Rushing Full Speed with LTE-Advanced isEconomical - A Power Consumption Analysis

Robert Falkenberg, Benjamin Sliwa and Christian WietfeldCommunication Networks Institute

TU Dortmund University44227 Dortmund, Germany

Email: {Robert.Falkenberg, Benjamin.Sliwa, Christian.Wietfeld}@tu-dortmund.de

Abstract—Boosting data rates in LTE mobile networks isone of the key features of LTE-Advanced. This improveduser experience is achieved by Carrier Aggregation (CA), inwhich the available spectrum of an operator is bundled outof several frequency bands. Accordingly, the user equipmenthas to supply multiple reception chains and therefore consumesconsiderably more power during a transmission. On the otherhand, transmissions terminate faster which enables a quickswitchover into energy-saving mode. In order to examine theseopposed facts, empirical analyses of existing devices are firstcarried out. Subsequently, we present a new CA enhancementof an existing context-aware power consumption model whichincorporates the development density of the environment andthe mobile device mobility. Based on the extended model weperform a detailed power consumption analysis and show thatCA leads to power savings of 31 % if the data rate doubledfor large file transmissions. In addition, we show that CA canlead to power savings even from a data rate increase of 25 %,regardless of mobility and urban development density. Besides,the measurement results show that CA operated in the sameband leads to a lower power consumption than inter-band CA.

I. INTRODUCTION

The ongoing evolution of Long Term Evolution (LTE) mobilenetworks increases the achievable data rate over the radio link.A prominent example is the Carrier Aggregation (CA) feature ofLTE-Advanced (LTE-A) networks in which, as shown in Fig. 1,the data is transmitted simultaneously over multiple distributedfrequency bands possessed by a single network operator inorder to boost the data rate of the connection. While datarates increased in the recent years, in spite of more and moreefficient system-on-chips and new power-saving techniques, thebattery lifetimes of mobile devices did not. This is caused bythe growing demands on the provided content: the resolutionof video streams increases, websites and documents containmore complex graphical components, and the amount of datawhich is being stored and processed by cloud services growscontinually. As the lion share of today’s smartphone traffic isissued in the downlink due to streaming and downloading webcontent, operators in Europe began to roll out LTE-A networkssupporting CA in the downlink.

For an LTE-A modem the reception of multiple carriersrequires powering a corresponding number of receive chainsand additional signal processing. At this point arises thequestion how the power consumption of an LTE-A device isaffected by CA. On the one hand, as illustrated in Fig. 1, CAleads to an increased power draw within the User Equipment(UE). On the other hand, the data rate is significantly boosted,hence allowing a faster termination of transmissions.

The objective of this work is to analyze the power consump-tion of today’s LTE-A devices and to utilize this empiricalknowledge for consumption simulations in numerous usagepatterns, environments, and mobility scenarios, e.g. for amoving vehicle. This allows the identification of situationswhere CA reduces the power draw of UE and thereforeincreases the battery lifetime of mobile devices.

For this purpose, an established power consumption modelwill be extended for downlink CA in Sec. III. Afterwards,Sec. IV covers empirical measurements with existing LTE-Adevices followed by various simulations in Sec. V which arebased on the gained values and the proposed model extension.The paper closes with a brief summary and conclusion on theresults.

II. RELATED WORK

The power consumption of LTE equipment has been mea-sured, modeled and analyzed in numerous studies. In [1]a power consumption model for downlink and uplink ispresented based on empirical measurements. It shows thatthe power consumption for the uplink is mostly affected bythe transmission power rather than by the uplink data rate.Complementary, the power draw invoked by downlink receptionmostly depends on the data rate rather than received signalpower. The most complex procedure in the reception process isthe iterative turbo decoding whose complexity linearly scaleswith the data rate. This fact was confirmed in [2] as thedownlink power consumption induced by the turbo decoder of

Frequency

Sig

nal

Frequency

Sig

nal

Single CarrierReception

Multiple CarrierReception

P Mbps

Power Speed

P Mbps

Power Speed

LTELTE-Advanced

Carrier Aggregation

Fig. 1. Functional Principle of Carrier Aggregation and its Influence on DataRate and Power Consumption

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newer devices linearly grows by 5 % if the number of allocatedResource Blocks (RBs) increases by factor 10. In case of anLTE cell with multiple UEs claiming for a large number ofradio resources (e.g., large file transfers) it is energy efficientto share the available RBs in time rather than in frequency [3].By doing so, single UEs process larger chunks of data for ashort time and save energy in the remaining period instead ofpowering the decoder continuously.

