Research Article Power Saving Scheduling Scheme for

Research ArticlePower Saving Scheduling Scheme for Internet ofThings over LTE/LTE-Advanced Networks

Yen-Wei Kuo and Li-Der Chou

Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road,Jhongli, Taoyuan County 32001, Taiwan

Correspondence should be addressed to Li-Der Chou; [email protected]

Received 1 October 2015; Accepted 1 December 2015

Academic Editor: Jong-Hyouk Lee

Copyright © 2015 Y.-W. Kuo and L.-D. Chou. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

The devices of Internet of Things (IoT) will grow rapidly in the near future, and the power consumption and radio spectrumman-agement will become the most critical issues in the IoT networks. Long Term Evolution (LTE) technology will become a promisingtechnology used in IoT networks due to its flat architecture, all-IP network, and greater spectrum efficiency. The 3rd GenerationPartnership Project (3GPP) specified the Discontinuous Reception (DRX) to reduce device’s power consumption. However, theDRX may pose unexpected communication delay due to missing Physical Downlink Control Channel (PDCCH) information insleep mode. Recent studies mainly focus on optimizing DRX parameters to manage the tradeoff between the energy consumptionand communication latency. In this paper, we proposed a fuzzy-based power saving scheduling scheme for IoT over the LTE/LTE-Advanced networks to deal with the issues of the radio resource management and power consumption from the scheduling andresource allocation perspective.The proposed scheme considers not only individual IoT device’s real-time requirement but also theoverall network performance. The simulation results show that our proposed scheme can meet the requirements of the DRX cycleand scheduling latency and can save about half of energy consumption for IoT devices compared to conventional approaches.

1. Introduction

The concept of Internet of Things (IoT) enables devicesto connect to the Internet to sense data and interact witheach other. Different type of IoT devices, such as fitness,entertainment, location and tracking, and surveillance, mayhave different real-time requirement for different purpose.It can be predicted that IoT devices also referred to as IoTUser Equipment (UE) pieces will grow rapidly and the powerconsumption and radio spectrum management will becomethe critical issues in the IoT networks. Moreover, the wirelessconnectivity is a promising way to the Internet for IoTdevices. The Long Term Evolution (LTE) that, nowadays, isthe dominant technology in wireless communication makesit ideal for IoT applications due to its flat architecture, all-IPnetwork, and greater spectrum efficiency than 2G or 3G [1].

In the LTE network, the 3rd Generation Partner-ship Project (3GPP) specified the Discontinuous Recep-tion/Transmission (DRX/DTX) mechanism to alleviate the

power consumption issue which can severely affect the bat-tery lifetime of the IoTUEs.TheDRXmechanism allowsUEsto stop monitoring the Physical Downlink Control Channel(PDCCH) and to enter low-power consumption mode toextend its battery lifetime. Without DRX mechanism, theLTE UEs have to continuously monitor the PDCCH inevery subframe in order to check UE-specific schedulingassignments such as Downlink (DL) resource allocation,Uplink (UL) grants, and PRACH (Physical Random AccessChannel) responses. Not only the UEs but also the EvolvedNode B (eNodeB) can also benefit from the DRXmechanismto reduce its power and signal consumptions, such as theChannel State Information (CSI) or Sounding Reference Sig-nal (SRS), and to improve its resource utilization. However,the main issue of the DRX mechanism is that it may poseunexpected delay if the UEs are in the sleep mode whilethe scheduling information arrived. Particularly, it should benoted that the PDCCH carries the scheduling informationrather than service data and the scheduling information is

Hindawi Publishing CorporationMobile Information SystemsVolume 2015, Article ID 971538, 11 pageshttp://dx.doi.org/10.1155/2015/971538

2 Mobile Information Systems

interleaved in the PDCCH in order to reduce the collisionof blind decoding of the UEs. This implies that extendingthe on duration time of the DRX mechanism may not helpto overcome this issue. Unfortunately, the specifications saynothing about the means of the DRX parameter configura-tion. Furthermore, different from the single DRX cycle in theUniversal Mobile Telecommunications System (UMTS), theLTE network introduces two types of DRX cycles, the shortand longDRX cycles, which complicates theDRXparametersconfiguration.

The DRX parameters must be configured to maximizepower saving while the constraints of the communicationlatency and system throughput are satisfied. The DRX cyclesallow UE to temporarily turn off its radio interface andenter a sleep mode for several Transmission Time Intervals(TTIs). On the one hand, too short of DRX cycle will lead toinefficient power saving. Too long DRX cycle, on the otherhand, will result in long communication latency decreasingsystem throughput. Thus, the scheduler of the IoT networksmust manage the tradeoff among the power consumption,communication latency, and the system throughput.

In this paper, we designed a fuzzy-based power savingscheduling scheme for IoT over LTE/LTE-Advanced net-works to deal with the issues of the radio spectrum manage-ment and the power consumption as well as the unexpecteddelay from UE’s scheduling and resource allocation per-spective instead of complicated DRX parameters adjustment.Not only individual IoT UE’s real-time requirement but alsothe overall network performance is taken into account. Themain issue of the DRX parameters adjustment is that it willcause the inter-parameter interaction. Although the powersaving can be achieved by adjusting the DRX parametersdynamically, higher signaling overhead is also introduced [2].Since the UE’s DRX parameters are semistatically configuredby the eNodeB, the parameters can only be adjusted in asignaling procedure [3]. In addition, it is more difficult toadjust the DRX parameters dynamically because the UEsautomatically enter the sleep mode, and the eNodeB does notknow about this change.Moreover, because of the uncertaintyof the wireless network and service traffic, optimizing theDRX parameters is challenging [2]. In contrast, dealing withthese issues from the perspective of the UE’s scheduling andresource allocation is more suitable to be realized in practice.The main idea behind the proposed scheme is to schedulethe UEs which in the period of the on duration to preventthe unexpected delay. To successfully receive the schedulinginformation, reduce the power consumption, and meet thereal-time requirement of IoT applications, the proposedscheme considers the UE’s DRX cycle and scheduling latencywhen allocating radio resources. Furthermore, in addition tosatisfying individual UE’s requirements, maintaining overallsystem throughput is necessary.

