Mobile Netw ApplDOI 10.1007/s11036-011-0293-7

Self Organized Network Management Functionsfor Energy Efficient Cellular Urban Infrastructures

Konstantinos Samdanis · Tarik Taleb ·Dirk Kutscher · Marcus Brunner

© Springer Science+Business Media, LLC 2011

Abstract Energy efficiency is a significant requirementfor the design and management of mobile networksand has recently gained substantial attention from bothnetwork operators and the research community. Thegeneral concept of energy saving management aimsto match the capacity offered by operators to theactual demand at given times and geographic areas.This paper introduces the notion of energy partition,an association of powered-on and powered-off BSs todeliver network-level energy saving. It then elaborateshow such concept is applied to perform energy re-configuration to flexibly re-act to load variations en-couraging none or minimal extra energy consumption.A simulation-based study evaluates the performanceof the proposed algorithms under different networktopologies and traffic conditions, highlights the benefitsand drawbacks, and provides recommendations for de-ployment scenarios.

Keywords 3GPP · energy saving management(ESM) · SON · network management · LTE

K. Samdanis (B) · T. Taleb · D. Kutscher · M. BrunnerNEC Europe Ltd., Kurfürsten-Anlage 36,69115 Heidelberg, Germanye-mail: samdanis@neclab.eu

T. Talebe-mail: taleb@neclab.eu

D. Kutschere-mail: kutscher@neclab.eu

M. Brunnere-mail: brunner@neclab.eu

1 Introduction

Energy saving in Information and CommunicationTechnology (ICT) is gaining sustainable attentiondue to environmental and Operational expenditure(OPEX) reasons. ICT is responsible for nearly 2%of CO2 emissions [1], consuming 60 billion kilowatthours, an amount which corresponds to nearly 0.5%of the global electricity consumption [2]. Consideringthe sector of wireless communications the main powerexpenditures, accounting 80% of the total energy, areassociated with the radio access network and with theoperations of Base Stations (BSs) [2]. This energy costis expected to rise dramatically along with the increasein mobile data forecasted to double every year un-til 2014 [3], matched by technologies such as LongTerm Evolution (LTE) that provides more capacityand higher data rates through the deployment of alarger numbers of smaller cells and extended spatialspectrum multiplexing. In principle there are five levelsfor reducing the energy consumption of mobile radionetworks:

– Improve the energy saving via energy efficientmanagement, optimizing network resources giventraffic loads, mobility patterns and locationinformation.

– Improve the efficiency of the network design byoptimizing the physical placement of resources, i.e.BSs.

– Optimize the material used in BSs and explore new“green” ones that are energy friendly.

– Develop new technologies based on renewable oralternative sources such as solar, wind or biofuels.

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– Increase the social awareness and encourageconservation by rewarding or penalizing usersthat conserve or are responsible for increasingconsumption.

Typical configurations of radio access networks forurban environments serve high capacity demand by uti-lizing dense BS deployments. In dense configurationscertain BSs operate with reduced transmission powerforming smaller cells in either overlaid formationswhere coverage is provided by more than one Ra-dio Access Technology (RAT) or single RAT, e.g.pure LTE. This paper addresses energy saving froma management perspective, permitting certain BSs toreconfigure the transmission power and adjust the an-tenna tilt/pilot based on load and coverage constraints.Such a re-configuration allows selected overlaid BSs tobe powered off once coverage compensation is guar-anteed to match the traffic demand at all times. Infact peak hour network utilization differs from theaverage off-peak providing potential for energy saving.Its magnitude depends on the load difference amongpeak and off peak times as well as on topology specificcharacteristics.

Powered-on BSs provide a wider coverage compen-sating on behalf of the powered-off ones forming a typeof energy subset or partition as illustrated in Fig. 1.Energy partitions are associations among powered-onand powered-off BSs defined within the coverage lim-its of the operating site. Since energy conservation isassociated with dynamic traffic conditions, an efficientway for management it is via Self-Organized Network(SON) means [4]. Energy SON functions should beactivated when the generic traffic demand is relativelylow, a decision based on the aggregate load of severalBSs of a wider network region as introduced in [5].

The concept of energy partition advances existingmethods by introducing coordination among BSs. Inessence, such coordination permits better energy solu-tions since network elements collectively contribute toenergy decisions. In addition, maintaining informationrelated to the association of powered-off and powered-on BSs ensures flexibility for further adjustments based

EnergyPartition

Peak Traffic Configuration Off-Peak Traffic Configuration

Fig. 1 A simple energy partition example

on evolving traffic conditions. Such flexibility allows adynamic energy re-configuration that balances the in-creased load among existing partitions allowing the op-timal BS to operate instead of powering-on additionalones. Each BS entering an energy saving state causesome overhead related to computation and networkupdate, while extra cost is required to re-power it on.It is therefore essential to determine a minimum periodduring which each BS may profitably be powered-off.

