Reducing Energy Consumption of LTE Devices forMachine-to-Machine Communication

Tuomas Tirronen, Anna Larmo, Joachim Sachs, Bengt Lindoff, Niclas Wiberg

Ericsson Research

Abstract—We present a method to reduce the device batteryconsumption to efficiently support machine-to-machine (M2M)communication in LTE. We first introduce a model for calculatingenergy consumption of a LTE device. We assume that theM2M device transmits small amounts of data with deterministicintervals. Our model takes into account the energy consumptionin active and nonactive periods which alternate depending onthe configuration of discontinuous reception (DRX). We use themodel with different parameter settings referring to potentialfuture M2M devices. The results indicate that making the currentmaximum DRX cycle length longer would lead to significantgains in the energy consumption of M2M devices. Thus, our keycontribution is to show the potential of trading the responsivenessof a device for energy consumption gain with very long DRXcycles.

I. INTRODUCTION

Machine-to-machine (M2M) communications means con-

necting possibly billions of different devices using different

communication technologies. Wireless networks will be a key

technology enabling this visions [1]. Cellular networks are al-

ready today used for some applications in the M2M field, such

as automatic meter reading of water or power consumption

in households. Although the modern cellular technologies are

designed for efficient transmission of speech as well as data

traffic, there is potential and need for further optimizing the

networks and procedures to be able to support higher number

of devices and devices with constrained capabilities. Making

current technologies more energy-efficient is a crucial step in

making the visions of billions of connected devices come true.

One important requirement for an M2M device is its low

energy consumption. Typical scenarios include many small de-

vices, such as sensors, operating on battery power in possibly

remote locations. The lifetime of such devices could be tied

to the lifetime of the battery, if replacement or recharging the

batteries would be impossible or too costly.

Discontinuous reception (DRX) is a technique where the

radio receiver of a device or user equipment (UE) is allowed

to be switched off during times where there is no allocated

transmission towards said device. In this situation, it would

be wasteful from an energy point-of-view to have the receiver

circuitry using power as there would be no information to

be received. On the other hand, the DRX cycle limits the

responsiveness of a device towards triggers from the network,

since the device can only be reached when it listens to the

network.

The purpose of this work is to show the advantage of long

DRX cycles in some LTE M2M scenarios in terms of lower

energy consumption. The length of the long DRX cycles go

beyond the values that the current standards allow. In 3GPP

Long Term Evolution (LTE) [2], the current maximum DRX

or paging cycle length is 2.56 seconds [3]. Our purpose is to

present evidence that longer DRX cycles are vital for making

cellular technologies a viable platform for M2M connectivity.

M2M devices configured with very long DRX cycles will

face reduced responsiveness in terms of how often they can

be reached. We do not consider this a serious limitation for

many M2M use cases, where services are often delay-tolerant.

Extended DRX cycles are intended for devices which would

be in a “sleeping” state for most of their lifetime.

We present a simple power consumption model for different

DRX cycle lengths. The considered M2M scenario includes a

device, such as a sensor, transmitting small amounts of data

with fixed time intervals. Thus, our model will be deterministic

by fixing the traffic to arrive at preallocated time instants. We

use a simplified model of reality and it does not model all

of the details of the DRX mechanism in LTE. Nevertheless,

the model and calculated results should convince the reader

of the energy consumption gain made possible by very long

DRX cycles. Moreover, with small effort, the model could be

easily adapted to other technologies, such as HSPA.

The model takes into account the energy consumption of

four different periods: Active, nonactive, transmission and

synchronization. Transmission period models the actual uplink

data transmission, active period refers to receiving data or

listening for paging messages from the network. Nonactive

period models the DRX, or sleeping, period of the device. Syn-

chronization period refers to the time period needed to obtain

proper synchronization to the air interface before transmitting

or receiving information.

The model is an idealized version of reality and we will not

go into the details of actual device implementation. However,

we show the potential of gaining better energy consumption

performance by making the maximum allowable DRX cycle

longer. The examples we use assume power consumption

figures for an optimized M2M device platform. They are prob-

ably lower than what is possible to achieve with today’s device

platforms which are targeted for smart phones. However, for

our assessment the absolute energy consumption numbers are

of little importance, since the relative gains of modifying DRX

cycles should be rather independent from the absolute power

levels.