The major contributions of this paper are that this paperis the first paper taking these two important issues, thepower consumption and radio spectrum management, intoaccount for IoT over LTE/LTE-Advanced networks, andour proposed scheme that benefited from the characteris-tics of low complexity of fuzzy theory can be realized inpractice. In particular, different from previous works, our

design considers the key aspects of the LTE/LTE-Advancednetworks. In addition, we proposed a systematic simulationmodel to investigate the DRX performance based on the con-cept of the LTE networks. The simulation results show thatour proposed scheme can provide approximately decisionto meet the requirements of the DRX cycle and schedulinglatency and to save about half of energy consumption for IoTUEs compared to conventional approaches.

The rest of this paper is organized as follows. Section 2provides an overview on related work. Section 3 introducesthe discontinuous reception mechanism used in LTE/LTE-Advanced networks. Section 4 formulates the problem ofmaximum radio resource utilization. Section 5 describesthe proposed fuzzy-based power saving scheduling scheme.Section 6 presents the simulation model. The simulationresults and the discussions are presented in Section 7. Finally,Section 8 concludes this paper.

2. Related WorkThe recent researches [2–9] are concentrated on modeling,investigating, or analyzing the DRX mechanism for 3GPPLTE/LTE-Advanced networks, and these studies show thatthe DRX mechanism has the ability to save UE’s powerconsumption. However, the research [3] points out that manystudies neglect the key aspects of LTE. In [4, 5, 7], the semi-Markov chain is used to model and analyze the wake-uplatency and power saving factor of the DRX mechanism.A light sleeping mode is proposed in [10]. From hardwareperspective, the light sleep mode allows UE to just turn offits power amplifier in order to reduce energy consumptionwhile satisfying the delay requirement of Quality of Service(QoS).

In [2], the authors proposed a concept of burst-basedscheduling scheme for the LTE networks to increase powersaving efficiency while the desired QoS of UEs are satisfied.The main idea of this paper is to prevent UEs from enteringthe period of the opportunity for DRXwith no data receptionby adjusting scheduling priority using forward and backwardstrategies. However, the paper only considers fixed inactivetimers, and the multi-DRX cycle scenario and the multipleaccess scheme of the LTE networks have not been taken intoaccount.

The guaranteeing quality of service is used to optimize theDRX parameters in [1, 11]. In [1], the authors proposed twoschemes, the three-stage scheme and the packet schedulingscheme, for the UEs and the eNodeB to cooperate with eachother, respectively. The UE’s power is saved by reducing itswake-up period. To accomplish this goal, these algorithmsmust determine the UE’s DRX parameters, such as theshort and long DRX cycles, on duration time, and the inactive timer, in advance. However, there may be an issue ofstarvation in the packet scheduling scheme since this paperonly considers higher channel rate and stringent data that aregoing to be swapped out from the buffer.

In [11], the authors mainly proposed two decisionalgorithms, the DRX parameters decision algorithm andscheduling-start offset decision algorithm.The former is usedto determine the UE’s on duration time, and the latter is

Mobile Information Systems 3

On duration

Opportunityfor DRX

On duration

Opportunity for DRXOpportunityfor DRX

On duration

Inactivity timer Short DRX cycle Short DRX cycle Long DRX cycle

· · · · · ·

Figure 1: Operation of the DRX cycle in RRC CONNECTED state.

used to disperse the UE’s on duration time. The main ideaof this paper is to prevent overlapping of UE’s on durationtime in order to fully utilize the network resource. However,the algorithmsmight be hard to be deployed in practice sincethe UE’s DRX parameters are semistatically configured by theeNodeB rather than the UEs and the parameters can only beadjusted in a signaling procedure [3]. In addition, the authorsalso neglect the key aspects of the multiple access scheme ofthe LTE networks.

However, from our understanding, the following two keyaspects of the LTE networks are usually neglected by recentresearch. (1) Extending the on duration time may not helpto improve QoS of service traffic since the UEs check thePDCCH scheduling information rather than service data inthe on duration period and the scheduling information isinterleaved in the PDCCH in order to reduce the collision ofblind decoding. (2) The UE’s DRX parameters are semistat-ically configured by the eNodeB rather than the UEs; theparameters can only be adjusted in a signaling procedure[3]. In addition, most of them do not consider multicellularnetworks in their simulation works.