This paper introduces and evaluates the methodsto establish and re-configure energy partitions. Energypartitions may be applied in any type of cellular net-work. However, the details considered for this study areLTE-specific. The remaining of this paper is organizedas follows. Section 2 presents an overview of energysaving management summarizing the related proposals.Section 3 introduces the main concepts and deploy-ment issues, while Section 4 elaborates the algorithms.Section 5 describes the simulation setup and analyzesthe results. Finally, Section 6 summarizes our work andprovides further research directions.

2 Related work: energy saving management

Managing energy consumption in radio access net-works should combine a dynamic operation withnetwork planning. A careful placement of BSs, config-uration of transmission power, antenna tilt and otherparameters allow for obtaining a minimum numberof BSs without compromising coverage. Based on thenotion of spectral efficiency per area [6] analyzes en-ergy saving by introducing micro sites to complementconventional ones in various formations. Results showthat area power consumption can indeed be reducedwithout neglecting throughput targets. However, net-work planning is primarily an off-line activity that is notindented to handle dynamic scenarios. By dynamicallyidentifying redundant capacity in addition to networkplanning, significantly higher savings can be obtained.Such energy saving potential depends on the specificnetwork layout and traffic distribution.

2.1 Local energy management

Local energy management allows BSs to switch offselected functions, such as radio carriers at off-peak times, based on local measurements. Operator-provided thresholds and policies ensure that coverageand Quality of Service (QoS) requirements are met,and that increasing utilization leads to re-activatingappropriate components. A different approach is to

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operate certain components in energy saving mode. Toreduce power consumption in low-traffic periods, [7]proposes energy aware schedulers that queue data inthe packet buffer until the varying channel gain be-comes sufficiently good. The scheduler also selects theappropriate modulation and coding schemes and con-trols the power amplifier that is switched off when thereis no data to send. Although, local energy managementprovides adequate savings, decisions on decreasing theenergy footprint are based on local observations with-out considering information from other BSs. Studiessuch as [1] demonstrate that this would enable muchhigher savings.

2.2 Overlaid network energy management

When considering network-level energy saving,different scenarios can be identified. In the overlaidnetwork scenario, the redundant coverage in someareas can be realized by local hotspots as illustratedin Fig. 2. A comprehensive overview of the differentvariants of overlaid energy saving is provided in3GPP’s TR 32.826 [1]. Fundamentally, energy savingis achieved when load conditions allow switching offhotspot cells. Reactivating an overlaid node may beachieved through the macro BS or using a periodicprocess as described in [8]. Whilst most overlaid energysaving schemes aim to switch off hotspots, it is alsopossible to take the opposite approach. Green DelayTolerant Networks (DTN) [9] can achieve energysaving by relaxing coverage and service requirementsat off-peak times leveraging disruption-tolerantnetworking concepts, allowing operators to power-offa number of macro BSs as depicted in Fig. 2c. Inoverlaid networks, it is relatively easy to identify BSs topower-off without causing radio complications but thisscenario, represents only a special case. The genericone is a network with partly redundant coverage,where cells are not fully contained by others.

2.3 Non-overlaid network energy management

Generalizing network-level energy saving to non-overlaid scenarios covers a broader range of scenarios,where energy saving requires more than simply switch-ing off specific BSs. If complete coverage should bemaintained, powered-on BSs are required to compen-sate by reconfiguring transmission power and adjustingantenna tilt. Numerical and analytical results verify thepotential of the non-overlaid energy saving manage-ment in dense network deployments. In particular, thenumerical study of [1] provides an estimation of en-ergy saving based on measurements of carrier and air-conditioning expenditures, while the analytical workin [5] focuses on determining the amount of energyconservation considering different cell layouts.

In [10] the concept of cell zooming that blends en-ergy saving and load balancing is described, a study thatcontinues the work of [11]. The proposed centralized al-gorithm aims to shift traffic towards the highest loadedBSs in a greedy way with the objective to power off asmany under-loaded BSs as possible. This algorithm op-erates on per-user basis, examining whether the processof shifting individual users satisfies load and coveragetargets—an activity that raises scalability and preci-sion concerns considering the user information and thesensitivity of user mobility. The distributed algorithmprovides energy saving as a handover parameter policy,tuning user handover selection preferences by employ-ing a utility function that associates a higher weight toBSs with higher load concentrating traffic at particularlocations allowing lower traffic BSs to “sleep”. In asimulation study, the authors compare the two variants,emphasizing the tradeoff between energy saving andcoverage, and showing that the centralized algorithmoutperforms its distributed counterpart.