Moreover, the authors of [2] attend on the energy-efficiencyevolution in the past few years. They point out a reductionof transmission-power consumption by 35 % at low andintermediate output power levels as well as for the entirereception process. Besides, the uplink power consumption ofLTE UE is independent from the actual uplink data rate.

In [4] an empirical analysis of the current consumption offirst-generation LTE-Advanced devices has been performed.The authors have measured power savings of 13 % by enablingCA for large File Transfer Protocol (FTP) downloads. Theauthors of [5] were able to confirm the same effect by theirmeasurements. Due to restrictions of the Device Under Test(DUT) in both studies, no intra-band CA could be analyzed,so our measurements will close this gap.

Since laboratory measurements typically lack of generalityand field measurements often cannot be performed in therequired extent, a context aware power consumption model hasbeen developed in [6] to perform representative simulationsof the UE’s power consumption. The model considers theradio channel (static, fading), environment (urban, rural), datasize, arrival rate, UE power consumption in different operationmodes, as well as further parameters. From these parameters themodel calculates the average long-term power consumption ofthe UE. The development and evaluation of the model showedthat the transmission of data in uplink direction is the mostenergy-intensive part of an LTE modem – especially at hightransmission power.

Furthermore, the model has been extended for LTE-A uplinkanalysis [7] and analyzed in theory. To the best of ourknowledge the model has not been adapted for an LTE-Adownlink analysis, which will be proposed in this paper.

III. POWER CONSUMPTION MODEL

Performing representative simulations on the impact of CAon the UE’s power consumption requires an exact power modelof the analyzed device as well as an incorporation of externalinfluencing factors like the environment and mobility. For thisreason the proposed power consumption model for downlinkCA is based on the excessively studied Context-Aware PowerConsumption Model (CoPoMo) presented in [6]. In order tofacilitate a better understanding of the presented approach,an introduction to the underlying model is given in the nextsection. For full details the reader is kindly referred to the fulldocumentation in [3] and [6].

A. Uplink Power Consumption ModelOne central statement of CoPoMo is the outcome that the

amount of power an LTE modem requires is highly dependingon its transmission power and the time spent in this mode.The authors have reduced the complexity of uplink powerconsumption to four main power states (Idle, Low, High, Max)

1Idle

2Low

3High

4Max

λ2 λ3 λ4

µ2 µ3 µ4

Fig. 2. Markovian Power State Model of LTE User Equipment

TABLE IPROPERTIES OF THE SIMULATED ENVIRONMENTS

ACCORDING TO [6] IF NOT NOTED OTHERWISE

Environment Inter Site DistanceUrban 500 m [8]Suburban 1732 m [8]Rural 5000 mChannel model MobilityAdditional White Gaussian Noise (AWGN) 0 km/hPedestrian 3 km/hVehicular 60 km/hFrequency Band Bandwidth800 MHz (Band 20) 10 MHz2600 MHz (Band 7) 10 MHz

a UE may enter according to the current radio conditionswhile still maintaining a very high model accuracy. Eachstate i is characterized by an average power consumption Piobtained from empirical measurements of the actually analyzeddevice (cf. IV). Furthermore, downlink transmissions withoutany uplink component result in entering the Low power stateregardless of the radio conditions. Those states, in conjunctionwith corresponding transition rates, build the midpoint of thepower model and can be expressed as a Markov chain asshown in Fig.2. Modeling the idle time between two uplinktransmissions as a negative exponential distributed function withmean 1/λ leads to the leaving transitions from Idle state withthe rate λ. The particular rates λi are the result of weightingλ with individual power state probabilities ϑi:

λi = ϑi · λ for i ∈ {2, 3, 4}, (1)4∑

i=2

ϑi = 1. (2)

They reflect the probability that the device must transmit ata specific transmission power to achieve the required Signalto Noise Ratio (SNR) at the addressed base station under thegiven radio conditions. The actual values of ϑi depends on thecell environment, e.g., urban or rural area, the UE’s mobility,e.g., pedestrian or vehicular movement, and finally the utilizedcarrier frequency. In this paper, and according to [6], ϑi isderived from ray tracing simulations and channel emulations ofthree environments, three mobility patterns, and two commoncarrier frequencies as listed in Tab. I.

