Energy efficient adaptive cellular networkconfiguration with QoS guarantee

Pierpaolo Piunti∗, Cicek Cavdar†, Simone Morosi∗, Kaleab Ejigayehu Teka†, Enrico Del Re∗ and Jens Zander†∗Department of Information Engineering, University of Florence, Florence, Italy

Email: pierpaolo.piunti@unifi.it, simone.morosi@unifi.it, enrico.delre@unifi.it†Communication Systems Department, KTH Royal Univesity of Technology, Kista, Sweden

Email: cavdar@kth.se, kaleab.teka@masterschool.eitictlabs.eu, jenz@kth.se

Abstract—Cellular network energy optimization is driven bydifferent factors, namely transmission power and activity of thebase stations. Quality of Service (QoS) of users is determinedby received power, interference and bandwidth allocation andcannot be neglected. In existing studies, such drivers are treatedseparately, i.e., bandwidth allocation is fixed while power con-sumption is a variable to be optimized. This paper proposesa novel optimization framework which is aimed at minimizingthe power consumption in cellular networks while affording aminimum bit rate for each mobile terminal by jointly consideringenergy consumption and QoS drivers. Mixed Integer QuadraticProgramming (MIQP) based optimization framework solves theproblems of the determination of the user association, thebandwidth allocation, the identification of the active base stationsand their transmission power, guaranteeing also a requestedservice rate for each user. The proposed solution is shown toallow power savings of up to 60%, very close to the optimumlower bound, when the traffic is below the 35% of the maximumload.

I. INTRODUCTION

In the internet era, mobile traffic is becoming one of thedominating contributors of the increasing data rates. Internettraffic is envisaged to grow approximately by a factor of 1.7each year [1] while the mobile operator average revenue peruser (ARPU) tends to decline due to the market saturationand the high infrastructure costs. In order to cope with thisrevenue gap, mobile operators are looking for methods toreduce their cost with no loss in the quality of service (QoS).However, capacity growth comes together with an increasein power consumption. From sustainability perspective, thereare currently more than 4 million base stations in the worldconsuming 60 TWh energy per year corresponding to 80%of the energy that is spent for mobile telecommunications[2]. Mobile networks are dimensioned according to the peakdata rates in order to satisfy the user requirements at busytraffic hours; however, the infrastructure capacity is under-utilized most of the time, calling for adaptive base stationactivation and deactivation mechanisms according to user andtraffic distribution to reduce power consumption: as a matterof fact, capacity must be available when and where the userdemands for it, but base stations do not have to be always on.

A large number of studies have been recently carried outfocusing on traffic adaptive activation of radio resources tosave energy in cellular networks. Since mobile networks aredimensioned according to peak traffic hours, a set of BSs

can be put in sleep mode when the traffic load is light, e.g.,during the night hours. Using this solution, the BS activitycould be adapted to the traffic demand avoiding the energywasted due to dimensioning for peak demand [3]. BS sleep isa critical issue because when a deactivation decision is taken,some coverage holes could arise in the area served by thatBS. Therefore a layer of BSs is assumed to serve as coveragelayer and load adaptive cell sleep strategies are not consideredfor those BSs. For traffic fluctuations happening between dayand night, i.e. according to a coarse time granularity, macrosleep or deeper sleep can be enabled under this assumption.However, for medium to high traffic hours deep sleep is notpossible. Therefore, in the time domain finer granularity ofsleep mode has been introduced. Such a strategy, identified ascell discontinuous transmission (DTX) [4], is enabled by theparticular structure of the LTE frame and allows the transceiverdeactivation (sleep mode) during the idle time slots. Thus,signaling symbols can be transmitted and cell coverage is notaffected. Cell DTX feature does not require specific decisionsto be taken regarding when to sleep and traffic offloading[4]. However, the saving that can be achieved is limitedcompared to deep long term sleep. For the long term sleep, inorder to maximize the energy savings, the association betweenusers and BSs can be considered as an optimization variabletogether with bandwidth allocated to each user [5]. As a matterof fact, energy can be saved by increasing the bandwidthper user and, consequently, reducing the BS transmissionpower if the target datarate per user is fixed [6]. Such asolution is known as bandwidth expansion mode (BEM) andcan be applied when resource utilization is light, i.e. in lowload conditions. Another radio resource management (RRM)solution for energy saving is power control, which aims atminimizing the BS transmission power with a given QoS targetfor the served users. Note that the benefit of power controlis not only in the energy reduction but also in neighbourcell interference management. In particular, in [7] authorspropose an optimization framework to maximize the quantityof transmitted bits per energy unit. Such a solution looks at theenergy efficiency maximization, but does not care about a QoStarget for each user. Moreover, in [8], transmission power foran OFDM communication is minimized without consideringany constraint on power model.