A similar distributed energy saving approach basedon a combination of a threshold policy with outagedetection and SON radio configuration is described in[12]. Once a certain BS is powered off, coverage is

Fig. 2 Overlaid networkenergy management

Peak Configuration Off-Peak Configuration

sleeping

sleeping

Off-Peak Green-DTN Configuration

sleeping sleeping

sleeping

(a) (b) (c)

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established by adjusting the transmission parameters ofneighbor sites via supplementary SON functions, whilewhen QoS drops below a limit the reverse process oc-curs. Another centralized approach introduced in [13]considers a hypothetical static case where a heuristicalgorithm with a complete knowledge of stationary userlocations aims to associate users to cells solving anassignment problem with network energy saving beingits main constraint. Both approaches share the samelimitations as cell zooming.

2.4 Energy aware cooperative networks

When considering redundancy in the network in-frastructure, it is important to note the duplication ofcoverage by multiple operators. Cooperation aims atutilizing the combined network resources of differentoperators and at employing energy saving regimes thatwork across operator boundaries. An energy conserva-tion management approach, allowing mobile terminalsto roam between two different operator networks thatoverlap the same geographical area, is proposed in [14].Cooperation among providers is efficient but businessburdens may be applied since coverage and infrastruc-ture investments are core operator assets.

3 Energy partition concepts and deploymentimplications

This section introduces the main concepts beneath ourproposal and elaborates the key issues for deployingenergy-aware re-configuration including the BS transi-

tion process among energy saving states, the handoverof multiple users as well as interference control andupdate of BS neighbor relation.

3.1 Energy partition concepts

Energy partition algorithms are performed once thenetwork enters an off-peak period assuming that BSsare able to enter a stand-by mode with the ability towake-up when needed. Off-peak periods of low trafficdemand ensure stability of the energy saving decisionsas demonstrated in [5], which is critical in realizinghigher benefits by allowing BSs being profitably into asleeping mode avoiding temporary local traffic minimathat may result in uneconomical solutions. Monitor-ing mechanisms that decide the triggering time shouldencounter traffic forecasting methods that considersmoothing to compensate rapid load fluctuations, whilehold down timers are also recommended to introduce aminimum spacing among sequential triggers.

3.2 Energy-aware network re-configuration

Energy-aware re-configuration is the most significantfeature of the proposed scheme allowing flexibility inbalancing the increasing load among existing parti-tions instead of powering-on additional BSs. Figure 3illustrates an example where the energy partitionsare changing their coverage and even the location ofpowered-on BS to accommodate load variations keep-ing energy expenditure constant. In particular, partitionA experiences congestion, i.e., load surpasses the pre-determined threshold. Based on collected information,

Energy partition A resource availability

Energy partition A

Energy partition B

Energy partition C

th

Load

th

Energy partition A

Energy partition B

Energy partition C

Overloaded Energy partition

th: energy re-configuration threshold

thth

th

th

Fig. 3 A simple example of the energy-aware network re-configuration

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partition A knows that its neighbors are under-utilizedand triggers a re-configuration process to balances theload by re-arranging the energy partitions.

To perform such a re-configuration, methods thatcollect information need to associate load and coveragewith certain powered-off BSs, which is a fundamen-tal difference to the initial energy partition establish-ment. Powered-on BSs are expected to collect loadinformation regarding powered-off ones using eitherexact measures or estimations. Exact measures rely onwaking up selected BSs at regular intervals to measurethe associated load, while estimations use time advanceinformation and UE measurements from adjacent BSs[15]. This paper adopts the estimation option whereBSs are able to associate potential hot-spots with thelocation of powered-off BS inside their energy partitionterritory.

3.3 BS coverage transition implications

Energy partitions power-off a specified set of BSs al-lowing certain other BSs to compensate in terms ofcoverage. Once the energy state of each BS is deter-mined, a transition policy follows with the objectiveto avoid outage, while ensuring a smooth and costefficient handover of the affected UEs. The reductionof signaling across the core network is also consideredsince a higher than expected amount of handovers islikely to occur. The transition process is expected to beperformed gradually in a number of steps correspond-ing to pre-determined power levels. BSs are assumedto be able to switch among such different power levelswith BS scheduled to be powered-off decreasing theirpower until they enter a stand-by mode.

The specified transition policy directs coverage com-pensating BSs to anticipate increasing their transmis-sion power creating temporal overlapping areas toensure no outage. Coordination among the involvedBSs is essential to manage the transition processeffectively. In particular, the coverage compensatingBS should increase its transmission power, wait untilall camping UEs handover and then signals to thepowering-off one to switch into a lower power state.Once such step is completed, feedback may be pro-vided through UEs measurements forming a closedloop control guidance. UEs residing in the overlappingareas handover from the BS that is scheduled to bepowered-off towards the coverage compensation onenearly at the same time. To reduce the overhead to-wards the core network, BSs may handle handoversignaling in bulk. It should be noted whilst the bulksignaling handling approach is applicable to any pro-cedure that involves a high number of devices with

common fields in the exchanged signaling messages,the following analysis focuses on its benefits in thecontext of LTE. Indeed, in case of LTE, BSs or eNBs inthe 3GPP terminology may hold back the Non-AccessStratum (NAS) signaling messages, i.e. Tracking AreaUpdate (TAU) requests, for the duration of each powerstep or for a certain number of messages, to proceedwith a bulk of NAS message towards the MobilityManagement Entities (MMEs).