Modeling DRX cycles and the tradeoff between energy con-

sumption and different timers involved has been studied before

GC'12 Workshop: Second International Workshop on Machine-to-Machine Communications 'Key' to the Future Internet of Things

978-1-4673-4941-3/12/$31.00 ©2012 IEEE 1650

in several papers. The previous studies differ in what kind of

power model is used and what are the traffic characteristics

and use scenarios. Also the model details and methodologies

differ. We summarize some of the previous work below.

Energy and power saving of 3GPP technologies have been

studied for example in [4], [5], [6] and [7]. In some of these

studies, a similar type of power model which we propose is

used in simulations, with periods for deep sleep, sleep and

active mode for reception. However, our contribution considers

a different scenario for M2M communications with long DRX

cycles and we use an analytical approach.

A typical way to address the effect of DRX cycle lengths

with different types of traffic, such as bursty or streaming

traffic is to model the DRX cycles as Markov processes, such

as in [8] and [9]. DRX inactivity timer in WCDMA is studied

in [10].

A recent study [11] is claimed by its authors to be the

first empirically verified power consumption model for LTE

systems. One drawback of that work is that the verification is

done using traces from 3G and WiFi traffic and not real LTE

traffic.

However, there is lack of good general models for ad-

dressing the power consumption of M2M devices in different

scenarios. Our main contribution compared to this earlier work

is to focus on an optimized M2M scenario and using longer

DRX cycle lengths then what is currently specified.

The rest of the paper is organized as follows: In the next

Section we describe briefly how DRX works in LTE and in

our simplified model. Section III presents the M2M traffic

and device assumptions we have made. In Section IV we

describe our model for the power consumption and discuss

the set of parameters used. Examplary results using the model

are presented in Section V. We present some discussion of

the obtained results in Section VI and conclude the work in

Section VII.

II. DISCONTINUOUS RECEPTION

Discontinuous reception in LTE is used to decrease the total

power consumption of the terminal by allowing it to turn

receiver circuitry momentarily off. A description of DRX can

be found, e.g., in [12].

Without DRX configured, an LTE terminal would have to

listen to the control channel (PDCCH) every subframe, that is,

in LTE, every millisecond. The PDCCH includes information

on uplink and downlink grants so the terminal would know

when it should transmit or receive data. As packet data traffic is

typically bursty, this behaviour will result in wasted resources:

The UE will listen to PDCCH every subframe although in

many cases this would not be necessary, as there is no data to

send or to receive.

A key parameter in DRX is the DRX cycle length. When

DRX is configured, the terminal needs to listen to the control

channel in only one subframe per DRX cycle. This will

provide opportunity for the UE to turn its receiver circuitry

off or go into low-power mode during the cycles. Extending

the DRX cycle length will result in longer delay, or reduced

responsiveness, of the terminal: the eNodeB cannot reach the

UE during the cycles and DRX restricts the scheduling in the

eNodeB towards the UE to those subframes where the UE

listens to PDCCH.

To account for the bursty traffic better, a terminal is typically

kept in active state, listening to PDCCH, for a while after

it has transmitted or received data. This is controlled via an

inactivity timer, which is always (re)started upon new uplink or

downlink transmission. LTE hybrid-ARQ (HARQ) mechanism

is accounted for by using a retransmission timer. Our model

is a simplified version of reality, where we do not explicitly

consider the inactivity and HARQ timers. However, these can

be implicitly taken into account using other model parameters,

see Section IV.

Thus, there is a tradeoff between the DRX cycle length

and the terminal responsiveness. Longer cycles would result

in energy savings, on the other hand the device responsiveness

will deteriorate.

In rest of the paper we will study and discuss the impact of

this tradeoff.