The time spent in any active state 1/µUL,i is derived fromthe channel dependent uplink throughput in each state RUL,iand the average upload file size DUL:

µUL,i =RiDUL

for i ∈ {2, 3, 4}. (3)

Especially in the Max state, where the maximum transmissionpower is limited to 23 dBm [9] and the UE must fall back tomore robust Modulation and Coding Scheme (MCS), RUL,4may be significantly lower than in the other modes. The modelconsiders this rate degradation according to empirical studiesperformed in [10] and [11], which leads to a longer residencetime in Max state.

By deriving the equilibrium equations for the Markov modelin Fig. 2 and solving the linear equation system we get thestate probabilities pi with

pi =

1

1+4∑

j=2(λj/µj)

for i = 1,

λi/µi

1+4∑

j=2(λj/µj)

for i 6= 1.(4)

With this state probabilities and the average power consumptionin each state Pi the average long term power consumption ofthe UE can be expressed as

PTotal =

4∑

i=1

Pi · pi. (5)

B. Model Extension for Downlink and Carrier Aggregation

In order to apply CoPoMo to mixed uplink/downlink trans-missions and carrier aggregation the model must be extended.The necessary modifications will be explained in this subsection.A timeline of an exemplary uplink transmission according toclassic CoPoMo and Fig. 2 is shown in the upper part ofFig. 3. It shows the residence time in each state according tocalculated long-term state probabilities from Eq. 4. Althoughthe model explicitly does not include transitions between twodistinct transmitting states (cf. Fig. 2), those states are stillgrouped next to each other in the figure. This is legitimate,because the power consumption of a device is assumed to beindependent of the order the states are traversed. Therefore,only the average time spent in each state must be maintained.

To extend the power model for downlink transmissions thefollowing assumptions were made:

• The data rates of the uplink RUL and the downlink RDL areaffected by the radio channel in the same way. Therefore,the relation between the two rates rDLUL is fixed torDLUL = RUL/RDL.

• Realistic downlink transmissions typically contain a frac-tion of uplink data, e.g. requests and acknowledgments.The relation of downlink data size DDL and uplink datasize DUL is described by dDLUL = DUL/DDL.

• The arrangement of uplink transmissions during an ongo-ing download has no effect on the power consumption.It is assumed, that no additional time is spent in tailstates [3].

• Downloading data during an active uplink transmissiondoes not increase the average power consumption of thedevice in the current active state.

• The power consumption during a pure downlink trans-mission PDL (without any uplink) equals to the powerconsumption in Low mode as shown in previous studies [3].Therefore, PDL = P2.

ULLow ULHigh ULMax DL Extra Idle

Power States

Classic Uplink CoPoMo

Time

1/λ1/µUL = DUL/RUL 1/µUL = DUL/RUL

Proposed Downlink CoPoMo Extension

Time

1/λ1/µUL1/µDL = DDL/RDL

Effect of Carrier Aggregation

Time

1/λ1/µDL 1/ (µDL · aCA)

Saved Time by CA Saved Time by CA

BoostFactor

Fig. 3. Comparison of Uplink CoPoMo and the Proposed Extension forDownlink and Carrier Aggregation

• Carrier aggregation will be modeled as a boost factoraCA ≥ 1 that increases the downlink data rate RDL. Asmeasurements in Sec. IV will show, enabling an additionalComponent Carrier (CC) leads to a constantly higherpower consumption in all active states.