TAU messages consist of mandatory fields, worth 15octets and a set of optional fields. Since we concen-trate only on UEs that are associated with the sameMME, the only parameter which is UE specific, is theM-TMSI (MME Temporary Mobile Subscriber Iden-tities), which identifies a UE at one MME. The otherfields, consisting 11 octets out of a total of 15 octets,are common to all UEs associated with the MME. Theeffort of parsing the parameters of many messages isminimized, decreasing also the time spent for locationupdates by a large factor, while signaling efficiencyis also improved avoiding the processing of multiplemessages.

3.4 Interference management and neighbor relations

The process of changing the coverage configurationof neighboring BSs raises concerns regarding interfer-ence. Assuming a partially frequency re-use Orthogo-nal Frequency Division Multiplexing (OFDM) system,we envision two main alternatives to manage inter-ference during transitions. The main idea is to eitherensure that UE’s frequency blocks are orthogonal oruse cooperative transmission.

Ensuring orthogonal frequencies among UEs camp-ing in progressively changing overlapping areas maybe a part of a pre-planning activity where a manage-ment entity provides hints to neighbor BSs for uti-lizing certain frequency blocks. Alternatively, coloringalgorithms as introduced in [16] may coordinate inter-ference permitting higher resource flexibility. Besidesscheduling orthogonal frequency blocks, a different ap-proach is to utilize cooperative communications as de-scribed in [17]. Such an approach synchronizes simulta-neous transmissions utilizing the same frequency blockstowards UEs ensuring a smooth handover. However,cooperative communications require synchronizationprocedures in the core and radio access network, whichis complex but ensure a better service quality.

When energy partitions or re-configurations are ap-plied, the neighbor relations is not the same and there-fore an update is essential. In the context of 3GPPLTE such update needs also to re-establish the X2interface. The update process may be determined at

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the same time as the energy arrangement and simplycommunicated among the coverage compensating BSs.Alternatively, neighbor relations could automaticallybe verified via the corresponding Automatic NeighborRelation (ANR) SON function [18].

4 Energy partitions and re-configuration

Once the network enters an off-peak period, energypartition and re-configuration algorithms are employedto determine the specific energy state of individualBSs. Three types of algorithms are considered: (1) cen-tralized, executed at a management entity based oninformation collected from the complete radio accessnetwork, (2) distributed, acting on individual BSs basedon neighbor information and (3) local BS-awaking,performed on coverage compensating BSs. Such algo-rithms rely on load and coverage information, whichare exchanged among specific network elements eitherregularly or on demand depending on the variant. Theload indicators considered are those of hardware orUE and radio physical resource block, defined in [4] as(low, medium, high, overloaded). Besides load indica-tors, load thresholds limiting the shifted load are alsointroduced as a protection margin.

The wireless cell model employed in this paper as-sumes that BSs form circular cells employing direc-tional antennas. Each BS is supposed to be capable

Initial Coverage

Extended Coverage

Sleeping BS

Peak Traffic

Off-Peak Traffic

Fig. 4 Cell model and coverage extension

to extend its coverage in a directional manner by re-configuring its transmission power. Outage is avoidedby exchanging coverage information among neigh-bor BSs assuming that they could adjust the antennatilt/pilot accordingly without encountering any specificlocation restrictions. A simple cell representation is de-picted in Fig. 4 considering a three-sector arrangementproviding an overview of the coverage extension.

4.1 Centralized max-load algorithm

Management systems are responsible for executing cen-tralized algorithms, collecting first load informationfrom individual BSs. The proposed centralized algo-rithm selects the maximum loaded BS and explores itsneighbor list starting from the lowest loaded neighborwithin its coverage limits. Its purpose is to concentratethe network load into a few moderately loaded BSpowering off as many under-loaded BSs as possible.Once the management entity determines the energyformation, it triggers the appropriate changes, whichare executed on the corresponding BSs.

Algorithm 1 Centralized Max Load1: create shorted SetBS of decreasing order; SEP = ∅;2: foreach BS ∈ SetBS3: pon = ∅; Soff = ∅;4: pon = {BS};5: SBS ⇐ SBS ∪ pon;6: while

∑BS∈Soff

L(UE,R) < thL & Cv(Soff) < Dlim

7: BSn = minL(UE,R);8: break ties based min distance;9: Soff = Soff ∪ BSn;

10: if∑

BS∈SoffL(UE,R) > thL

11: break;12: else13: SEP(pon) = Soff;14: update pon neighbor list and L(UE,R);15: end16: end17: SetBS ⇐ SetBS \ Soff

18: end

Algorithm 1 illustrates a pseudo-code version of thecentralized algorithm called max load. The algorithminitializes a sorted SetBS of BSs based on maximum loadconstraints and selects the first one as powered on, pon.For each pon, it keeps track of the set of associated pow-ered off BSs, Soff and the energy partitions using theset SEP. As long as the UE and Radio load constraints

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L(UE,R) are satisfied and the coverage Cv(Soff) is withindistance limits Dlim, the algorithm centered on the pon

continuously selects its minimum load neighbor BSn,breaking ties based on distance. Otherwise, it selectsthe next maximum load BS until the SetBS is empty. Ateach step the algorithm performs the essential updatesand upon termination the management entity providesthe energy (re-)configuration.