3. Discontinuous Reception in LTE

The DRX mechanism is known as a sleep mode in whichUEs can stop monitoring the PDCCH and enter low-power consumption states to extend its battery lifetime. Asmentioned in Section 1, the PDCCH carries the schedulinginformation, such as the UE-specific scheduling assignmentsfor Downlink (DL) resource allocation, Uplink (UL) grants,and PRACH (Physical Random Access Channel) responses,rather than service data for the UEs, and the schedulinginformation is interleaved in the PDCCH in order to reducethe collision of the UE’s blind decoding. Depending onUEs in active or idle mode, two mobile’s Radio ResourceControl (RRC) states, RRC IDLE and RRC CONNECTED,are used in the DRX mechanism. When a UE in RRC IDLEstate, the eNodeB will free up its resources for other usersto maximize the UE’s battery life and will know nothingabout this UE. After that, the procedure of paging requestwill be typically used if the eNodeB wishes to contact thisUE, and procedure will take more time to establish theconnection between them. The eNodeB can negotiate theDRX parameters with UE through the RRC configurationprocedure. In this paper, we concentrated on dealing with

the DRX issues in the RRC CONNECTED state since thestate enabling data transmission and reception is important.

In the LTE networks, different from the single DRX cycleused in the UMTS, two types of DRX cycles, the short andlong DRX cycles, are introduced. The long DRX cycle offersgreater opportunity to achieve better power saving than theshort DRX cycle. The short DRX cycle is optional. If bothare configured by the eNobeB, the UE starts with 16 shortDRX cycles, 2 to 640 subframes, and then enters a long DRXcycle, 10 to 2560 subframes, without receiving any schedulinginformation on the PDCCH.

The DRX parameters, typically, consist of three timers,the inactivity timer, on duration timer, and opportunity forDRX timer as shown in Figure 1. The UE should start the onduration timer in the subframe which satisfied (1) and (2) forthe longDRX cycle and short DRX cycle, respectively [12–14]:

[(SFN × 10) + subframe number]

⋅ mod (Short DRX cycle) = (DRX start offset)

⋅ mod (Short DRX cycle) ,

(1)

[(SFN × 10) + subframe number]

⋅ mod (Long DRX cycle) = (DRX start offset) ,(2)

where the System Frame Number (SFN) which consists of 10subframes will be reset to zero if it is greater than 1023 and theDRX start offset indicates the start of the on duration timer.

The three timers, the inactivity timer, on duration timer,and the opportunity for DRX timer, are contributed to theDRX mechanism. First, the UE stays awake for an inactivitytime, 1 to 2560 subframes, when it received every PDCCHinformation. If the inactivity timer expires, the UE monitorsthe PDCCH for an on duration time, 1 to 200 subframes, andthen it may go to sleep for an opportunity for DRX time,periodically. However, it should be noted that the DRX-to-connected latency is specified as less than 50ms for the LTEnetworks and less than tightly 10ms for the LTE-Advancednetworks in the 3GPP specification [15]. This implies that themaximumDRX cycle must be 32 Transmission Time Interval(TTI) or 32 one-millisecond subframes for the LTE networks.In addition, theDRXcycles are expressed as power of 2 and allDRX cycles are always a multiple of the maximumDRX cyclein order to maintain the periodicity of DRX cycle. Therefore,there are six DRX cycle combinations and six DRX cycle


2 2 2 2 2 2 2

16

2 2 2 2 2

1 1 1 1 1

16

4 4 4 4 4 4

16

16 short DRX cycles 1 long DRX cycle

16 short DRX cycles

8 short DRX cycles

2 long DRX cycles

32

1 long DRX cycle

· · ·

· · ·

· · ·

Figure 2: Six DRX cycle combinations.

granularities, 32, 16, 8, 4, and 2 subframes, and 1 subframe,can be used in LTE DRX mechanism as shown in Figure 2.

The selection of DRX combination is based on the Con-trol Channel Elements (CCEs) aggregation level of PDCCH,which indicates the numbers of consecutive CCEs interleavedin the control region. The CCE aggregation level is set basedon theUE’s channel condition.TheCCEcarries theDownlinkControl Information (DCI), which contains resource assign-ments and control information for each UE. For example, inthe case of the UE with good channel condition, one CCEmight be sufficient, but eight CCEs might be required in thecase of the UE with poor channel condition. Therefore, theUE’s reported Channel Quality Indicator (CQI) should betaken into account in the scheduling decision to ensure theoverall system performance.

4. Problem Formulation

We consider the downlink transmission of the LTE networksin the proposed scheme. In this section, the problem of UEscheduling and radio resource allocation of the LTE networksis formulated as maximum radio resource utilization basedon the fuzzy theory. We aim to find the UEs with higherscheduling priority and then to allocate radio resources forthem. Given an eNodeB and a set of UEs UE = {UE𝑘, 1 ≤𝑘 ≤ 𝑁UE} with its corresponding Channel Quality Indicator(CQI) 𝐶𝑘, the scheduling efficiency which involves the exactDRX time, CQI, and scheduling latency is given as

SE𝑘 =𝐶𝑘

𝐸𝑘𝐿𝑘, ∀𝑘 ∈ UE, 0 ≤ ∀𝐶𝑘 ≤ 15, (3)

where 𝐸𝑘 is the exact DRX timer of 𝑘th UE and 𝐶𝑘 and𝐿𝑘 denote the CQI index and scheduling latency of UE𝑘,respectively. The CQI index 0 represents the out of range

Fuzzy membership

function

Low-complexityintersection

function

Exact DRX cycleCQI

Guaranteed scheduling latency

Scheduler

Resourceallocator

RRM

Figure 3: The structure of the proposed scheme.

due to bad signal quality or DTX. Thus, the maximum radioresource utilization can be defined as

