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Abstract— In this paper, we propose a switch on/off algorithm for Base Stations (BSs), which exploits the knowledge of the distance between the User Equipments (UEs) and their associated BS. Our novel approach hopes to provide an improvement to the problem of energy consumption. The major concern lies on reducing the energy consumption of the telecommunication networks by optimizing the power utilization without sacrificing the offered Quality of Service (QoS). Our proposed scheme achieves a significant power saving, based on switching off the Base Stations that are underutilized during low traffic periods (especially during night) in the LTE-Advanced.

Index Terms— Energy-efficient, LTE-Advanced, Green Communication, Base Station Switching On/Off

I. INTRODUCTION ONG TERM EVOLUTION (LTE) is a new cellular standard formally submitted as a candidate Fourth Generation (4G)

system by 3GPP. LTE-Advanced, in turn, refers to the most advanced version of LTE [1].

The LTE-Advanced architecture is typically composed of three interactive domains: (i) the UEs, (ii) the Evolved Universal Terrestrial Radio Access Network (E-UTRAN), and (iii) the core network, which is called Evolved Packet Core (EPC). The EPC comprises several functional entities. The Mobility Management Entity (MME) is responsible for the control plane functions related to subscriber and session management. The Serving Gateway (SG) is the anchor point of the data interface towards E-UTRAN. Moreover, it acts as the routing node towards other 3GPP technologies. The Packet Data Network Gateway (PDNG) is the termination point for sessions towards the external packet data network. The Policy and Charging Rules Function (PCRF) controls the tariff making and the IP Multimedia Subsystem (IMS) configuration of each user [2].

Green Information and Communication Technology (ICT) has gained significant traction in the last recent years. The main catalyst for this change is the impact of ICT on the earth's climate and the fact that energy consumption is growing at a staggering rate. ICT is responsible for a percentage which varies between 2% and 10% of the annual world-wide energy consumption [3]. Since telecommunication networks are growing fast in exponential values, energy consumption of ICT is expected to increase in fast rates. In particular, since the BS, called evolved NodeB (eNB) in LTE-

Advanced, is the principal entity of ICT power consumption [4], it is clear that reducing the power consumption of BSs is of highest importance.

In the context of BSs planning, several works have been proposed in literature. In [5], [6], [7] different approaches for switching off a specific number of BSs in UMTS cellular networks during low traffic periods are presented. In [5], Chiaraviglio et al. switch off a random number of BSs and the energy saving is computed by means of simulations for UMTS cellular networks. In [6], the same authors provide an improvement of their former work. They propose a dynamic network planning for switching on/off the BSs, considering a uniform and a hierarchical scenario. In another work [7], Marsan et al. show how to optimize energy saving, by assuming that any fraction of cells can be switched off according to a deterministic traffic variation pattern over time. In addition, in [8], two approaches that achieve energy saving are proposed: (i) a greedy centralized algorithm where each BS is examined according to its traffic load and it is determined whether it is going to be switched off or not, and (ii) a decentralized algorithm where each BS locally estimates its traffic load and decides independently whether it is going to be switched off or not. Gong et al. [9] propose a dynamic switch on/off algorithm based on blocking probabilities. The BSs are switched off according to the traffic variation with respect to a blocking probability constraint. The authors take into account the fact that the BSs should stay in active or sleeping mode for a minimum time. Apart from the previous studies that propose switch on/off algorithms for UMTS cellular networks, simple analytical models are also introduced for reducing the power consumption in Wireless LANs (WLANs) [10], [11]. In particular, in [10] a cluster is responsible for deciding which BS should be switched off by estimating the associated users and the traffic load of each BS. In the same context, a switch on/off approach for dense WLANs based on the number of associated UEs to each Access Point (AP) is presented in [11].