The centralized max load algorithm is deployed us-ing binary heaps for maintaining the neighbor list el-ements sorted, encountering UE load as the primaryconstraint and radio resources consumption as a sec-ondary. The computational complexity is O(1) for re-trieving the optimal element, O(logV) for updating thebinary heap and O(VlogV) for sorting V elements,where V is the number of BSs involved in the process.

4.2 Distributed algorithms

Distributed algorithms rely on regular load and cover-age information and on distributed coordination mech-anisms that avoid conflict among the decisions ofneighbor BSs. The algorithms for establishing energypartitions select a pair and then determine the energysaving state of each BS, while the ones that performre-configuration iteratively enforce changes among thecongested energy partition and its neighbors.

To select a pair, the energy partition algorithm ini-tially activates energy saving on a certain BS, whichexamines its neighbor list to select the minimum loadone breaking ties based on distance. Provided that loadand coverage constraints are fulfilled, it proceeds todetermine the BS to keep powered-on, otherwise, itterminates on that particular BS. The preference ofpowered on BSs is towards the ones that seem ableto accommodate further energy potential, i.e. towardsBSs with a maximum number of neighbor list withinthe shortest distance. The BS kept powered on per-forms load and coverage updates and the process ofselecting pairs continues until there is no more energysaving improvement. Upon congestion, the problem-atic partitions, select the least loaded neighbor to per-form load balancing by exchanging powered-off BSs.Such process may cause coverage compensating BS toswitch-off and awake other BSs instead to change thecoverage range and consequently the load balancingpotential.

A pseudo-code version of the algorithm is shownin Algorithm 2, with two parts, one for establishingenergy partitions and another for re-configuring. Inestablishing energy partitions, the selected BS, BSsel

becomes a member of the pairBS and determines fromits neighbor list, nl, the optimal neighbor BSn consid-

ering load and coverage Cv(listn), which is added inpairBS. Provided that the UE and radio load L(UE,R) isbelow the predefined thresholds, the algorithm deter-mines the energy state for each BS, i.e. pon − poff andperforms the related updates.

Algorithm 2 Distributed Pair-wise Partition Match1: if no partitions are established2: pairBS = pairBS ∪ BSsel;3: while

∑BS∈pairBS

L(UE,R) < thL & Cv(nl) < Dlim

4: BSn ∈ nl with minL(UE,R);5: break ties based min distance;6: pairBS = pairBS ∪ BSn;7: if

∑BS∈pairBS

L(UE,R) > thL

8: break;9: end

10: pon = BS with max nl ; break ties by min distance;11: poff = remaining, other BS;12: pon performs L(UE,R) and Cv(nl) updates;13: end14: else15: create sorted SEP and Soff ∈ EPc based on load;16: while EPc L(UE,R) > thL&

∑SEP

L(UE,R) < thL

17: EPt ∈ SetEP with min L(UE,R);18: BSex = poff ∈ Soff with L(UE,R) & Cv < Dlim;19: if (EPt ∪ BSex)L(UE,R) < thL20: EPt = EPt ∪ BSex;21: update EPc poff association and L(UE,R);22: else23: Soff = Soff \ BSex;24: end25: if Soff = ∅ {break;} end26: end27: end

Energy re-configuration begins from the congestedpartition, EPc provided that at least one neighbor isnot overloaded. Initially, the EPc creates a set SEP ofneighbor partitions EPn in an increasing load order,and a set of its powered-off BS Soff in a decreasingload order. The least loaded neighbor partition is thenselected as target EPt, and the most loaded powered-off BS within its coverage area as a candidate to beexchanged BSex. As long as the EPt is not overloaded,the algorithm shifts BSex and performs the appropriateupdates. Otherwise BSex is excluded from the Soff sinceits shift is not feasible. The algorithm continues pro-vided that Soff is not empty, until a solution is reachedor there are no more under-utilized neighbor partitions.If under-utilized neighbor partitions exist but cannotassist the congested one due to coverage limitations, thealgorithm powers-off the compensating BS inside theoverloaded partition and awakes another one within acloser proximity. Such a process may prove complex

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though, especially when major changes are introduceddue to coverage reasons.