III. MACHINE-TO-MACHINE SCENARIO

As the basis for the model and the results presented in Sec-

tions IV and V, respectively, we use a M2M communication

scenario. More specifically, we have a device, which sends

small data, consisting of one or couple of packets, infrequently

with data reporting periods t. In terms of the traffic process,

infrequently in this paper refers to t from several minutes up to

hours (or more). Moreover we consider the device to have low

mobility, i.e., it does not move at all or the movement speed

is slow. In cellular systems this would mean the serving cell

is changed rarely if at all. An example of a device fulfilling

the above characteristics could be a static sensor or a battery-

powered utility meter sending reports on some measurements

with pre-defined sending intervals. Of course, the model used

in this paper can also be applied to moving devices. However,

in this case the energy consumption is also affected by mobility

related measurements and signaling.

In our assessment we do not consider the energy con-

sumption of any signaling procedures associated with data

transfer. This corresponds to a use case where devices have

radio bearers and security context established and keep them

maintained. They can initiate data transfer at any point in

time. In LTE terms this corresponds to the device being

in RRC CONNECTED state, see, e.g. [12]. If the devices

changed to RRC IDLE state in-between data transfers, the

data transfer would be accompanied by an additional radio

bearer establishment procedure.

We will present results where we assume that the device

will be in deep sleep state in-between two active periods. This

means that the device needs to obtain time synchronization to

the air interface to be able to communicate.

In [11] the authors claim that one major reason for the

inefficiency of LTE compared to 3GPP 3G and WiFi is high

tail energy consumption. This means the energy that the UE

consumes after listening to transmissions from the base station

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TABLE IMODEL PARAMETERS FOR POWER CONSUMPTION

Power consumption of M2M (mW) Smartphone (mW)Ptx transmission 300 1000Pact active period 100 200Pclock accurate clock 10 10Pbase base 0.01 0.1Psleep sleep 0 1

TABLE IIOTHER MODEL PARAMETERS

parameter M2M Opt. Smartphonen number of cycles 2− 300 2− 300t data reporting period 768 s 768 s

tDRX length of DRX cycle 2.56− 384 s 2.56− 384 sttx time spent in tx period 50 ms 50 mstact time spent in active period 10 ms 10 mstsync time to obtain sync 10 ms 10 msCbat battery capacity 6500 J 6500 J

and before transitioning back to RRC IDLE state, where the

UE needs to basically only listen to network paging messages.

However, this is not an inherent behavior required for mobile

devices. In our M2M model we assume that the device can

switch off most of its circuitry immediately after transmission

and active periods. Further, as described above, we assume

that the device stays in RRC CONNECTED, or similar, state.

The model parameters can be tuned to model longer periods

of transitioning time if so required.

IV. ENERGY CONSUMPTION MODEL FOR DRX CYCLES

A. Model description

As presented, we study a static M2M scenario and we have

made some simplifying assumptions to the procedures found

in LTE specifications regarding the device behaviour and the

DRX mechanism. This way we can study the tradeoff between

energy saving and responsiveness of the using a simplified

model, which still quantifies the tradeoff realistically enough.

Our model takes into account four different periods or

phases:

1) Active period consisting of receiving data and related

procedures (Pact, tact).

2) Nonactive, or sleeping, period, where the device does

not listen to any radio channels and tries to conserve

energy (Psleep, tDRX).

3) Transmission period, where the device sends data

(Ptx, ttx).

4) Sync period before active and transmission periods

(Pclock, tsync).

In addition, in our energy consumption model, we will take

into account a base power consumption which is always

consumed independent from the transmission state.

We do not explicitly model possible erroneous transmissions

or retransmissions. In the case of retransmissions the total

energy consumption would grow and the figures of energy

consumption gains in Section V would be lower. However,

if the radio channel conditions are known, average values of

expected power consumption due to retransmission can be

incorporated into the calculations below.

All of the parameters used in the model are presented

in Tables I and II, with reference values for two scenarios

presented later in Section V. We will refer to the two scenarios

as “M2M Optimized” and “Smartphone-like” scenarios. The

times are also depicted in Figure 1 with the accompanying

periods. The semantics of the parameters are further elaborated

in the next few sections.