In all the aforementioned works, the main focus is the energy aware management of cellular access networks, mainly UMTS and WLANs. The goal is to decrease the energy consumption by reducing the number of active BSs. The BSs are switched off during low traffic periods when their presence is not essential for the proper operation of the

"Green" Distance-Aware Base Station Sleeping Algorithm in LTE-Advanced

Alexandra Bousia1, Angelos Antonopoulos2, Luis Alonso1, and Christos Verikoukis2 1Signal Theory & Communications Dept., Technical University of Catalonia, Barcelona, Spain

email: {alexandra.bousia, luisg}@tsc.upc.edu 2Telecommunications Technological Centre of Catalonia, Barcelona, Spain

email: {aantonopoulos, cveri}@cttc.es

L

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network. The decision of which BSs should be switched off is either random (e.g. half of the BSs are switched off), or based on the traffic load of each BS (e.g. the BSs with the lowest traffic load are switched off).

The contribution of our work is twofold: (i) we consider the distance between the UEs and their associated eNBs in order to minimize the energy consumption of the whole network, and (ii) we deal with the LTE-Advanced Standard. The energy saving problem is studied by switching off some eNBs under low traffic conditions.

The rest of the paper is organized as follows. In Section II we describe our system model, and the adopted network traffic profiles. Section III introduces our proposed switch on/off algorithm. The numerical results for the energy saving are provided in Section IV, while Section V concludes the paper.

II. THE SYSTEM MODEL We consider a dense deployed network, where the coverage

areas of neighboring cells overlap, as it is shown in Fig. 1. Our network consists of a set of K cells that have the same coverage radius R and traffic load that has the periodic day/night pattern (Fig. 2).

Figure 1. Network Topology

We consider a traffic generation model based on a M/M/N

queue, which represents the traffic flows between the UEs and the eNBs in our telecommunication network. The model relies on the following assumptions: (i) eNBs generate traffic according to a Poisson process with mean λ; (ii) traffic service time follows an exponential distribution with mean 1/μ. Despite the simplifications that have been made in the traffic model, it is still an adequate and realistic representation of the traffic in order to estimate the amount of energy saving that can be obtained with energy-aware planning.

The call generation rate has the typical periodic night/day pattern, such as the one reported in Fig. 2 [5]. The traffic function )(tf has the simple sinusoidal shape, which we assume that corresponds to the day/night pattern. The night zone extends from 8pm to 8am. We assume that the daily traffic profile repeats periodically, and the average traffic per user in all access networks is the same, so that the overall traffic is proportional to the respective number of users.

Figure 2. Traffic generation rate vs time Regarding the eNB power consumption in a cell, we know

that the total operating energy of an eNB is composed of two parts: a constant part constE covering the energy that is independent of the number of users and their distances (e.g. power for cooling, power supply, etc.) and a dynamic part dynE which corresponds to the energy consumption for

the radio operation. The dynamic part consists of two parts:

transE , is the energy during for transmission which depends on the number of users and their distance from the associated eNBs, and idleE that corresponds to the energy consumed when eNB is in idle state.

The total operating energy is given in the following equations. For a single cell, with one eNB that serves N users, the total energy consumption is:

dynconsttotal EEE �� )1(

onconstconst tPE �� )2(

� � idleidle

N

iitransitransdyn tPtPE �����

�1,, )3(

Where,

constP is the power consumption of the eNB when switched on,

ont is the total time that the eNB is switched on,

itransP , is the power consumed during a transmission to the

UE i,

itranst , is the time that the eNB transmits data to the UE i,

idleP is the power consumption of the eNB when idle,

idlet is the total time that the eNB is idle. The transmission power can be calculated as [12]:

3

�

��

��

� �

���

�

���

����

����

��� �

aiilex

RBitrans L

LR

NPP ,

minmax, ,max,1min )4(

iiilex RlL 10, log6.37 ���� )5(

Where,

maxP is the maximum transmission power,

RBN is the number of subcarriers assigned to the UE,

minR is the minimum power reduction rate to prevent UEs with good channels to transmit at very low power level,

iilexL ,� is the x-percentile pathloss (plus shadowing) value

for the user i. If x is set to 5 , then statistically 5% of UEs with bad channels will transmit at maxP . The pathloss and the shadowing depend on the distance of each UE.,

10 �� a is the balancing factor for UEs with bad channels and UEs with good channels,

L is the pathloss, l is a constant that incorporates the fast fading and the

shadowing of the channel,

iR is the distance between UE i and eNB. We further assume that when we switch off one of the

eNBs, its traffic load should be served by a neighbor eNB that remains active. Therefore, the total energy consumption of the eNB, that remains switched on is:

���� totalnewtotal EE _ )6(

��

��

�

�

��

�

��

�

��

��

� �

���

�

���

����

��� ��

����N

iitrans

ai

RB

tL

RlRN

P1

,10

minmaxlog6.37

,max,1min

)7( Where,

totalE is the energy consumption of the eNB taking into

account only its traffic and its constE

�� is the extra energy consumed due to the additional traffic of the switched off eNB.