With these assumptions a downlink transmission without CA(aCA = 1) can be arranged in a time line as shown in the middleof Fig. 3. The example shows two downlink transmissions(white) with an associated uplink portion (yellow, orange andred) arranged to the beginning of each block. By using µDLinstead of µUL to calculate the stationary distribution of theMarkov chain (Eq. 4), the resulting state probabilities reflectthe relative stay time of each mode as if the device wascontinuously uploading during the entire downlink transmissionperiod of

1

µDL=DDL

RDL=DUL · rDLUL

RUL · dDLUL. (6)

As the actual uplink time during the particular power statesis only 1/µUL, the remaining time consists of pure downlinktraffic consuming PDL.

When enabling an additional CC and therefore increasingthe downlink data rate by aCA > 1 this will result in a shorterdownlink transmission time as shown in the bottom row ofFig. 3. During this saved time the UE immediately enters theIdle state, thus saves energy.

Let ti = 1/µDL,i the average time spent in a particular statei without CA and tULDL,i = 1/(µDL,i · aCA) the actual activetime with CA then the extra idle time gained by CA is

tidle,i = ti − tULDL,i. (7)

The average uplink time is derived from its data size and ratetUL,i = DUL/RUL,i and the remaining active time consists of

Base Station Emulator Combiner DUT within Shieldingbox Power Probe Measurement Software

Shielded ChamberBattery

AntennaCoupler

DUT

Power Probe

Fig. 4. Schematic Overview of the Measurement Setup and Photography of the Measurement Setup Inside a Shielded Chamber

only pure downlink transmissions:

tDL,i = tULDL,i − tUL,i for tULDL,i > tUL,i. (8)

Cases where the uplink transmission time is larger than orequal to the downlink activity time are covered by the classicCoPoMo, thus are not further discussed in this paper.

According to the calculated time intervals, the mixed powerconsumption Pi can be described as

Pi =tUL,i

tiPi +

tDL,i

tiPDL +

tidle,i

tiP1 for i ∈ {2, 3, 4} (9)

and P1 = P1. This model now allows a simulative rating ofthe impact of CA on the UE power consumption pursuant todifferent system and context parameters.

IV. MEASUREMENTS

In order to fed the proposed power consumption model withappropriate values and to analyze the static power consumptioncaused by downlink CA in today’s UE, we performed excessivemeasurements with two Common-off-the-shelf LTE DeviceUnder Test (DUT) in a laboratory environment, abbreviated asDUT-A and DUT-B:

• DUT-A: LG G5• DUT-B: Samsung Galaxy S5 Neo

A. Measurement SetupA measurement setup, as outlined in Fig. 4, was built to

create a fully controlled environment consisting of an LTEBase Station Emulator (BSE), an LTE UE, and measurementequipment to determine the UE power consumption. The BSEwas a composite of two Rohde & Schwarz CMW500 thatallows the emulation of an LTE cell with up to three CCs a20 MHz at arbitrary frequencies and using 2×2 Multiple InputMultiple Output (MIMO) on each carrier. Due to limitationsof the available DUT, measurements with CA were limitedto the Primary Carrier Component (PCC) (the actual servingcell) and a single Secondary Carrier Component (SCC) whichformed the aggregated part. The signal was fed into a shieldedchamber as shown in Fig. 4 to avoid interference with publicnetworks. In order to measure the power consumption of theDUT, the battery has been extracted and replaced by wireddummy which was interconnected with a measurement probe.The power consumption was captured by a Hitex Powerscalewith a sampling rate of 100 kHz and evaluated on a computer.

If not stated otherwise the measurements were performed inSingle Input Single Output (SISO) configuration at bandwidthsof 10 MHz for each CC. During an active connection the UE

continuously got full RB allocations on all active CC forcingthe UE to decode random padding bits and withdraw themin downlink direction and to fill padding bits in the uplink.By doing so, no user-space application was required on theDUT to generate traffic. This minimized the influence of otherpower consuming components (e.g. CPU, memory, display)on measurements of the LTE part, as only the over-all powerconsumption of the DUT could be measured.

B. Band Specific Power ConsumptionThe first measurement targets the question whether it makes

a difference in terms of power consumption if a UE receives thePCC and SCC in specific bands and if there are any differencesbetween intra-band and inter-band carrier aggregation. Toquantify this, we sequentially connected the two DUT toa serving cell in one of three different bands which arelicensed for LTE in Germany: Band 3 (1800 MHz), Band 7(2600 MHz), and Band 20 (800 MHz). Subsequently, SCC atthe listed frequencies has been added covering all possiblepermutations with two carriers and three bands. The additionalpower consumption in relation to single carrier operation isplotted in Fig. 5 for both DUT.