𝐽 = max𝛼

𝑁RB

∑𝑛

𝑁UE

∑𝑘

𝛼𝑘,𝑛SE𝑘 (4)

subject to𝑁UE

∑𝑘

𝛼𝑘,𝑛 = 1, ∀𝛼𝑘,𝑛 ∈ [0, 1] , (5)

where 𝑁RB denotes the number of Resource Blocks (RBs) indownlink transmission and𝑁UE represents the total numberof UEs served by the eNodeB. The number of RBs dependson the system bandwidth of the eNodeB. The restriction of∑𝑁UE𝑘

𝛼𝑘,𝑛 = 1 entails that each resource block can only beallocated to a UE. Particularly, the 𝛼 ∈ [0, 1] is fuzzy set notcrisp set in the proposed scheme in order tomake appropriatedecision. The resource block is the smallest entity that canbe scheduled for the UEs in the frequency domain. Theprimary goal of (4) is to maximize the scheduling efficiencyof the system bandwidth by adjusting the fuzzy set 𝛼. Theappropriate fuzzy set𝛼 can be figured out by using three fuzzymembership functions and a low-complexity intersectionfunction described in the next section.

5. Proposed Fuzzy-Based Power SavingScheduling Scheme

In this section, we introduce a fuzzy-based power savingscheduling scheme to deal with the issues of the radiospectrum management and the power consumption as wellas the unexpected delay for IoT over LTE networks. Theproposed scheme considers not only individual IoT UE’sreal-time requirement but also overall system performance.We intend to allocate RBs to those UEs who have stringentreal-time requirement, exact DRX cycle, and higher CQI.The tradeoff among them should be determined to achievethis goal. Fortunately, fuzzy theory provides means to makeapproximate decisions especially in multiobjective problems.The data even in different result spaces can be combinedwith each other through fuzzy set operations, such as union,intersection, and complement operation. An approximatedecision is typically decided according to the highest mem-bership values intersected by fuzzy operation.

A block diagram of designed fuzzy-based approach isshown in Figure 3. The proposed scheme mainly consistsof two fuzzy functions, the fuzzy membership function andlow-complexity fuzzy intersection function. The IoT UE’s


PRB allocated

TTI

TTI

Maximum DRX cycle

On duration

Opportunityfor DRX

Exact DRX cycle

kms

· · ·

k − 32ms

Figure 4: The idea of the exact DRX cycle.

CQI, exact DRX cycle, and guaranteed scheduling latencyare considered as the scheduling metrics. The approximatescheduling decision is made according to the result of thelow-complexity intersection function and performed by thescheduler and resource allocator of the Radio ResourceManagement (RRM) in the LTE/LTE-Advanced networks.In the LTE networks, the RRM comprises scheduler andresource allocator that are responsible for the UE selectionand radio resource assignment, respectively [16].The guaran-teed scheduling latency and exact DRX cycle is used to ensureindividual IoT UE’s requirements such as the power savingand real-time demand. In addition, we maintain overallsystem performance according to the CQI. The details ofthree metrics as well as the fuzzy membership function,low-complexity fuzzy intersection function, and an adaptivepower control function are described as follows.

5.1. ExactDRXCycle. In the proposed scheme, the exactDRXcycle timer is used to prevent the unexpected delay causedby the sleep mode. To achieve this goal, the eNodeB mustschedule the UEs within the on duration period in order tosuccessfully receive the scheduling information. Because theDRX parameters are configured by the eNodeB, the eNodeBhas the ability to handle the UE’s exact DRX cycle. Themaximum DRX cycle that is considered as the timer of theshort and long DRX cycle combination is shown in Figure 4.Because of the constraint of 50 subframes DRX-to-connectedlatency, themaximumDRXcyclemust bewithin the 𝑘−32msand 𝑘ms for the LTE networks.The exact DRX cycle must bethe period of 𝑘 − 32ms to 𝑘-Opportunity for DRX time. Inthe proposed scheme, if the exact DRX cycle expires, it willbe restarted when the IoT UEs are scheduled and allocatedRBs again by the eNodeB. The RB, which consists of 12subcarriers of 15 kHz, and the 1ms subframe, which consistsof two RBs, are the smallest entity that can be scheduledfor the UEs in the frequency domain and time domain,respectively. Furthermore, network operators can adjust thelength of the maximum DRX cycle to apply to the LTE-Advanced networks.

5.2. Guaranteed Scheduling Latency. In the proposed scheme,the guaranteed scheduling latency configured by IoT man-ufacture is used to specify and to ensure the real-time

Table 1: CQI table.

CQI index Modulation scheme Coding rate ∗ 1024 Efficiency0 Out of range1 QPSK 78 0.152 QPSK 120 0.233 QPSK 193 0.384 QPSK 308 0.605 QPSK 449 0.886 QPSK 602 1.187 16-QAM 378 1.488 16-QAM 490 1.919 16-QAM 616 2.4110 64-QAM 466 2.7311 64-QAM 567 3.3212 64-QAM 666 3.9013 64-QAM 772 4.5214 64-QAM 873 5.1215 64-QAM 948 5.55

PRB allocated

TTI

Scheduling latency

…

PRB allocated

· · ·

Figure 5: An example of the scheduling latency.

requirement of IoT UEs. The scheduling latency, a real-timeindicator, is the nonproductive time between the end of lastRB and the start of next RB as shown in Figure 5. Thescheduling latency usually consists of the context switchingtime and scheduling decision time. In the proposed scheme,each IoTUE has a timer of the guaranteed scheduling latencyto ensure the scheduling latency. The timer will be restartedwhen the UEs are scheduled and allocated RBs again by theeNodeB if the guaranteed scheduling latency expires.