In establishing energy partitions updates are trig-gered on per pair basis causing excessive overhead.A variant algorithm is introduced to overcome suchproblem. It selects a powered on BS, which keepsconstant forming consecutive pairs with the optimalneighbors. The variants of the distributed algorithmare illustrated in Fig. 5, the first one is referred to aspair-wise match, while the second is dubbed as maxneighbor list. The worst-case complexity of distributedalgorithms is similar to centralized ones because of theuse of binary heaps; the difference is on the scope ofoperation. Specifically, the complexity of algorithms forestablishing initial partitions is O(NlogN), where Ndenotes the number of BSs, and for re-configurationO(kMlogk), where k is the number of neighbor parti-tions and M is the BSs inside the overloaded one.

4.3 BS-awaking selection algorithm

In case the re-configuration process fails, an essentialapproach is to awake a BS to accommodate the in-creased load. Although waking up any BS may improvethe network performance balancing the load of the con-gested partition, poor planning might lead to temporarysolutions, which may cause further BS awaking andenergy expenditure. The aim is to select a BS within theproblematic region taking into account flexibility, con-sidering the potential for further coverage adjustmentsupon additional load variations. For this reasons theBSs with the highest coverage potential are preferred.

A pseudo-code version of the BS-awake selection al-gorithm is shown in Algorithm 3. The algorithm beginsconsidering only the powered-off BSs associated withthe hotspot, SetBS, which are sorted based on load. Eachmember of the SetBS is a candidate BS to be powered-on, cpon , assessed in terms of load, coverage and dis-tance encountering the complete overloaded partition

Algorithm 3 BS-Awaking Selection Algorithm1: create sorted SetBS of decreasing load order;2: Setcpon

= ∅;3: foreach poff ∈ SetBS4: cpon = poff max load; Cv(cpon ) = ∅;5: foreach poff ∈ SEP6: if d(cpon ,poff) < Dlim7: Cv(cpon ) = Cv(cpon ) ∪ poff;8: end9: end

10: Setcpon= Setcpon

∪ Cv(cpon );11: update sorted Setcpon

of coverage-distance;12: if Cv(cpon ) contains all SetBS elements13: break;14: end15: end16: pon = highest coverage-distance cpon ∈ Setcpon

;

SEP. As long as the distance between the cpon and poff

satisfies the coverage limits, Dlim, the poff is stored ina temporary variable Cv(cpon) that keeps track of thecoverage and distance relations. Distance is consideredas an additional energy metric because it indicates thetransmission power for providing coverage.

The Cv(cpon) information is then used to create asorted set, Setcpon that lists candidate powered-on BSsbased on their flexibility, i.e. coverage and distance.Once the highest load cpon is identified with potentialcoverage to the entire overloaded partition, the algo-rithm is completed. The pon solution is the optimal onewith highest flexibility. Otherwise, it continues until allcpon are considered and selects as pon, the candidate BSwith the highest potential coverage breaking ties basedon distance. The BS-awaking selection algorithm hasan O(QWlogW) complexity, where Q and W are thenumber of BS inside the hotspot and overload partitionrespectively, using binary heaps for maintaining thedistance-coverage information.

Fig. 5 Cell model andcoverage extension

Energy saving -pair

BS adjacencyPair-Wise Match Max-Neighbor List

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Table 1 Simulation parameters for evaluating energy partition algorithms

BSs (V) CU E CBw [LR, HR] p(BSlow) Loadlim pact [L,H]Bw

[10100] 100, 24 Mb [0.5, 3] 0.7 20% 0.7 6.54 k, 3 Mb[10100] 100, 24 Mb [0.5, 3] 0.4 40% 0.3 6.54 k, 1 Mb

5 Simulation setup and result analysis

Following the description of the energy partition al-gorithms, this section compares their performancethrough a simulation based study. The simulation envi-ronment and the energy algorithms are implemented inMatlab. The objective is to compare the proposed algo-rithms using different off peak network configurations.Initially, the energy partition algorithms are comparedconsidering the following scenarios: (i) high user trafficvolume per BS and (ii) high bandwidth per session. Inthe first scenario, more users are introduced with lowbandwidth demands, while the second utilizes fewerusers with higher per session bandwidth demand. Thenthe re-configuration algorithms are compared with amore simple energy partition, which maintains a con-stant configuration regardless of load alternations.

The radio access network is represented as a graphG(V, E) where V and E indicate BSs and adjacencyamong two neighbor BSs permitting users to handover.The Erdos–Renyi model G(V, p) [19] is employed togenerate random topologies considering different de-grees of adjacency with p = 0.1. Such a model is usedto study the effect of adjacency as a function of networksize. Cells are circular with radius uniformly distributedbetween [LR, HR] with wider coverage potential three

times higher the maximum radius. Cell dimensions areassumed to be in kilometers but the same results holdfor any kind of arrangement that maintains the sameinitial-potential cell radius ratio.

Every BS supports up to CUE UEs and provides CBw

bandwidth, representing a generalization of wirelessresources as in [14], assuming no inter-cell interfer-ence. Load is established by launching individual UEsassociated with each BS with UEs being active basedon probability pact. The bandwidth of each session isuniformly distributed in the interval [L, H], where L is6.54 Kbps being the smallest rate of the Adaptive Multi-Rate (AMR) speech codec and H representing web tovideo or ftp applications.