It can be seen, that depending on the selected permutationthe additional power consumption of both devices is between190 mW and 403 mW and varies in the order of 100 mW for asingle device. Particularly, intra-band CA leads to the smallestpower increase with two exceptions: First, DUT-A does notsupport any intra-band CA in the 800 MHz band. Second,for DUT-B the combination of 2600 MHz and 1800 MHzconsumes slightly less power than the corresponding intra-bandconstellation at 2600 MHz. For both DUT with the exclusion ofintra-band CA, enabling an SCC in the 2600 MHz band resultsin the highest power draw compared to the other covered bands.

C. Number of Resource Blocks vs. Power ConsumptionThe second measurement aims at the cause of the power draw

increase, whether it is caused by the increased decoding effortor by powering an additional receive chain. For this purpose weperformed two power measurement series for various numbersof allocated RBs with both, PCC and SCC, in the 2600 MHzband at 20 MHz bandwidth. This allows a total downlinkallocation of maximum 200 RBs at the same time. The firstseries allocates sequentially 8 to 100 RBs on the PCC only.Afterwards SCC is being added and the sequence is appended to200 RBs. The second series covers the same range but enablesthe SCC right from the beginning and shares the allocatedRBs equally among the two CC. Fig. 6 shows the results ofthese measurements for DUT-A normalized to the PCC-only

800 1800 26000

200

400

0

300 310273

190

310323 310

190

Primary Component Carrier Frequency [MHz]

Pow

erC

onsu

mpt

ion

[mW

]

SCC 800 SCC 1800 SCC 2600

Not

Supp

orte

d

Intra-Band CA

DUT-A

800 1800 26000

200

400

300 314 330339307 310

403 394

330

Primary Component Carrier Frequency [MHz]

Pow

erC

onsu

mpt

ion

[mW

]

SCC 800 SCC 1800 SCC 2600

Intra-Band CA

DUT-B

Fig. 5. Additional Power Consumption for Carrier Aggregation at DifferentFrequencies for DUT-A and DUT-B

consumption at 8 RBs. While the power consumption linearlygrows with an approximated rate of 0.8 mW per RB, adding theSCC constantly causes a jump of 323 mW independent of thedecoding complexity. As soon as multiple carriers are activated,the distribution of allocated RBs among the carriers makes nodifference in the power consumption. At this place it must benoted that in contrast to Fig. 5 the operating bandwidth now istwice as high as before, thus the additional power consumptionfor CA has roughly doubled as well. Based on this knowledge,the base station should always prefer – if possible without theloss of service quality – a single carrier allocation over anaggregated allocation as long as the number of RBs fits intothe PCC. Due to space restrictions the results for DUT-B areomitted as it behaves similarly to DUT-A.

D. Uplink Power Consumption

As mentioned in Sec. III the highest power draw of LTEequipment is caused by the uplink. Although the focus of thispaper lies on the analysis of the downlink, realistic usagepatterns of LTE devices always contain a certain fractionof uplink traffic. Consequently, a simulation of the over-allpower consumption of a UE requires power measurementsin the distinct power states (cf. Fig. 2). Since the uplinkconsumption has been examined in previous works [12], only abrief overview of our results is given. Measurements were madefor both DUT at 800 MHz and 2600 MHz with a bandwidthof 10 MHz at full RB allocation. The UE transmission powerwas set by the BSE covering the range of −10 dBm to 23 dBmwhile capturing the power draw of the DUT. As an example

0 50 100 150 200

0

0.2

0.4

0.6

8

+323 mWfor Carrier

Aggregation

Enable CA

No CA

CA Always On0.8 mW/RB

Total Number of Allocated Resource Blocks

Add

ition

alPo

wer

Con

sum

ptio

n[W

]

Reference

Fig. 6. Normalized Power Consumption of DUT-A in Relation to the Numberof Allocated Resource Blocks and Carrier Aggregation

−10 −5 0 5 10 15 200.5

1

1.5

2

γ

P2 P3

P4

Transmission Power [dBm]Po

wer

Con

s.[W

]

MeasuredLinear Model

Fig. 7. Power Consumption of DUT-B at 800 MHz in Relation to TransmissionPower.