B. Number of cycles

We consider an M2M scenario where the device has small

and infrequent data to send, with the intervals in-between

transmissions being in the range from several minutes to hours.

When the uplink data transmission is an infrequent event, the

total energy consumption during the transmission periods will

form a negligible part of the total energy consumption of

the device. Instead, with today’s LTE configuration, energy

consumption is dominated by the device becoming repeatedly

active to perform measurements and read the paging channel.

Another assumption is that the power consumption during

nonactive period is much less than during the active period.

The factor of the power consumption of these two depends on

the used technology and how capable the device is in saving

energy. Our base assumption here is that during the nonactive

period the device can switch most of its circuitry off, thus

keeping only the absolute necessary functions running. Parts

which are always on could include, for example, a hardware

clock which is not able to keep the synchronization required

for successfully transmitting or receiving data.

Based on above, we conclude that a key factor contributing

to the total energy consumption of an M2M device would

be the total number of DRX cycles n between two data

transmission instants. For example, if we assume a 60 minutes,

i.e., 3600 s transmission interval the device would need to

wake up approximately 1400 times between transmissions

when using the current maximum DRX cycle length of 2.56

s.

Using the parameters in Table II we can calculate n:

n(tDRX) =t− ttx

tDRX

. (1)

For convenience we will write n = n(tDRX) in the following,

keeping in mind that the number of cycles is really a function

of the DRX cycle length (and t, which is, however, considered

constant in this work).

Having set the power values for different phases as listed

in Table I it is straightforward to calculate the actual energy

consumption of the device over period t.

C. Transmission period

During nonactive periods the device is in a low power state.

That typically means not only disabling the transceiver and

majority of the baseband processing, but also the accurate

high power clock used for keeping in-sync with the network.

Instead only a very low power clock is enabled. This clock

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Active period• The receiver circuitry is on • Device reads the necessary control / system information channels

Nonactive period• Device tries to save as much power as possible• Radio is not listening the air interface

time

txtacttsynct DRXt

t

synct

Sync period• Device obtains fine sync to the air interface

Data transmit• Period includes all the necessary functions for the device to send some (small) data

Fig. 1. Different periods of the simple power model. Height of the bars roughly represent the power consumption in each period. Psleep is assumed to be 0.Pbase is negligible and not visible.

keeps only rough timing to determine when to enable the

reception in order to be ready for the next active period.

The advantage with such a solution is that the low power

clock typically has magnitudes lower power consumption

(μW compared to mW), however there is then a need for

a synchronization time period, tsync, to determine the fine-

time/frequency synchronization with the network. In case the

accurate clock has been on, tsync would be in the order of 1

ms, while starting with only the rough clock on we would need

10-100 ms, depending on the length of the sleep period and

hence how large the time and frequency drift is. Clearly there

is a tradeoff when to disable the accurate clock and when not,

but for M2M applications discussed in this paper with very

infrequent data reporting event, there is a clear advantage to

turn off the accurate clock.

Thus, before each uplink transmission period, we have a

time period tsync, where the device obtains synchronization to

the air interface. In our model the accurate clock, Pclock, is on

during both the sync and transmission times. Parameter Pbase

includes the power consumed by the low power clock, which

is always on.

The parameter Ptx includes the whole power consumption

of the circuitry during uplink transmission. This parameter is

thus also a function of the actual radio transmitter power used,

and the relation is nonlinear. For a typical M2M device we

can assume that the transmitter power is quite low in order

to save battery. However, sensor-type devices could also be

distributed in remote areas needing to use higher power to

reach the eNodeB.

The size of the packet the device sends does not explicitly

exist as a parameter. However, time ttx can be modified to

model shorter or longer packet transmissions. The time ttx in

Table II corresponds to a device transmitting a packet of 1000

bytes at a rate of 160 kb/s.

There is only one transmission period in t, thus, for the

energy consumption we have

Etx = tsync · (Pact + Pclock) + ttx · (Ptx + Pclock) , (2)

where we have taken into account the synchronization period

consuming receiver power (Pact) and the actual uplink trans-

mission period.