5.1 Evaluation of energy partition algorithms

For evaluating energy partition algorithms, we initiatesimulations from an off-peak instance, where the ra-dio access network supports sporadic spots of averageload, lower than normal conditions. The simulationdoes not consider a temporal development, but is try-ing to find an optimal solution based on the initialconfiguration. Under-loaded BSlow are introduced withspecified probability p(BSlow) having an upper boundload limit Loadlim. Table 1 summarizes the simulation

Fig. 6 ES algorithmsperformance

10 20 30 40 50 60 70 80 90 100

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

Number of eNBs

Per

cent

age

of E

nerg

y S

avin

g

Performance Comparison of Energy Saving Algorithms

C. Max Load UEC. Pair wise UED. Pair wise UED. Max Neigh UEC. Max Load BwC. Pair wise BwD. Max Neigh BwD. Pair wise Bw

Higher number of UEs

Higher Bw per session

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Fig. 7 ES Algorithmshoff overhead

10 20 30 40 50 60 70 80 90 1001

2

3

4

5

6

7

8

9

10

11Transition Cost: Number of UE Hoffs per eNB

Number of eNBs

Num

ber

of U

EH

offs

per

eN

B

C. Max Load UEC. Pair wise UED. Pair wise UED. Max Neigh UED. Max Neigh BwD. Pair wise BwC. Pair wise BwC. Max Load BW

Higher number of UEs

Higher Bw per session

parameters for the two scenarios. A centralized versionof the pair-wise algorithm is also considered with theoption of repeating the process returning the highestenergy saving configuration. A prior simulation studyindicated that 20 repetitions provide good results with-out increasing significantly the delay. We assess theinitial energy partition algorithms using the followingcriteria:

– Energy efficiency represents the percentage ofpowered off BSs to the total number of BSs.

– Load update cost indicating the number of loadupdate messages among neighbor BSs.

– Handover cost indicating the number of handoversthat occur when BSs are powered-off.

– Cost of collecting load information referring tothe load messages from particular BSs towards acentralized management entity.

Figure 6 presents the energy efficiency of the al-gorithms for high UE volume and high bandwidthper request, respectively. From the figure, centralizedalgorithms outperform distributed ones regardless ofthe network arrangement. Specifically, the centralizedmax load algorithm surpasses all the others, followedby the centralized pair-wise match and then by thedistributed algorithms, which exhibit similar perfor-mance. The reason behind this is the scope of loadinformation used by centralized algorithms from thecomplete network producing better energy partitionscompared to the distributed ones that use informationobtained from the neighbor BSs. The energy efficiencyof all algorithms increases with the network size, pri-marily because the neighbor list of each BS grows. Ageneric observation is that higher overall resource con-

sumption in combination with the high bandwidth peruser limits the flexibility of transferring active sessionsamong coverage compensating BSs. Therefore, a loadand mobility policy that restricts bandwidth demandson specific locations could prove useful for meeting thetarget conservation limits.

The handover cost related with configuring the en-ergy state is illustrated in Fig. 7. The cost of centralizedalgorithms is similar, much lower than the equivalentcost of distributed. The main reason is based on theway different algorithms determine the energy parti-tions. Specifically, centralized algorithms determine thecomplete energy partitions before applying any related

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Fig. 8 High UE: load update overhead

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Fig. 9 High bw/request load update overhead

changes, whereas the distributed ones perform a step-wise operation. A higher UE volume with lower persession demands generates higher overhead, due to alarger number of transferred UEs.

The load update cost is shown in Figs. 8 and 9 for thehigh UE volume and bandwidth per user, respectively.Such update cost is essential for distributed algorithmsbut optional for centralized. Whereas the variants ofthe centralized algorithms exhibit similar performance,the load update cost of distributed algorithms dependssignificantly on the specific variant—with the pair-wisematch generating higher cost compared to the maxneighbor list. The reason is that each pair-wise step, isfollowed by a load update, while in the max neighborlist, an update is performed upon the new BS selection.

Considering the high UE volume scenario, the over-head of the distributed pair-wise algorithm is higherthan the others. However, as the bandwidth per sessiondemands increases, the overhead of centralized algo-rithms surpass the one of distributed. Higher bandwidthper session demand cause a nearly double increase inthe overhead for centralized algorithms but a doubleoverhead reduction for distributed ones. This overheaddecline of the distributed algorithms is caused by thelimited solution space, while the higher amount of com-pensating BS is the reason for the increased overheadfor the centralized algorithms.