By combining some of these approaches, different opti-

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mization solutions have been proposed. A pricing algorithmis evaluated in [9] to solve user association problem andminimize area power consumption, while considering interfer-ence: therein, mobile terminal rates are fixed and bandwidthis equally allocated to users regardless of their received signalstrength. In [10] power supply per LTE frame is minimizedby assigning an opportune transmission power and rate foreach link, without considering the mapping problem and aminimum rate value to ensure QoS. Quality of service con-straints are considered in [11], [12]. In [11] BS transmissionpower and user rate are optimized in order to maximize theenergy efficiency in terms of ”bit per Joule”. The formulationconsiders a single cell scenario and the effect of neighbouringinterference and users mapping are not considered. In [12] atwo step algorithm aiming at minimizing energy consumptionby opportunely assigning subcarriers and power to users isproposed. Such a solution considers a very simple bandwidthdivision among served users and does not take advantageof BEM in order to reduce the unfairness in the perceiveddatarate.

In this study, a more realistic strategy is investigated:a given service rate is guaranteed to mobile terminals; ifsufficient bandwidth resources are available, mobile users canobtain higher rates than the target value since their receivedpower must be greater than the terminal sensitivity threshold.Moreover, the bandwidth blocks are assigned not uniformly,but according to the spectral efficiency of the overall userassociations, saving more resources for the users experiencinglower signal quality and including the benefits provided byBEM. Since spectral efficiency depends on the interference aswell as on the user associations, the solution of the optimiza-tion problem is not trivial. Hence, an optimization frameworkis introduced which is based on Mixed Integer QuadraticProgramming (MIQP): within this framework the user asso-ciations are solved together with the decision variables forthe base stations’ activity. Moreover, also the user bandwidthand rate assignments as well as transmit power of each activebase station are determined. This approach utilizes a snapshotmodel where the problem is solved for a given time while allusers are transmitting together. Moreover, the cell switch offis possible only if another layer guarantees the coverage of thearea. This work focuses on the capacity layer, assuming theexistence of a coverage layer. This assumption is justified bythe 5G vision that separates control and data layer in cellularnetworks for green network operation [13]. In order to evaluatethe framework performance, we have used two benchmarkingsolutions: as an upper bound, a conventional power controlalgorithm is used whereas, as a lower bound, MIQP model issolved to find minimum power consumption, guaranteeing therequested user rates only if interference between neighbourcells could be neglected.

The paper is organized as follows. Sec. II describes thesystem model and the scenario. In Sec. III the optimizationproblem is formulated. Sec. IV describes the evaluated energysaving strategies. Sec. V discusses the simulation results and,finally, Sec. VI concludes the work.

II. THE SYSTEM MODEL

A typical LTE system deployment is considered with a givenbandwidth and a set of resource blocks. This study focuses onthe capacity layer, assuming that the coverage condition isguaranteed by another layer of the cellular deployment. In theconsidered deployment, a set of omnidirectional base stations(BSs) provides radio access to a certain number of user equip-ments (UEs). Each base station must allocate the availablebandwidth resources among the associated transmitting usersby assigning the proper amount of physical resource blocks(PRBs) and guaranteeing their satisfaction in terms of bitrate.UEs request a constant bitrate and will be served at the sametime.

Let B = {BS1, ..., BSN} and U = {UE1, ..., UEM} bethe set of N deployed base stations and the set of M userswhich have to be served respectively. The binary variable xmodels the association between BSs and UEs, such that:

xij =

{1 if UE j is served by BS i

0 otherwisei ∈ B, j ∈ U (1)

Assuming πij is the power assigned for transmission be-tween BS i and UE j, and wij is the bandwidth assigned byBS i to UE j, the datarate achieved by UE j is:

ρj =∑i∈B

xijwij log2(1 + γij) (2)

where γij is the SINR between BS i and UE j. Transmissionpower of each BS i is calculated as Pi =

∑j∈U πijxij .