TABLE IIEMPIRICAL MODEL PARAMETERS FOR POWER AMPLIFIER MODES

Parameter DUT-A DUT-BFrequency [MHz] 800 2600 800 2600

P1 [mW] 97 97 30 30P2 [mW] 753 860 604 980P3 [mW] 1912 1578 1309 1515P4 [mW] 3053 2450 1873 1993γ [dBm] 15 10 12 12

the measurement results for DUT-B at 800 MHz are given inFig. 7. For the power model the consumption is approximatedby two linear graphs. These were separated by the breakpointγ which marks the switching point from a low power amplifierto a high power amplifier within the device [3]. While thepower consumptions of the Idle state P1 and the Max state P4

are clear, the representative consumption for Low mode P2 istaken at 0 dBm and for P3 (High) at (γ+ 23)/2 dBm [3], [12].The determined values for both DUT are listed in Tab. II.

V. SIMULATIONS

Based on measurements in Sec. IV, which we fed intothe proposed power consumption model (cf. Sec. III-B), weperformed simulations to evaluate the power-savings potentialof CA in various scenarios. If not stated otherwise thesimulations are configured as listed in Tab. III.

A. Impact of Data SizeThe first simulation varies the average download file size

DDL for both DUT at 800 MHz and 2600 MHz as shown inFig. 8. At small amounts of data (DDL < 2 Mbit) most of

TABLE IIIDEFAULT SETTINGS FOR SIMULATIONS

Parameter ValueFrequency 800 MHz, 2600 MHz

Bandwidth PCC, SCC 10 MHz eachCA Type Intra-Band-CA

CA Boost Factor aCA 2Antenna Configuration SISO

Environment SuburbanChannel Model AWGNArrival Rate λ 1/(5 min)

File Size DDL 1 GbitTransmission Protocol TCPUL/DL Ratio dDLUL 0.02

UL/DL Rate Ratio rDLUL 0.5

the power consumption is issued by the idle state, thus beingindependent of frequency and aggregation type. For largertransmissions raises the 2600 MHz curve much earlier to ahigher power level than the curve of the lower band. This ismainly caused by the uplink fraction of the transmission andthe increased attenuation in this band, which requires in averagea higher transmission power of the UE. Since both devicesconsume more power during pure data reception at higherfrequencies (cf. P2 in Tab. II), CA is especially beneficial asthe expensive active time is shortened by the boosted data rateand consequently allows the device to fall back quickly intoIdle state. Both devices consume at this band and at a mean filesize of 1 Gbit about 30 % less power if CA is added. Changingthe frequency to 800 MHz results in savings of at least 21 %to 26 % depending on the device.

B. Impact of Cell Environment and Boost Factor

To analyze the impact of the cell environment on the UEpower draw, simulations at urban, suburban, and rural buildingdensities were performed. In addition, the CA boost factor aCAwas varied to identify the point from which CA outperformsthe single CC consumption. The results for DUT-A can be seenin Fig. 9. Results of DUT-B are omitted due space limitationsand similar behavior. As reference, the power consumptionof particular scenarios without CA are drawn in red untilthey intersect with their corresponding CA curve. It can beseen, that the total power consumption of a UE varies withthe chosen environment. Rural environments issue the lowestpower consumption due to the high probability for line-of-sight connections. The highest consumption takes place inthe suburban case as the inter-site distance is still very high(cf. Tab. I) but a large fraction of the covered area has noline-of-sight connection, hence the signals suffers of increasedattenuation. In the urban case the reduced cell size decreasesthe probability for high power states.