D. Active period

In total we have n cycles during which the total energy

consumption consists of the downlink reception of paging or

other control messages, also including other processing made

at the device, all conveyed by Pact. This would include the

procedures listed in [13] and the procedures of user and control

plane that have been implemented in the device. It is worth

noting that Pact, the power consumed by the receiver circuitry,

would be a function of the downlink bit rate; faster bit rates

result in higher energy consumption. As we consider an M2M

scenario, we, again, assume that the received bit rate is low

and constant, and that the eNodeB takes this into account when

scheduling transmissions.

The active period length includes the possible inactivity

timer in LTE DRX, see Section II.

Over time t with n cycles in total (if n > 1) we arrive at

energy consumption

Eact = n · (tact + tsync) · (Pact + Pclock) . (3)

E. Nonactive period

The power consumption of the device in nonactive periods

in our model is Psleep. We do consider this value to be negligi-

ble if the device has been optimized for the use scenarios we

have described. Calculating the total length of one nonactive

period to be the difference between DRX cycle length and the

time spent in active state we arrive at

Esleep = n · (tDRX − (tsync + tact)) · Psleep. (4)

F. Total energy consumption over sending interval

In total, taking into account the cycles of active and non-

active periods, we can write an expression for the energy

consumption of the device over time t:

E(t) = Etx + Eact + Esleep + Ebase =

n · (tact + tsync) · (Pact + Pclock)+

n · (tDRX − (tsync + tact)) · Psleep+

tsync · (Pact + Pclock) + ttx · (Ptx + Pclock) + Pbase · t. (5)

Here we have additionally taken into account the base power

consumption, Ebase = Pbase · t, over the whole time period t.

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This could be, for example, a rough hardware clock mecha-

nism (not able to keep the interface synchronized by itself).

For some M2M scenarios we will assume that Psleep = 0 in

Section V. Note that the power consumption parameters do not

explicitly include the base consumption, Pbase. That is, even if

Psleep = 0, some parts are still on during the nonactive period

contributing to the total energy consumption with Pbase.

We will also study the average power consumption:

P (t) =E(t)

t, (6)

where we have divided the total energy consumption over

one time period with the time giving us average energy

consumption per time unit. We see that

n

t=

t− ttx

t · tDRX

=1

tDRX

when t→∞,

thus (6) converges

limt→∞P (t) =

tact + tsync

tDRX

· (Pact + Pclock)+

tDRX − (tsync + trx)

tDRX

· Psleep + Pbase. (7)

If we further let tDRX grow as well, i.e., set tDRX = t there

are no active periods remaining in-between data transmission,

and we would eventually have

limt,tDRX→∞P (t) = lim

t,tDRX→∞E(t)

t= Psleep + Pbase. (8)

G. Battery lifetime

We can use (5) to approximate the battery lifetime of an

M2M device. As in our consumption model, we consider the

power consumption figures as average consumption. Let us

denote Cbat the battery capacity in joules. Then the battery

lifetime tbat can be solved from

Cbat = E(tbat), (9)

using (5).

V. SCENARIOS AND RESULTS

A. M2M optimized scenario

The energy consumption and battery lifetime for M2M

optimized scenario referred in Tables I and II is depicted

in Fig. 2.

In this case we consider a future low-energy-optimized

M2M device, which basically falls into a deep sleep mode in

between the active periods. The tail energy problem mentioned

in [11] is assumed to be solved by hardware and network

setting optimizations. The transmitter power is assumed to be

on the range of 0 − 10 dBm. Note that the power parameter

listed in Table I consists of all power consumed during the

transmission period, not just the radio transmitter power. The

power consumption parameters are on scale with the ones

used in the model in [5] and [6]. The device needs to wake

up before any activity and synchronize to the base station

reference signal. We assume that the hardware and the network

are optimized so that in total the wake-up, synchronization and

time spent in active phase amount to 20 ms.