Therefore, the SINR γij is

γij =πijσijxij

wij

W

(∑Nk=1 Pkσkjζk(1− xkj) +WN0

) (3)

where σij is the channel gain between BS i and UE j, Wis the total available bandwidth at a BS and N0 is the noisespectral density. Note that in (3) the received interference isweighted by the effective fraction of bandwidth assigned tothe UE. Such a choice is justified by the need to consider theeffect of allocating different portions of bandwidth to eachUE. The activity status of each BS is modelled by the binaryvariable ζ, such that:

ζi =

{1 if BS i is active0 if BS i is in SLEEP mode

i ∈ B (4)

III. PROBLEM FORMULATION

Given the system defined in Sec. II, the problem is tominimize the global power consumption. Therefore, the op-

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Algorithm 1 Power ControlGiven: xij , wij , PMINj ∀i ∈ B, ∀j ∈ U ; PMAX ; Rt;Return: Pi ∀i ∈ B; Pij ∀i ∈ B, ∀j ∈ U ;

1: for all i ∈ B do P (0)i ← PMAX

2: repeat3: for all i ∈ B do4: Calculate πij as in (6) ∀i ∈ B5: Pi ←

∑j∈U πijxij ∀j ∈ U

6: end for7: until convergence8: Update πij as in (6) ∀i ∈ B ∀j ∈ U9: Update Pi to the maximum allowed value ∀i ∈ B

timization problem is formulated in (5a)-(5h).

argminπ,x,ζ

N∑i=1

[(a

M∑j=1

πijxij + P0)ζi + (1− ζi)Psleep] (5a)

s.t.M∑j=1

xij ≤ NPRB ∀i ∈ B (5b)

N∑i=1

M∑j=1

xij =M (5c)

N∑i=1

xij = 1 ∀j ∈ U (5d)

cij ≤πij · σijPMINj

∀i ∈ B ∀j ∈ U (5e)

cij − xij ≥ 0 ∀i ∈ B ∀j ∈ U (5f)ζi ≤ xij ∀j ∈ U ∀i ∈ B (5g)M∑j=1

πij ≤ PMAX ∀i ∈ B (5h)

The objective function is the global power consumption,which is calculated as in [14]; parameters a, P0 and Psleep arethe slope of the dynamic consumption, the fixed consumptionand the sleep mode consumption respectively. Constraint (5b)is for the BS capacity limitation, since a BS cannot assignmore than available bandwidth elements NPRB . Constraints(5c) and (5d) ensure that each UE must be covered by atleast one BS and can be connected to only one BS at a time.Constraint (5e) is the key for assuring the QoS. The binaryvariable cij equals to 0 if πijσij ≤ PMINj . For a givenUE, cij will define the set of potential BSs that can providethe minimum received power, PMINj . Then, by the help ofconstraint (5f), only one of the BSs in this set is selected. Theactivity status of a base station is linked to the user associationsby constraint (5g). Finally, constraint (5h) set the limit on BStransmission power.

Algorithm 2 Equal BW allocationGiven: xij ∀i ∈ B, ∀j ∈ UReturn: wij ∀i ∈ B, ∀j ∈ U ;

1: wij =WPRB

⌊NPRB∑j∈U

xij

⌋∀i ∈ B, ∀j ∈ U

Algorithm 3 Proportional BW allocation (no residuals)Given: σij , Pi, πij ∀i ∈ B, ∀j ∈ UReturn: wij ∀i ∈ B, ∀j ∈ U ;

1: Set nPRB = 0 ∀j ∈ U2: for all i ∈ B do3: repeat4: for all j ∈ U do5: Calculate ρj as in (2)6: if xij = 1 AND ρj < Rt then7: increment nPRB8: wij = nPRBWPRB

9: end if10: end for11: until all PRBs are assigned or Rt is reached by all

served UEs12: end for

Algorithm 4 Proportional BW allocation (with residuals)Given: σij , Pi, πij ∀i ∈ B, ∀j ∈ UReturn: wij ∀i ∈ B, ∀j ∈ U ;

