model for Multiple Input Multiple Output (MIMO) simulations(Release 13),” 3rd Generation Partnership Project (3GPP), TR V13.1.0,Dec. 2016.

[53] ——, “Technical Specification Group Radio Access Network;EvolvedUniversal Terrestrial Radio Access (E-UTRA); Further advancement forE-UTRA physical layer aspects (Release 9) ,” 3rd Generation PartnershipProject (3GPP), TS V9.1.0, Dec. 2016.