Instead of absolute values, both the energy consumption

and battery lifetime are plotted as relative values compared to

those obtained using current maximum LTE DRX cycle length

of 2.56 s. For example, for cycle length of 20.48 seconds we

would see energy consumption reduction of 85% and battery

lifetime increases by a factor of 6.4. If we assume a standard

AAA type battery with capacity of 6.5 kJ, the battery lifetime

would grow from approximately 2.3 months to 5.5 years (with

DRX cycle length of 384 s).

B. Smartphone-like scenario

The second scenario represents a device which has non-

zero sleep mode power consumption and further consumes

more power during transmission, reception and processing.

This would roughly correspond to a smartphone-like scenario,

where some parts of the device are on and consume some

power all the time. These parameters could also refer to a

poorly optimized M2M device and this scenario will work

as a comparison to the M2M optimized scenario. Higher

transmission power could be due to higher used transmitter

power, e.g., in range of 20 dBm. The energy consumption

and battery lifetime factors are depicted in Fig. 3. With tDRX

larger than 8 ms the device energy consumption is reduced

by more than 50%. Note that the values in this case are not

considered to be optimized for M2M communications setting

and are deliberately chosen to give an example of scenario

where the magnitude of the possible energy saving is smaller

than in the proposed M2M communication settings.

C. Effect of data reporting period

In Fig. 4 we have plotted how average power consumption

behaves as a function of the data reporting interval. We have

results for the three scenarios. In scenario i) the DRX cycle

length is 2.56 s, the maximum value possible in LTE today.

This corresponds as reference value. In ii) we show a long

DRX cycle set to tDRX = 128s and in iii) the DRX cycle

length becomes identical with the data reporting interval, i.e.,

tDRX = t. In this case n ≈ 1, there is no active period at all,

and the UE battery consumption is minimized. We see that

the average power consumption converges according to (7)

and (8). The plot values have been normalized, thus power

consumption of 1.0 refers to different absolute average power

consumption values. For tDRX = 2.56 s the values would be

1.39 mW and 4.37 mW, respectively, for M2M optimized and

the smartphone-like scenarios.

It can be seen in Fig. 4 that the graphs flatten out with larger

data reporting intervals. This means that if a M2M device

transmits data less frequently than once every 15 min. (i.e.

900 s) the device energy consumption is hardly affected any

longer by the data transmission. Instead it is dominated by the

base power consumption of the device and the active periods

when the device is monitoring the network. It can also be

seen that with a tDRX value of 128 s, the energy consumption

is already very close to the minimum value, when tDRX = t.

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0 50 100 150 200 250 300 350 4000

0.2

0.4

0.6

0.8

1

DRX cycle length (s)

Ene

rgy

cons

umpt

ion

(rel

ativ

e)

Energy consumption

0 50 100 150 200 250 300 350 4000

5

10

15

20

25

Bat

tery

life

time

fact

or

Energy consumptionBattery lifetime

Fig. 2. Relative gain in energy consumption and battery lifetime in M2Moptimized scenario.

0 50 100 150 200 250 300 350 4000

0.2

0.4

0.6

0.8

1

DRX cycle length (s)

Ene

rgy

cons

umpt

ion

(rel

ativ

e)

Energy consumption

0 50 100 150 200 250 300 350 4000

0.5

1

1.5

2

2.5B

atte

ry li

fetim

e fa

ctor

Energy consumptionBattery lifetime

Fig. 3. Relative gain in energy consumption and battery lifetime insmartphone-like scenario.

When the device is transmitting more frequently the device

energy consumption rises quickly and becomes dominated by

the data transmission periods, and the active and nonactive

periods become negligible. When data is transmitted more than

once every 40 s, the energy savings due to extended DRX

cycles do not exceed 10-20%.

VI. DISCUSSION

It is clear from the results presented in the previous section

that lengthening the maximum possible DRX cycle length

provides the possibility to trade the responsiveness of M2M

devices to significantly lower energy consumption and ulti-

mately longer battery lifetime. Even multiplying the current

maximum DRX cycle length of 2.56 seconds to around one

minute would make M2M devices using LTE for connectivity

much more energy efficient.