1: Set nPRB = 0 ∀j ∈ U2: for all i ∈ B do3: repeat4: for all j ∈ U do5: Calculate ρj as in (2)6: if xij = 1 AND ρj < Rt then7: increment nPRB8: wij = nPRBWPRB

9: end if10: end for11: until all PRBs are assigned or Rt is reached by all

served UEs12: end for13: if any residual PRB AND all UEs are satisfied then14: share equally residual PRBs15: end if

IV. NETWORK ADAPTATION SOLUTIONS

A. Power control

As described in Sec. I, power control is a well known solu-tion to decrease the global energy consumption by acting onthe reduction of intercell interference. In this work a modifiedversion of the power control algorithm presented in [15] isconsidered in order to extend its solution to a multichannel

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Algorithm 5 MinPowerGiven: σij , PMINj ∀i ∈ B, ∀j ∈ U ; PMAX ;Return: xij , wij ∀i ∈ B, ∀j ∈ U ;ζi, Pi ∀i ∈ B; Pij ∀i ∈ B, ∀j ∈ U

1: Solve MIQP2: Solve bandwidth allocation (as in Algs. 2, 3 or 4)

Algorithm 6 MinPower-QoSGiven: σij ∀i ∈ B, ∀j ∈ U ; PMAX ; PMINj ;Return: xij , wij ∀i ∈ B, ∀j ∈ U ;ζi, Pi ∀i ∈ B; πij ∀i ∈ B, ∀j ∈ U

1: repeat2: Execute MinPower algorithm (Alg. 5)3: Execute Optimum power control algorithm (Alg. 1)4: Datarate (ρj) calculation as in (2) ∀j ∈ U5: for all j ∈ U\satisfied UEs do6: PMINj ← PMINj + δ7: end for8: until no outages

TABLE ISIMULATION PARAMETERS

Parameter ValueDeployment 19 BS, ISD=500m, hexagonal grid, wrap-aroundPath loss L = 15.3 + 37.6 log(d) (3GPP Typical Urban)Shadow fading std dev 8 dBIndoor loss 20 dBBandwidth 5 MHz (25 PRBs)Carrier frequency 2GHzMax BS PTX 20 WUE sensitivity -90 dBmNoise PSD -174 dBm/HzTarget user datarate 512 KbpsPower consumption a = 4.7 P0 = 130 W Psleep = 13 W

scenario with minimum and maximum power constraints 1. Asshown in Alg. 1, this power control algorithm takes as input aUE-BS association and a bandwidth assignment for each UEand it iteratively obtains the optimum BS transmission powerwhich is able to guarantee the target datarate for each UE. Thecore of the optimum power control algorithm is to calculateat each iteration n the power transmitted by a BS to a certainUE as

π(n)ij =

wij2Rtwij

Wσij

(∑k∈B

P(n−1)k (1− xkj)σkj +WN0

)(6)

where Rt is the target datarate, i.e. the QoS constraint foreach UE. The initial condition is such that

∑j π

(0)ij = PMAX

for all i ∈ B. Note that the power assigned to a BS, i.e. Pi,cannot be greater than the maximum allowed power PMAX ;in that case the power PMAX is divided equally among each

1For the proof of convergence in iterative power control, please see [15],pp. 163-171.

UE to indicate the UEs under outage. Moreover, the receivedpower for each UE j cannot be smaller than the sensitivityPMINj ; in that case the power which is transmitted by a BSto a certain UE is adjusted by πij = max

(PMINj

σij; πij

).

B. Bandwidth adaptation

Bandwidth assignment to each served UE is a task thatcan be solved in different ways by radio network operators(RNOs) depending on their policies. The simplest way to do itis to equally split the available bandwidth among all connectedUEs, as in Alg. 2: in this strategy the UE performance dependsonly on the signal quality for a given PRB bandwidth (WPRB)and a given amount of PRBs at the BS (NPRB). Moreover,a RNO could give more priority to the UE experiencing thebest channel conditions in order to maximize the throughput.The distribution of bandwidth resources becomes importantespecially when the resources are limited. When an energysaving strategy is applied and the network capacity is reducedby putting a set of BSs in sleep mode, each served UE shouldbe guaranteed a level of QoS.