5.3. Channel Quality Indicator. The four-bit channel qualityindicator ranged from 0 to 15, as shown in Table 1, whichindicates the highest Modulation and Coding Scheme (MCS)and the maximum data rate with less than 10% block errorratio that UE can support. The CQI is calculated at theUEs according to its Signal to Interference plus Noise Ratio(SINR) and the CQI is regularly reported from the UE to theeNodeB.The reporting intervals of the CQI lie between 2 and160ms [13]. A higher CQI value will result in higher data rateand higher MCS profile used. In the proposed scheme, wemaintain the overall system performance based on the CQIas high as possible. Since relatively sufficient buffer is usedin the eNodeB, the channel quality rather than service trafficdominates the QoS in the cellular networks.


5.4. Fuzzy Membership Function. In the proposed scheme,the goal of themembership functions is to convert threemet-rics, the UE’s exact DRX cycle, scheduling latency, and CQI,into degree of memberships. Recall that, in Problem Formu-lation, we attempt to maximize the scheduling efficiency byadjusting the fuzzy set 𝛼. Since this fuzzy set is related to thesethree metrics, we fuzzified them into memberships to figureout the intersection of the three metrics by the intersectionfunction. The three fuzzy membership functions used in thescheme are given as

𝜇𝑒,𝑘 = (𝐸𝑘2

∑𝑗𝐸𝑗2)

−2

,

𝜇𝑙,𝑘 = (𝐿𝑘2

∑𝑗𝐿𝑗2)

−2

,

𝜇𝑐,𝑘 = ((CQImax − 𝐶𝑘)

2

∑𝑗(CQImax − 𝐶𝑗)

2)

−2

,

(6)

where the notation 𝜇 denotes the fuzzy membership withoutloss of generality.The 𝐸𝑘, 𝐿𝑘, and𝐶𝑘 are the exact DRX cycle,guaranteed scheduling latency, and the CQI of 𝑘th IoT UE,respectively. The CQImax which equals 15 is the maximumCQI index.

5.5. Low-Complexity Fuzzy Intersection Function. In thispaper, to reduce the computational complexity of the schedul-ing decision, we propose a low-complexity and simple inter-section approach.The scheduling decision is made accordingto the three metrics, the UE’s exact DRX cycle, schedulinglatency, and CQI. The intersection function is formulated as

𝛼𝑘 (𝐸𝑘, 𝐿𝑘, 𝐶𝑘) = 𝜇𝑒,𝑘 ⋅ 𝜇𝑙,𝑘 ⋅ 𝜇𝑐,𝑘, (7)

where each IoT UE’s membership intersection can beregarded as the volume or capacity in the exact DRX cycle,guaranteed scheduling latency, and the CQI planes fromthe capacity point of view. To present our ideas clearly, weillustrate an example of the IoT UEs intersections as shownin Figure 6, in which each node presents the product of theUE’s three metric memberships.

The IoT UEs which are closer to the upper right side havehigher priority to be considered for scheduling. The numberof IoT UEs can be scheduled and allocated RBs at a timeinstant depending on the available system bandwidth of theeNodeB.

5.6. Adaptive Power Control Algorithm. In the future, a lotof IOT UEs are expected to be deployed indoors where thesmall cells, such as femtocells, are necessary to be deployedto achieve a high spectral efficiency and to provide betterQoS for IoT UEs. In our previous study [17], we proposed aneffective Adaptive Smart Power Control Algorithm (ASPCA),which can be applied to cluster IoT UEs in the coveragehole and to deal with the cross-tier interference issues bydetermining an appropriate serving range of home eNodeB

00.02

0.040.06

0.08

00.020.040.060.08

Scheduling latency (k)DRX cycle (k)

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

CQI (

k)

Figure 6:The example of three metrics intersection, the CQI, exactDRX cycle, and the guaranteed scheduling latency.

without requiring complicated negotiation among them.Theproposed ASPCA not only improves the overall systemperformance but also takes QoS of UEs into account.

6. Simulation Model

In this section, we introduce a heterogeneous simulationmodel, in which an outdoor macro eNodeB is located incenter of the map, and six surrounding femto eNodeBs aredeployed around the macro eNodeB. The macro eNodeB’scoverage is overlaid with the six femto eNodeBs. In addition,about 70% of IoT UEs are randomly deployed in a 50 ×50m2 indoor area, and others are randomly deployed in therange of 100 meters of each femto eNodeB. In addition, allIoT UEs of macro eNodeB are randomly deployed in theRange of Interest (ROI). The main simulation parametersare summarized in Table 2. The transmission power of thefemto eNodeB is 20 dBm, and the transmission power ofthe macro eNodeB is 46 dBm. The details of channel fadingmodel, propagation model, data rate calculation, schedulinglatency parameter, and the power consumption model aredescribed as follows.

6.1. Channel Fading Model. To provide realistic results, weuse the Rayleigh fading channel model to reflect the effect ofurban environments. Because of multipath propagation, thechannel fading will lead to Bit Error Rate (BER) increasing.The BER is defined as the number of received bit errorsdivided by the total number of transmitted bits over a com-munication channel. The analytical equation in the Rayleighchannel [18] is given as

BER = 𝑀 − 1

𝑀 log2(𝑀)

(1 − √𝛿

𝛿 + 1) ,

𝛿 =3 log2(𝑀) ⋅ 𝐸rb

(𝐼rb/Δ𝑓rb + 𝑁𝑜) Δ𝑓rb𝛾 ⋅

1

𝑀2 − 1,

(8)


Table 2: Simulation configuration.