In the first case we consider a M2M device which consumes

close to zero power during the nonactive periods. When the

101

102

103

104

105

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Power consumption with different reporting intervals

Reporting interval t (s)

Pow

er c

onsu

mpt

ion

(rel

ativ

e)

M2M, tDRX

= 128

Smart, tDRX

= 128

M2M tDRX

= t

Smart tDRX

= t

M2M tDRX

= 2.56

Smart tDRX

= 2.56

Fig. 4. Effect of lengthening the data reporting period on power consumption.1.0 consumption refers to case when t = 32 seconds, the absolute values aredifferent for all cases. The lower lines (red) correspond to M2M optimizedcase while the middle lines (blue) represent the smartphone-like case. Thetopmost lines overlap each other and correspond to both scenarios with DRXcycle of 2.56 s.

data reporting period is long, the energy consumed during the

transmission period is negligible compared to the total power

consumption. The energy consumption gain compared to the

current maximum cycle length of 2.56 seconds is very good

with the longer DRX cycles in this case. As the length of the

DRX cycle increases, the total number of cycles decreases and

the energy consumed during the nonactive periods becomes

small and comparable to the energy consumed during the

transmission period. In the example of Fig. 2 the battery

lifetime is increased by more than 20 times.

The second scenario presented a little bit different case,

where the nonactive period power consumption is constant 1

mW. Moreover, the transmission power and the power con-

sumption of active periods are higher. The parameters in this

scenario were deliberately chosen to represent smartphone-

like scenario, where the hardware is not optimized for M2M

communications. Still, we see in Fig. 3 that the battery lifetime

can be almost doubled by allowing four times longer DRX

cycle lengths compared to the current maximum of 2.56 s.

The average power consumption will converge to a limit

related to the base power consumption, as shown in (7). Total

energy consumption will of course be the lower the longer the

data reporting period is, resulting in longer battery lifetime

as well; however, consumed energy will not further decrease

beyond a certain point.

Note that the real value of parameter Psleep depends on the

platform and the possible application used. Furthermore, Pbase,

that is, the power consumption in sleeping state when the

device is not doing anything else than waiting for the next

active or transmission period, could probably be optimized

further and is hardware implementation dependent. Especially

for an optimized M2M device this value could be even lower.

The effect of Pbase on the results is significant. The height

of the tails of the plots presented in Section V depend on

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the base power consumption (plus the possible Psleep). We

see that up to 10 times lower energy consumption is possible

and we could go as far as up to 100 lower if Pbase could be

optimized further. An important conclusion is that in order to

make device lifetime longer, the power consumption during

sleeping times has to be very low.

A. Idle mode vs. connected mode

While the basis of this work is to assume that the device

stays in connected mode during the whole time, the same

model could be applied to idle mode and paging cycles as well.

This would require to setting the parameters correspondingly,

for example using tsync to model the time required for perform-

ing the random access procedure. Also the power consumption

parameters should be tuned to correspond to this scenario,

as during the radio bearer setup procedure both receiver and

transmitter circuitry would be used. The current maximum

paging cycle for LTE when in RRC IDLE state is the same

as for the maximum DRX cycle , i.e., 2.56 s. Both DRX

and paging cycle maximums should be used for maximum

flexibility to allow tradeoffs between energy consumption and

device responsiveness.

VII. CONCLUSION

We presented a simple and general model for addressing

possible energy consumption gains for LTE devices when

using long DRX cycles. The model was developed for M2M

devices where the energy consumption is assumed to be very

low and the behaviour of the radio transceiver of the device is

near to optimal in the sense that different parts can be switched

off when not needed.

The results suggest that longer DRX cycles would signifi-

cantly lower the power consumption of M2M devices enabling

them to be used for long time periods using battery power.

The tradeoff is the device responsiveness as the time instants

when the network can reach the device become more rare. We

conclude that LTE can be a suitable radio access technology

for M2M devices, if it is configured with longer DRX cycles

according to the device’s needs.

ACKNOWLEDGEMENT

This research was partly conducted in the Internet of Things

program of Tivit (Finnish Strategic Centre for Science, Tech-

nology and Innovation in the field of ICT), funded by Tekes.

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