Bandwidth assignment strategy has an impact also on theenergy saving potentials. Different policies can result in differ-ent number of BSs to be put in sleep mode when bandwidthis shared among a set of UEs. When the amount of PRBsis equally shared among UEs, the rate provided to the UEsexperiencing better channel conditions will be higher than theothers. Alg. 3 and Alg 4 present a possible solution to assignbandwidth in order to achieve fairness among UEs in termsof QoS, i.e., user data rates. In particular, Alg. 3 assigns justthe needed amount of bandwidth allowing each UE to reachthe target QoS. On the other hand, Alg. 4 equally splits theresidual PRBs in order to increase the UE performance. Notethat both algorithms converge to the same behaviour for highnumber of connected UEs or weak channel quality.

In this paper the focus is not on bandwidth assignmentstrategies but on the evaluation of the impact of the bandwidthmanagement on the power savings.

C. Network energy optimization guaranteeing QoS

In order to introduce a higher power saving in cellularnetworks, a new optimization framework is proposed. Theproblem is iteratively solved for three variables: associationbetween BS and UE, bandwidth assignment and power allo-cation. In particular, an opportune BS-UE association allowssaving power by increasing the number of BSs that are notserving traffic and that can be deactivated or put into sleepmode. By joint bandwidth and power allocation a further gainis obtained by reducing transmission power and decreasing theintercell interference. BSs deactivation and power reductionare allowed only if no outage is introduced, i.e. the targetQoS is satisfied for each served UE.

The optimization framework is composed of (i) MIQPsolver to optimize the UE to BS mapping and the active BSsset, (ii) bandwidth allocation scheme and (iii) power controlalgorithm which is used to control the feasibility of the UEto BS mapping found in (i) and to identify the outages. The

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MIQP optimization refers to the problem formulated in (5a)-(5h). The output of this step is the mapping xij between BSsand UEs, the power transmitted by each BS to each connectedUE πij and the set of active BS ζi. Then in the second step thebandwidth is allocated to each UE by the respective servingBSs. The bandwidth allocation is performed following Alg. 2,Alg. 3 or Alg. 4.

The MIQP model is solved by IBM ILOG CPLEX R©solver.Since the model cannot manage directly the QoS for each UEbecause of its non-linearity, two approaches are proposed in or-der to avoid outages. These approaches are (i) Power consump-tion minimization assuming an interference controlled scenario(MinPower); (ii) Iterative power consumption minimizationto guarantee QoS (MinPower-QoS). The MinPower scenarioassumes a good planning or a perfect intercell interferencecancellation (ICIC) solution, but it could be also the referencecondition for rural areas. In that case the rate of each useris only dependent on the signal to noise ratio (SNR), sothe only interesting variable is the power received by theserving BS. If interference cannot be neglected, MinPower,as shown in Alg. 5 cannot guarantee the required quality ofservice and some outages could arise. For that reason, thisalgorithm represents an optimum lower bound for the networkoptimization in terms of global power consumption. In orderto avoid the datarate outages MinPower-QoS is introduced.MinPower-QoS is presented in Alg. 6 and combines theoptimum power control and the MinPower approaches in aniterative framework. In particular MinPower is executed inorder to obtain the optimum set of active BSs, the optimummapping and the bandwidth assignment and minimize thepower consumption, while the feasibility of this solution iscontrolled by the Power Control as shown in Alg. 6. If somedatarate outages occur, the received power of the users whichdo not satisfy the target QoS is iteratively increased by a δvalue in order to select a better mapping and active BSs set.

V. SIMULATION RESULTS

In order to evaluate the power consumption savings dueto the base station sleep mode introduction and the trans-mission power adaptation, the proposed solution, namely theMinPower-QoS is compared to upper bound and lower boundsolutions. As an upper bound, Closest BS mapping follows theAlg. 1 based on power control with closest BS mapping andequal bandwidth assignment to each UE. BSs which are notserving any UE are put into sleep mode. As a lower bound,the optimum solution MinPower is used. Moreover, referringto MinPower-QoS, different bandwidth allocation strategieshave been considered in order to show the bandwidth effecton the power optimization. Therefore MinPower-QoS eq bwfollows the bandwidth allocation solution described in Alg.2, i.e. bandwidth is equally divided among UEs; MinPower-QoS no res follows Alg. 3, i.e. UEs experiencing the worstchannel conditions are assigned more bandwidth blocks. Afterachieving the target rate requirements for each UE, the residualPRBs are not assigned; MinPower-QoS res follows Alg. 4, i.e.residual PRBs are equally split among UEs allowing higher