Furthermore, the results show that the intersection of powerconsumption with CA and without CA happens at exactly thesame boost factor aCA regardless of the chosen environment.Instead, it depends on the device and operating frequency andlies in a range of 1.25 to 1.29 for DUT-A. This means, thatCA reduces the power consumption of an UE if it increasesthe downlink data rate at least by an order of 25 % to 29 %. In

1M 10M 100M 1G 10G0

0.5

1

2600 MHz800 MHz

−26 %

−31 %

Data Size DDL [Bit]

Pow

erC

onsu

mpt

ion

[W] Single Carrier

Carrier Aggregation

DUT-A

1M 10M 100M 1G 10G0

0.5

1

2600 MHz 800 MHz

−21 %

−30 %

Data Size DDL [Bit]Po

wer

Con

sum

ptio

n[W

] Single CarrierCarrier Aggregation

DUT-B

Fig. 8. Power Consumption of DUT-A and DUT-B in Relation to AverageTransmission File Size (cf. Tab. III)

1 1.5 2 2.5 30

0.2

0.4

0.6

0.8

1

1.2

ReferenceWithout CA

1.25

1.29

CA WastesPower

CA Saves Power

2600 MHz

800 MHz

Carrier Aggregation Boost Factor aCA

Pow

erC

onsu

mpt

ion

[W]

EnvironmentUrbanSuburbanRural

Fig. 9. Power Consumption of DUT-A in Relation to Carrier AggregationBoost Factor for Different Cell Environments

general, at higher frequencies smaller boost factors are requiredto reduce the power draw of the device. While higher boostfactors lead to further power savings, the savings do not scalelinearly with the boost factor, but rather stagnate beyond valuesof 2 to 3. In any case, a higher boost factor leads to largerpower savings of the UE.

C. Impact of Mobility and Boost Factor

After analyzing relationship of CA and the building densityon the power demands of LTE equipment, now the impact ofmobility will be investigated. Mobility affects the radio channelin terms of fast fading, which is basically a result of the signal’s

1 1.5 2 2.5 30

0.5

1

1.5

ReferenceWithout CA

1.25

1.29

CA WastesPower

CA Saves Power

2600 MHz

800 MHz

Carrier Aggregation Boost Factor aCA

Pow

erC

onsu

mpt

ion

[W]

MobilityAWGN 0 km/hPedestrian 3 km/hVehicular 60 km/h

Fig. 10. Power Consumption of DUT-A in Relation to Carrier AggregationBoost Factor for Different Mobility Patterns

multipath propagation that leads to constructive or destructiveinterference of the distinct signal paths at the receiver. As thiseffect highly depends on position and frequency, the signalfluctuates more intensively if sender or receiver move faster.Consequently, this reduces the throughput in both directionsand requires the UE to spend more time in active transmissionmodes. Fig. 10 shows the simulated average power consumptionof DUT-A for two frequency bands, three mobility types andCA boost factors from 1 to 3. Again, DUT-B behaves similarly,hence the results are omitted due to limited space. It can be seen,that mobility – especially in the 800 MHz band – significantlyincreases the power demands of DUT-A if compared to thestatic AWGN case in average by 41 %. Conversely, increasingthe speed of movements from 3 km/h to 60 km/h increases thepower draw in average only by 6 % at 800 MHz; at 2600 MHzthe power increase is even negligible at 1 %.

The intersection point of power consumption with CA andwithout CA is located at the exactly same boost factor for agiven frequency and device independent of the mobility anddo not differ from the previous simulation. This makes CAbeneficial for the battery lifetime of mobile equipment in anycase, no matter of the velocity and building density as long asthe achieved boost in data rate overbalances the increased CApower draw.

VI. CONCLUSION

The results of this paper show that Carrier Aggregation (CA)is beneficial for the power consumption of LTE devices whenused deliberately. Measurements of existing LTE-A devicesshowed that the use of multiple Component Carrier (CC) entailsan increased power draw for supplying additional receive chainsin the order of 300 mW. Therefore, as long as the allocatednumber of Resource Blocks (RBs) fits into a single CC, CAshould be avoided since this only leads to an increased powerdraw. This device-specific power increase varies with the chosenband. In most cases inter-band CA pointed out a slightly higherpower draw than intra-band CA.