Network layout Hexagonal/2-tierTransmission scheme DownlinkIntercell distance 100mCarrier frequency Band 3, 1800MHz

System bandwidth 20MHz for macrocell,1.4MHz for femtocells

Antenna type Omnidirectional with 0 dBgain

Noise density −174 dBm/Hz

Propagation model TS 36.942 for urbanenvironment

Penetration loss forfemtocells 12 dBm

Channel fading model Rayleigh fadingNumber of macrocells 1Number of femtocells 6Transmit power ofmacrocell 46 dBm

Maximum transmissionpower of femtocells 20 dBm

Minimum transmissionpower of femtocells −20 dBm

Number of macrocell users 200Number of users perfemtocell 20

Proportion of cell-edgeusers per femtocell 0.3

Simulation duration 512ms

where 𝑀 denotes the constellation size of adopted 𝑀-aryQAM signal which is related to the adopted MCS. Theadopted MCS is configured according to the CQI reportedby each IoT UE.

6.2. PropagationModel andData Rate Calculation. Theprop-agation model specified by 3GPP [19] has been consideredand implemented for the urban environment as

PLoutdoor = 40 (1 − 4𝑒−3Dhb) log

10(𝑅)

− 10log10(Dhb) + 21log

10(𝑓) + 80 dB,

PLindoor = 40 (1 − 4𝑒−3Dhb) log

10(𝑅)

− 10log10(Dhb) + 21log

10(𝑓) + 80 dB

+ pentration loss,

(9)

where 𝑅 is the separation from the cell to the user inkilometers, 𝑓 is the carrier frequency inMHz, and Dhb is theantenna height of cell in meters. In our simulation, a lookuptable, as given in Table 3, is used to map SINR estimate tospectral efficiency [20] in order to calculate the data rate.

6.3. Scheduling Latency Parameters. TheQoS Class Identifier(QCI) has been standardized [1] as shown in Table 4, in which

Table 3: Lookup table for mapping SINR estimate to spectralefficiency.

CQI index SINR estimate (dB) Data rate (bps/Hz)1 −6.7 0.15232 −4.7 0.23443 −2.3 0.37704 0.2 0.60165 2.4 0.87706 4.3 1.17587 5.9 1.47668 8.1 1.91419 10.3 2.406310 11.7 2.730511 14.1 3.332312 16.3 3.902313 18.7 4.523414 21.0 5.115215 22.7 5.5547

Table 4: QCI characteristics.

QCI index Packet error rate Packet delay budget (ms)1 10−2 1002 10−3 1503 10−3 504 10−6 3005 10−6 1006 10−6 3007 10−3 1008 10−6 3009 10−6 300

the packet delay budget is the latency that a packet receivesbetween the IoT UE and the eNodeB. In the simulation, fixed50ms delay budgets are used as the scheduling latency of theIoT UEs for performance evaluation.

6.4. Power ConsumptionModel. In this section, we introducea power efficiency indicator to emulate the power consump-tion of DRX mechanism. In the simulation, unlike otherstudies, we focus on investigating the power consumptionof the scheduling information reception on the PDCCH. Alookup table to map CQI index to power consumption isshown in Table 5. The power efficiency indicator is given as

PE =𝑃subframe

𝑁subframebit

, (10)

where the 𝑃subframe = 500mw/ms denotes the UE’s powerconsumption of reception and the 𝑁subframe

bit denotes thenumber of bits per subframe. By defining the power efficiency,we can calculate the IoT UE’s power consumption accordingto its signal quality. In the case of DRX adapted, the IoTUE’s power consumption is defined as the PE multiplied bythe number of PDCCH bits. On the other hand, the 𝑃subframe


Table 5: Lookup table for mapping CQI to power consumption.

CQI indexCCE

aggregationlevel

Number ofPDCCHbits

Powerconsumption

(mw)1 8 576 11520.002 4 288 3789.473 4 288 2285.714 4 288 1440.005 2 144 489.796 2 144 363.637 2 144 290.328 2 144 225.009 2 144 178.2110 2 144 157.2011 2 144 129.2612 2 144 109.9213 1 72 47.4314 1 72 41.8615 1 72 38.62

0

2

4

6

8

10

12

14

16

CQI

10 155 200UEs

Figure 7: The CQI of IoT UEs of a femto eNodeB.

multiplied by theUE’s scheduling latency is defined as the IoTUE’s power consumption in the case of being without DRX.

7. Results and Discussion

In this section, we present simulation results to verify that ourproposed scheme has the ability to ensure theDRX constraintand real-time requirement and to maintain overall systemperformance in IoT over the LTE/LTE-Advanced networks.First, we investigate the scheduling performance in termsof the exact DRX cycle and guaranteed scheduling latencyas shown in Figure 7 to Figure 10. After that, we examinethe power consumption efficiency of our proposed schemeas shown in Figure 11. Then we investigate the performanceof the DRX mechanism in terms of average data rate as

UE 1UE 2UE 3UE 4UE 5




5 10 15 20 25 30 35 40 45 500TTI (ms)

0

10

20

30

40

50

60

Sche

dulin

g lat

ency

(ms)

Figure 8: The performance of guaranteed scheduling latency inproposed scheme.