data rates than what is required. Simulation parameters arereported in Table I [16]. For the sake of simplicity omni-directional antennas have been considered instead sectoredsites. The considered sleep mode power value is in line withevaluations done in [17]. In each iteration of MinPower-QoS,if a UE cannot achieve the datarate target, minimum receivedpower threshold, PMINj in constraint (5e) is increased byδ = 1 dB and the MIQP model is solved for this new setting.The number of active UEs that are randomly placed in theconsidered playground has been set variable from 5 to 230UEs. The maximum value has been chosen according to themaximum number of UEs that can be managed by the ClosestBS mapping solution without any capacity outage, which givesthe maximum load of the cellular network.

The results are obtained by statistical analysis considering50 simulation runs and a 95% confidence interval. Fig. 1(a)presents the comparison of the number of active BSs forMinPower-QoS with respect to the other solutions, i.e., ClosestBS mapping and MinPower. From the figure the behaviourof proposed solution is evident: if it is feasible, for eachnumber of active UEs the minimum power configurationis selected; note that the number of active BSs is almostlinearly dependent on the number of UEs. Because of theQoS requirements, the slope of MinPower-QoS is higher thanMinPower. Finally, the MinPower-QoS converges to ClosestBS mapping when the number of active users increases. Thisresult is reinforced by Fig. 1(b) where total power consumptionis depicted versus the number of users. It is interesting tosee that in the interference limited scenario under high load,all BSs are activated but MinPower-QoS can still have powersavings over the Closest BS mapping due to its flexibilityin user association and bandwidth distribution. Moreover, theperformance of MinPower-QoS is close to the global optimumwhich is represented by MinPower when the number of usersis very low.

More in detail, the bandwidth impact in MinPower-QoSoptimization can be highlighted. The solutions assigning thebandwidth prioritizing UEs with lower signal quality, i.e.MinPower-QoS no res and MinPower-QoS res, perform betterfrom the power consumption perspective. Such a behaviourcan be explained by a more flexible management of the radioresources that allows a lower number of active BSs and alower transmission power. In Fig. 1(c) the UE satisfaction rateis depicted starting from optimum MinPower solution as firstiteration. From this figure, it is possible to note the impact ofthe number of active UEs on the number of required iterationsto reach 0 outages. Finally, it is possible to see that, whileMinPower experiences outages, MinPower-QoS converges to0 outage performance after a certain number of iterationsdepending on the number of UEs. Each iteration correspondsto a solution in the search space for the MIQP model whichstops when it reaches a minimum power solution with 0outages. The average transmission power vs the number ofiterations is depicted in Fig. 1(c). Active UEs are satisfiedwhen mapping and received power allow to reach the targetQoS. Among the feasible solutions, the one minimizing the

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(a) (b) (c)

Fig. 1. Simulation results: (a) active BSs vs number of UEs; (b) global power consumption vs number of UEs; (c) satisfied UEs and average transmissionpower per active BS vs number of iterations in MinPower-QoS

global power consumption is chosen. Particularly, even if theproper mapping is obtained by increasing the BS transmissionpowers, this trend can also be harmful for some UEs atthe cell edge because of the interference level. Therefore,the BSs can also be switched on, reducing the total powerconsumption together with the interference caused by veryhigh transmission power.

The fluctuations in Fig. 1(c) are not related to any optimiza-tion variable and the algorithm stability is not affected by thisresult. On the other hand a lower number of UEs requires lessiterations to reach the solution.

VI. CONCLUSION

In this paper, a novel optimization framework is developedwhich is aimed at minimizing the power consumption withguaranteed QoS. The results for interference limited scenariosand different traffic loads are shown. By putting the cellsin sleep mode, up to 60% power savings can be achievedwith respect to the basic scheme. Moreover, the proposedMinPower-QoS methodology affords performance very closeto the optimum solution, particularly for low traffic loadscenarios. This study demonstrates the potential savings bylong term sleep in a multi cell scenario. Significant savingshappen when the traffic is below 35% of the maximum load.Above that level, in medium to high traffic load, other energyefficiency features can be preferred instead of deep sleep.Combination of different energy saving features is consideredas a future work.