The equations (6) and (7) inherently imply the effect of the

distance between the eNB and the UEs that are now associated because of the switch on/off algorithm.

III. THE PROPOSED SWITCH ON/OFF ALGORITHM In this section, we describe the proposed eNB switch on/off

scheme that reduces the power consumption. The system is designed to operate in order to satisfy the

QoS constraints under off-peak traffic load periods. Hence, the underutilization of the eNBs during low traffic periods leads to a significant energy waste. Our objective is to switch off some eNBs when the traffic load is low, and our intention is to decide the number of the eNBs to switch off and the conditions under which the switch on/off scheme is effective.

The decision is very crucial and we have to take into account that the QoS should not be degraded, the cell coverage should not be reduced and the network operations must be stable. When we switch off some eNBs, the coverage is restricted, and thus, some users are in the outage. Consequently, we must provide radio coverage to the parts that were covered by the switched off eNBs. In order to achieve increased coverage, we must increase the transmission power. The transmission power depends on the distance of the UE, as it is demonstrated in equations (4) and (5). As a result, the longer the distance between the UE and the eNB is, the greater the transmission power we use [13], [14]. Hence, the impact of the distance on the transmission power, the propagation and the pathloss makes it an appropriate indicator in the decision about which and how many eNBs have to be switched off.

Summarizing from the above, the proposed idea is to switch off an eNB not according to its traffic load, but according to the average distance of its users. Therefore, each eNB should estimate the distance of its UEs. Then each eNB should also estimate the distance of the UEs of its neighboring eNBs, after exchanging the necessary information through X2. Each eNB calculates the average of the above distances. Greater average distance leads to greater average transmission power. Our algorithm proposes to switch of the eNB with the maximum average distance value because this eNB would increase its transmission power in a greater value if it was switched on.

Our algorithm leads to power saving, but it is important that it guarantees the QoS. The QoS refers to the outage probability of the UEs and throughput. The outage occurs when there is no eNB to serve the traffic of a switched off eNB. Our proposed scheme deals with the reduction of outage probability by maintaining the cell coverage of the network. The outage probability must be almost zero. Although we switch off some eNBs, the remaining eNBs are responsible for covering the parts of the networks that were covered by the switched off eNBs. In addition, before switching off an eNB, we first ensure that the remaining eNBs can serve the traffic of the network. We do not switch off any further eNBs if those that remain on are not able to serve the existing traffic at the present time in the network.

The proposed eNBs switch on/off algorithm works as follows (Fig. 3): Step 1: Each eNB estimates the distance of its UEs and obtains the information for the distance of the UEs that are associated with its neighbors through the X2 interface. Step 2: The eNBs calculate the average distance of the traffic load based on the results of the first step. The eNBs are ranked based on the estimated average and they are examined from the top one with the maximum average distance value. Step 3: The first eNB is switched off, if there is no QoS degradation, and the neighboring eNBs deal with the possible increases in the transmission power. Step 4: The algorithm continues with the next eNB in the list, until the maximum number of eNBs is switched off. As the eNBs are switched off, we should guarantee that there is no QoS degradation.

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Estimation of the distance between each eNB and its UEs and between

each eNB and the UEs of its neighboring eNBs

Start

Calculation of the average distance of the traffic load

Transmission power increase of the neighboring eNBs

Switch off eNB

Power saving calculation

End of switch on/off algorithm

Switching off the 1st eNB of the list leads to

QoS degradation?

No

Yes

End

eNBs ranking in descending order according to the average distance

Figure 3. Switch on/off scheme

IV. PERFORMANCE EVALUATION

A. Simulation Scenario In the previous section, we explained the proposed energy-

aware algorithm to dynamically switch off the eNBs. Here, we study how our scheme is applied to a given network configuration.