Applying the empirical data into a proposed downlinkextension of the Context-Aware Power Consumption Model(CoPoMo) allowed a quantitative evaluation of the costs and

benefits of CA for different mobility patterns, environmentsand frequencies. The simulations show, that CA has a positiveeffect on the power consumption, if it boosts the downlinkdata rate by at least 25 % to 29 %. At this point the powersavings by additional idle time compensate the higher powerdraw during an active transmission. The exact values dependon the carrier frequency of the primary carrier but not on thebuilding density or whether the UE is static or moving quicklywith a vehicle.

Further simulations showed increasing power savings as theaverage file size of downloads grows. In the simulated scenarioat a mean file size of 1 Gbit, which is typical for multimediavideos or navigation map data, a power reduction of 21 % to31 % can be achieved by CA.

ACKNOWLEDGMENT

Part of the work on this paper has been supported byDeutsche Forschungsgemeinschaft (DFG) within the Collab-orative Research Center SFB 876 “Providing Information byResource-Constrained Analysis”, project A4.

REFERENCES

[1] A. R. Jensen, M. Lauridsen, P. Mogensen, T. B. Sørensen, and P. Jensen,“LTE UE power consumption model: For system level energy andperformance optimization,” in Vehicular Technology Conference (VTCFall), 2012 IEEE, September 2012, pp. 1–5.

[2] M. Lauridsen, L. Noel, T. B. Sørensen, and P. Mogensen, “An empiricalLTE smartphone power model with a view to energy efficiency evolution,”Intel Technology Journal, vol. 18, no. 1, pp. 172–193, 2014.

[3] B. Dusza, “Context aware power consumption modeling for energy effi-cient mobile communication services,” Ph.D. dissertation, TU Dortmund,December 2013.

[4] R. Sanchez-Mejias, Y. Guo, M. Lauridsen, P. Mogensen, and L. A.M. R. de Temino, “Current consumption measurements with a carrieraggregation smartphone,” in 2014 IEEE 80th Vehicular TechnologyConference (VTC2014-Fall), September 2014, pp. 1–5.

[5] L. A. Sonkusare and S. N. Dhage, “LTE UE performance evaluationfor carrier aggregation and voice-over-LTE,” in 2015 InternationalConference on Applied and Theoretical Computing and CommunicationTechnology (iCATccT), October 2015, pp. 642–647.

[6] B. Dusza, C. Ide, L. Cheng, and C. Wietfeld, “CoPoMo: A context-awarepower consumption model for LTE user equipment,” Transactions onEmerging Telecommunications Technologies (ETT), Wiley, vol. 24, no. 6,pp. 615–632, October 2013.

[7] B. Dusza, P. Marwedel, O. Spinczyk, and C. Wietfeld, “A context-awarebattery lifetime model for carrier aggregation enabled LTE-A systems,” in2014 IEEE 11th Consumer Communications and Networking Conference(CCNC), January 2014, pp. 13–19.

[8] 3GPP TR 25.813 - Physical layer aspects for evolved UniversalTerrestrial Radio Access (UTRA), 3rd Generation Partnership ProjectTechnical Report, Rev. V7.1.0, September 2006.

[9] 3GPP TS 36.101 - Technical Specification Group RadioAccess Network; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception(Release 12), 3rd Generation Partnership Project TechnicalSpecification, Rev. V12.7.0, March 2015. [Online]. Available:http://www.3gpp.org/ftp/specs/archive/36 series/36.101/

[10] B. Dusza, C. Ide, and C. Wietfeld, “Measuring the impact of the mobileradio channel on the energy efficiency of LTE user equipments,” inProc. of the 21st International Conference on Computer CommunicationNetworks (ICCCN), 2nd International Workshop on Context-aware QoSProvisioning and Management for Emerging Networks, Applications andServices (ContextQoS). Munich, Germany: IEEE, July 2012.

[11] B. Dusza, C. Ide, P. B. Bok, and C. Wietfeld, “Optimized cross-layerprotocol choices for LTE in high-speed vehicular environments,” in2013 9th International Wireless Communications and Mobile ComputingConference (IWCMC), July 2013, pp. 1046–1051.

[12] B. Dusza, C. Ide, L. Cheng, and C. Wietfeld, “An accurate measurement-based power consumption model for LTE uplink transmissions,” in 2013IEEE Conference on Computer Communications Workshops (INFOCOMWKSHPS), April 2013, pp. 49–50.