REFERENCES

[1] “Cisco visual networking index: Global mobile data traffic forecastupdate, http://www.cisco.com/.”

[2] F. Richter, A. Fehske, and G. Fettweis, “Energy efficiency aspects ofbase station deployment strategies for cellular networks,” in VehicularTechnology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th, 2009,pp. 1–5.

[3] M. Marsan, L. Chiaraviglio, D. Ciullo, and M. Meo, “Switch-off tran-sients in cellular access networks with sleep modes,” in CommunicationsWorkshops (ICC), 2011 IEEE International Conference on, June 2011,pp. 1–6.

[4] P. Frenger, P. Moberg, J. Malmodin, Y. Jading, and I. Godor, “Reducingenergy consumption in LTE with cell DTX,” in Vehicular TechnologyConference (VTC Spring), 2011 IEEE 73rd, May 2011, pp. 1–5.

[5] T. Chen, Y. Yang, H. Zhang, H. Kim, and K. Horneman, “Networkenergy saving technologies for green wireless access networks,” WirelessCommunications, IEEE, vol. 18, no. 5, pp. 30–38, October 2011.

[6] S. Videv and H. Haas, “Energy-efficient scheduling and bandwidth-energy efficiency trade-off with low load,” in Communications (ICC),2011 IEEE International Conference on, June 2011, pp. 1–5.

[7] L. Venturino, C. Risi, A. Zappone, and S. Buzzi, “Green joint userscheduling and power control in downlink multi-cell OFDMA net-works,” in Future Network and Mobile Summit (FutureNetworkSummit),2013, July 2013, pp. 1–10.

[8] C. Y. Wong, R. Cheng, K. Lataief, and R. Murch, “Multiuser OFDMwith adaptive subcarrier, bit, and power allocation,” Selected Areas inCommunications, IEEE Journal on, vol. 17, no. 10, pp. 1747–1758, Oct1999.

[9] A. Yildiz, T. Girici, and H. Yanikomeroglu, “A pricing based algorithmfor cell switching off in green cellular networks,” in Vehicular Technol-ogy Conference (VTC Spring), 2013 IEEE 77th, June 2013, pp. 1–6.

[10] H. Holtkamp, G. Auer, S. Bazzi, and H. Haas, “Minimizing base stationpower consumption,” Selected Areas in Communications, IEEE Journalon, vol. 32, no. 2, pp. 297–306, February 2014.

[11] X. Xiao, X. Tao, and J. Lu, “QoS-Guaranteed Energy-Efficient PowerAllocation in downlink multi-user MIMO-OFDM systems,” in Commu-nications (ICC), 2014 IEEE International Conference on, June 2014,pp. 3945–3950.

[12] Z. Ren, S. Chen, B. Hu, and W. Ma, “Energy-Efficient ResourceAllocation in Downlink OFDM Wireless Systems With ProportionalRate Constraints,” Vehicular Technology, IEEE Transactions on, vol. 63,no. 5, pp. 2139–2150, Jun 2014.

[13] M. Olsson, C. Cavdar, P. Frenger, S. Tombaz, D. Sabella, and R. Jantti,“5GrEEn: Towards Green 5G mobile networks,” in Wireless and MobileComputing, Networking and Communications (WiMob), 2013 IEEE 9thInternational Conference on, Oct 2013, pp. 212–216.

[14] G. Auer, V. Giannini, C. Desset, I. Godor, P. Skillermark, M. Olsson,M. Imran, D. Sabella, M. Gonzalez, O. Blume, and A. Fehske, “Howmuch energy is needed to run a wireless network?” Wireless Communi-cations, IEEE, vol. 18, no. 5, pp. 40–49, October 2011.

[15] J. Zander, S.-L. Kim, M. Almgren, and O. Queseth, Radio ResourceManagement for Wireless Networks. Norwood, MA, USA: ArtechHouse, Inc., 2001.

[16] “Further advancements for E-UTRA physical layer aspects v.9.0.0,”3GPP TR 36.814, 2010.

[17] K. Hiltunen, “Utilizing enodeb sleep mode to improve the energy-efficiency of dense lte networks,” in Personal Indoor and Mobile RadioCommunications (PIMRC), 2013 IEEE 24th International Symposiumon, Sept 2013, pp. 3249–3253.

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