For our experiments, we consider a typical urban scenario, similar to the one presented in Fig. 4. The network is composed of 25�K cells, where the distance among the eNBs is 800 meters [15]. The eNBs are deployed in an orthogonal grid, while each eNB has to serve the same number of users (10 UEs on average). For the calculation of the power consumption, we consider the power that is consumed in the eNBs for the downlink transmission. Models and simulation assumptions are selected according to the 3GPP evaluation criteria [15] (summarized in Table I). Control channels are assumed to be error-free. In each iteration of the simulation, UEs are placed in the system area based on a uniform distribution and the eNBs generate traffic according to a Poisson distribution. The packet size we use for the simulation is 1500 bytes. The radio channel between each eNB and UE pair is calculated according to the propagation and fading models.

In our simulations, we compare our proposed algorithm to a switch on/off scheme, where half of the eNBs are randomly switched off [5].

We present an example in order to see how the two algorithms behave. We consider the following scenario, as it is shown in Fig. 4. The case study is based on an outdoor wide area cellular network scenario, where the coverage areas of the eNBs overlap. We assume that we have four identical eNBs with the same cell coverage and the same traffic load.

The eNBs are located at the center of each cell.

Figure 4. Example for the proposed algorithm

When we apply the random switch on/off scheme, we

assume that eNB1 is switched off based on a random decision. However, according to our proposed algorithm, we select to switch off eNB3, since the UEs of eNB1 are gathered near the cell edge far from eNB2, eNB3, and eNB4. Apparently, the random decision leads the eNBs to considerable transmission power increase in order to cover the whole area and to serve the traffic load of the switched off eNB. On the other hand, the proposed distance-aware scheme selects intelligently the eNB to be switched off, thus succeeding the minimum transmission power increase.

Table I. Simulation Parameters based on LTE-Advanced

Traffic Model User distribution Uniform λ Variable: 0.03-0.15 calls/sec

(Fig.2) μ 1/50 calls/sec Radio Network Model Distance attenuation RlL ilex 10log6.37��� , R is

the distance in meters ( 3.35�l ) Inter site distance 800 m Penetration loss (L) 20 dB System Models Maximum UE output power

250 mW (24 dBm)

Scheduling Random scheduling with 10 users BS total TX power 46 dBm BS idle power 0.19 dBm Power Control Path-loss Compensation

α=0.8

B. Simulation Results In the following figures, we show the actual performance of

different energy saving approaches under a realistic daily traffic profile.

Figure 5 shows the average power consumption of our proposed scheme compared to the random algorithm and the case that all eNBs remain active. Figure 6 represents the percentage of saving in the power consumption during night. We assume that every eNB can be switched off for about 12 hours, saving 29% of power consumption in a day. Comparing to the random switch on/off algorithm, our proposed scheme

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achieves up to 10% better performance. Figure 7 depicts the energy efficiency accomplished by our proposed scheme. For the energy efficiency metric we calculate the number of bits that are delivered per energy unit. The energy efficiency metric is appropriate for our network because both the numerator (the number of delivered bits) and the denominator (the network energy consumption) are typically variable. Thus, the efficiency metric is more convenient for networks with low traffic load.

Figure 5. Average power consumption achievable when

half of the eNBs are switched off vs. time

Figure 6. Maximum % power saving vs. time

Figure 7. Energy efficiency vs. time

V. CONCLUSION In this paper we studied the energy consumption problem.

We proposed a smart strategy in order to minimize the energy consumption in a given network, without compromising the

offered QoS. We introduced a distance-aware algorithm that achieves a significant reduction in power consumption. In particular, we proved how important is to efficiently choose the eNBs to be switched off during low traffic periods, by considering not the distance of the UEs from the eNB. Our results indicated that we can save up to 29% of the power consumed to operate the network, by decreasing the number of the active eNBs during low traffic periods. In our future work, we plan to elaborate on models that will achieve greater energy efficiency.

ACKNOWLEDGEMENTS This work has been funded by the Research Projects GREENET (PITN-GA-2010-264759), CO2GREEN (TEC2010-20823) and GEOCOM (TEC2011-27723-C02-01). GREENET is funded by the EU and GEOCOM by the Spanish Governement.

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