0

5

10

15

20

25

30

35

DRX

cycle

(ms)

5 10 15 20 25 30 35 40 45 500TTI (ms)





Figure 9:The performance of exact DRX cycle in proposed scheme.

shown in Figure 12. Next, we compare the proposed schemewith conventional schemes, Round-Robin (RR), and Max-C/I scheme as shown in Figure 13. Finally, we show that therequirements of exact DRX cycle and guaranteed schedulinglatency can be satisfied as shown in Figure 14.

In our simulation, the CQI does not vary since the low-mobility of IoT UEs is assumed and deployed in the indoorenvironment, thence no fast-fading effects. In addition, 32msexact DRX cycle, 50ms guaranteed scheduling latency, and


0

0.5

1

1.5

2

2.5

3

3.5

Inte

rsec

tion

5 10 15 20 25 30 35 40 45 500TTI (ms)





×10−4

Figure 10: The fuzzy membership intersections of three metrics.

No DRXDRX

10 20 30 40 500TTI (ms)

0

1

2

3

4

5

6

7

8

9

10

Aver

age p

ower

cons

umpt

ion

on P

DCC

H re

cept

ion

(mw

)

Figure 11: The average power consumption comparison of theproposed schemewithDRXand the proposed schemewithoutDRX.

1ms inactivity timer are used in the normal case. In themulti-DRX cycle case, we examine the impact of six different DRXcycles, 32ms, 16ms, 8ms, 2ms, and 1ms, which are randomlyassigned to the IoT UEs.

Our simulator generates three metrics, the CQI, guaran-teed scheduling latency, and the exact RDX cycle as shownin Figures 7, 8, and 9. The results of these three metricsintersections are shown in Figure 10, in which we can clearlysee that there are few intersections at about 32ms since fixed32ms exact DRX cycle is used. Even though fixed 50ms

Proposed DRXProposed multi-DRX cycle

0

50

100

150

200

250

300

Aver

age d

ata r

ate (

bits/

ms)

5 10 15 20 25 30 35 40 45 500TTI (ms)

Figure 12: The average data rate comparison of single DRX cycleand multi-DRX cycle.

Proposed DRXProposed multi-DRX cycle

RRMax-C/I

0

50

100

150

200

250

300Av

erag

e thr

ough

put (

bits/

ms)

2 3 4 51Scheduling schemes

Figure 13: The throughput comparison of the proposed scheme,Round-Robin (RR) scheme, and the Max-C/I scheme.

of guaranteed scheduling latency is adopted, the schedulingdecision is still dominated by short exact DRX cycle due tomore stringent time restriction. Comparing Figure 6 withFigures 7 and 8, we can observe that even though the outdoorUEs of femtocell 0, UE 4, UE 8, UE 16, UE 17, UE 18, andUE 19, have bad CQI, they can still reach their constraints ofthe guaranteed scheduling latency and exact DRX cycle. Inaddition, it should be noted that there are only 6 RBs that canbe allocated to IoT UEs for a TTI due to intended narrowingof 1.4MHz system bandwidth.

A comparison of average power consumption betweenthe case of being with DRX and the case of being withoutDRX mechanism is shown in Figure 11. The figure showsthat the proposed scheme can conserve about half of energyconsumption compared to the case of being without DRXmechanism.


Single DRX cycleMulti-DRX cycleScheduling latency

0

0.5

1

1.5

Satis

fact

ion

(%)

50 100 150 200 250 300 350 400 450 5000TTI (ms)

Figure 14: The satisfaction of the exact DRX cycle and guaranteedscheduling latency.

A comparison of single DRX cycle and multi-DRX cycleis shown in Figure 12. We can observe that the multi-DRXcycle has more stable data rate than the single DRX cycle.Evidently, the diversity of metrics, such as multi-DRX cycleand multischeduling latency, can benefit our scheme to makebetter decision.

The throughput comparison of the proposed scheme,RR, and the Max-C/I scheme is shown in Figure 13. SinceRound-Robin scheme serves IoT UEs in turn without takingthe instantaneous channel quality into account, it gets theworst throughput. Because the Max-C/I scheme prefers toserve UEs which have better channel condition, it gets thebest throughput but loses fairness. The proposed scheme canprovide a better throughput aswell as fairness for the IoTUEs.

Finally, the satisfaction of the proposed scheme is shownin Figure 14, which shows that the requirement of the exactDRX cycle and the guaranteed scheduling latency can beaccomplished by our fuzzy-based power saving schedulingscheme.

8. Conclusions

In this paper, we proposed a fuzzy-based power savingscheduling scheme for IoT over the LTE/LTE-Advancednetworks to deal with the issues of the radio resource man-agement and power consumption as well as the unexpecteddelay caused by theDRXmechanism from the scheduling andresource allocation perspective. The simulation results showthat our scheme has the ability to leverage the tradeoff amongthree individual metrics and overall system performance.Hence, the IoT UE’s requirements can be guaranteed. Inaddition, we exam the performance of multi-DRX cycle, andwe find that the diversity of the systemmetrics can help fuzzy-based approach make better decision. The proposed schemeis useful to be applied to the IoT over the LTE/LTE-Advancenetworks in practice.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgment

This work was partially supported byMinistry of Science andTechnology, Taiwan, under Grants nos. NSC 102-2221-E-008-039-MY3 and 104-2221-E-008-039-MY3.