The overhead for collecting load information forcentralized algorithms is of V magnitude, where Vdenotes the number of BSs included in such process.The exact amount of overhead also depends on the av-erage packet size, i.e. packet header and on the numberof load indicators. Finally, considering the worst-casecomplexity, centralized algorithms are V/N times morecomplex, where N is the average neighbor list size. Insummary, for establishing energy partitions, centralizedalgorithms are more energy efficient, produce less han-dover overhead but cause more overhead for gainingload information and are more complex.

5.2 Evaluation of re-configuration algorithms

For evaluating re-configuration algorithms, we initiatesimulations again from an off-peak instance, but thistime the simulation process encounters traffic devel-opment, which is modeled as requests with specificsource, destination and bandwidth requirements. In-coming users are assumed to arrive independently fol-lowing Poisson with mean λ, while the session holdingtime is assumed to be an exponential with mean μ = 5min. The cell residence time is also assumed to beexponentially distributed with mean n = 3 min. En-ergy partitions are established based on the initial loadconditions and then additional load is introduced atfour random partitions by inserting new users with λ ={4, 5, 6, 7, 8} to create congestion at selected regions.

A summary of the simulation parameters is illus-trated in Table 2. The evaluation is performed mea-suring the contribution of each method to blocking anddropping rates with respect to the energy consumption.The blocking-dropping rate is defined as the percentageof blocked incoming and dropped handover sessionsto the total amount of sessions while energy conser-vation is measured as defined priory. The blockingand dropping rates for each re-configuration algorithmincluding the simple centralized energy partition with-out re-arranging capabilities are shown in Fig. 10. Theperformance of the energy partition approach withoutre-configuration degrades rapidly. The distributed re-configuration then follows, while the centralized andBS-awaking selection algorithms introduce a consider-ably lower dropping and blocking rates especially asλ grows, with the BS-awaking scheme slightly outper-

Table 2 Simulation parameters for evaluating the re-configuration algorithms

BSs (V) CU E CBw [LR, HR] p(BSlow) Loadlim pact [L,H]Bw

40 50, 12.6 Mb [1, 5] 0.7 10% 0.3 6.54 k, 1 Mb

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5% higher energy consumption

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Fig. 10 Comparison of the re-configuration methods

forming the centralized. Distributed and centralizedre-configuration algorithms cannot wake up additionalBSs, maintaining constant energy consumption. Thissimplifies the comparative study, which focus on theperformance of each individual algorithm.

The performance of all schemes is similar for λ < 5,beyond which their difference increases since furtherincoming load creates congestion on certain partitions.The centralized algorithm outperforms the distributed,because it uses information from the complete networkproducing more energy efficient re-configurations. Inaddition, the centralized scheme is faster in the sensethat it is able to assess in a single step multiple con-gestion problems involving more than a single parti-tion. Such rapid reaction is impossible for a distributedscheme because once a BS starts the re-configurationprocess neighbor BSs wait to avoid conflicts. Thisdifference is investigated in this study by increasing λ

on four regions introducing overloaded partitions at thesame time, evident in Fig. 10 for λ > 7.

The algorithm that selects and wakes-up additionalBSs inside overloaded partitions outperforms both cen-tralized and distributed schemes at the cost of increasedenergy. Specifically, it selects additional BSs to re-power-on accounting for an energy increase of 2.5to 10% respectively. For λ < 6 the energy consump-tion growth is 2.5%, while for λ = {7, 8}, it rises to5 and 10% respectively. Centralized and distributedschemes are not increasing the energy consumption,since they only attempt to re-arrange the energy for-mation keeping the same number of operating BSs.Considering complexity, the centralized approach is themost demanding and introduces the highest overhead

for retrieving load information. The distributed and BS-awaking one are less complex since a smaller numberof BSs are involved producing also lower informationexchange overhead.

6 Conclusion

This paper introduces the concept of energy partitions,associations of powered on and off BSs, formed collec-tively in a SON manner with the objective to matchthe operator offered capacity to the traffic demand.The unique benefit of energy partitions is flexibility,which permits energy re-configuration upon varyingload conditions conserving maximum energy or upona failure.

Centralized and distributed variants were developedfor establishing energy partitions and for producingenergy re-configuration. Our simulation results suggestthat centralized algorithms perform better especiallyas bandwidth demand per user increases. In addition,in the re-configuration process centralized algorithmsmay react faster if congestion is spread among multi-ple partitions simultaneously. However, for regionallylimited parts of the network, the advantage of havinga global view is becoming less important, and a dis-tributed algorithm could be competitive. Regarding theadoption of the BS-awaking algorithm, it could provecritical for cases where the re-configuration effort ishigher than the energy consumption of one moreawake BS, especially under circumstances of networkinstability.

Leveraging the insights described in this paper, oneof the next steps for future work would be to analyzethe limitations of the re-configuration process in termsof cost and stability. Moreover, we envision analyzingthe performance of energy saving taking into accountradio communication aspects, i.e. propagation and in-terference. Finally, we see some potential in furtherincreasing energy saving by applying operator policiesto influence user behavior, by rewarding conservationand penalizing users responsible for high consumption.

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