References

[1] J.-M. Liang, J.-J. Chen, H.-H. Cheng, and Y.-C. Tseng, “Anenergy-efficient sleep scheduling with QoS consideration in3GPP LTE-advanced networks for internet of things,” IEEEJournal on Emerging and Selected Topics in Circuits and Systems,vol. 3, no. 1, pp. 13–22, 2013.

[2] Y.-W. Chen, M.-H. Lin, and Y.-T. Su, “Power saving efficiencyanalysis of QoS scheduling in the LTE network featuringdiscontinuous reception operation,” IEICE Transactions onCommunications, vol. 97, no. 10, pp. 2212–2221, 2014.

[3] G. Stea and A. Virdis, “A comprehensive simulation analysis ofLTE discontinuous reception (DRX),” Computer Networks, vol.73, pp. 22–40, 2014.

[4] K. Wang, X. Li, and H. Ji, “Modeling 3GPP LTE advanced DRXmechanism under multimedia traffic,” IEEE CommunicationsLetters, vol. 18, no. 7, pp. 1238–1241, 2014.

[5] K. Zhou, N. Nikaein, and T. Spyropoulos, “LTE/LTE-a dis-continuous reception modeling for machine type communica-tions,” IEEE Wireless Communications Letters, vol. 2, no. 1, pp.102–105, 2013.

[6] S. Jin andD. Qiao, “Numerical analysis of the power saving witha bursty traffic model in LTE-Advanced networks,” ComputerNetworks, vol. 73, pp. 72–83, 2014.

[7] S. Fowler, A. O. Shahidullah, M. Osman, J. M. Karlsson, and D.Yuan, “Analytical evaluation of extended DRX with additionalactive cycles for light traffic,” Computer Networks, vol. 77, pp.90–102, 2015.

[8] E. Liu and W. Ren, “Performance analysis of a generalizedand autonomous DRX scheme,” IEEE Transactions on VehicularTechnology, vol. 64, no. 5, pp. 2148–2153, 2014.

[9] A. T. Koc, S. C. Jha, R. Vannithamby, and M. Torlak, “Devicepower saving and latency optimization in LTE-a networksthrough DRX configuration,” IEEE Transactions on WirelessCommunications, vol. 13, no. 5, pp. 2614–2625, 2014.

[10] K.-C. Ting, H.-C. Wang, C.-C. Tseng, and F.-C. Kuo, “Energy-efficient DRX scheduling for QoS traffic in LTE networks,” inProceedings of the 9th IEEE International Symposium on Paralleland Distributed Processing with Applications (ISPA ’11), pp. 213–218, IEEE, Busan, South Korea, May 2011.

[11] L.-P. Tung, Y.-D. Lin, Y.-H.Kuo, Y.-C. Lai, andK.M. Sivalingam,“Reducing power consumption in LTE data scheduling with theconstraints of channel condition andQoS,”Computer Networks,vol. 75, pp. 149–159, 2014.

[12] 3GPP TS 36.321, Evolved Universal Terrestrial Radio Access (E-UTRA); Medium Access Control (MAC) Protocol Specification,v12.7.0, 2015.

[13] 3GPP TS 36.321, Evolved Universal Terrestrial Radio Access (E-UTRA); Physical Layer Procedures, v12.7.0, 2015.


[14] 3GPP TS 36.331, “Evolved Universal Terrestrial Radio Access(E-UTRA); Radio Resource Control (RRC); Protocol specifica-tion,” v 12.7.0, 2015.

[15] I. Grigorik, High Performance Browser Networking: What EveryWeb Developer Should Know about Networking and Web perfor-mance, O’Reilly Media, 2013.

[16] M. I. Salman, M. Q. Abdulhasan, C. K. Ng, N. K. Noordin, A.Sali, and B. Mohd Ali, “Radio resource management for green3gpp long term evolution cellular networks: review and trade-offs,” IETE Technical Review, vol. 30, no. 3, pp. 257–269, 2013.

[17] Y.-W. Kuo, L.-D. Chou, and Y.-M. Chen, “Adaptive smart powercontrol algorithm for LTE downlink cross-tier interferenceavoidance,” in Proceedings of the 11th EAI International Con-ference on Heterogeneous Networking for Quality, Reliability,Security and Robustness (QSHINE ’15), Taipei, Taiwan, August2015.

[18] Y. S. Cho, J. Kim, W. Y. Yang, and C. G. Kang, MIMO-OFDMWireless Communications with MATLAB, Wiley, 2010.

[19] 3GPP TS 36.942, “Evolved Universal Terrestrial Radio Access(E-UTRA); Radio Frequency (RF) system scenarios,” Tech. Rep.v12.0.0, 2014.

[20] H. Zarrinkoub,Understanding LTE withMATLAB: FromMath-ematical Modeling to Simulation and Prototyping, JohnWiley &Sons, 2014.

Submit your manuscripts athttp://www.hindawi.com

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttp://www.hindawi.com

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation http://www.hindawi.com Volume 2014


Applied Computational Intelligence and Soft Computing

Advances in

Artificial Intelligence


Advances inSoftware EngineeringHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

http://www.hindawi.com Volume 2014

Advances in

Multimedia


Biomedical Imaging


ArtificialNeural Systems

Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling & Simulation in EngineeringHindawi Publishing Corporation http://www.hindawi.com Volume 2014

The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in


Documents

Research Article Power Saving Scheduling Scheme for