Performance Evaluation for Energy-HarvestingMachine-Type Communication in LTE-A System

Mei-Ju Shih∗, Yuan-Chi Pang†, Guan-Yu Lin‡, Hung-Yu Wei§, Rath Vannithamby¶∗§Graduate Institute of Communication Engineering, National Taiwan University, Taiwan

†‡§Department of Electrical Engineering, National Taiwan University, Taiwan¶Intel Labs, United States§hywei@cc.ee.ntu.edu.tw

Abstract—To explore the energy efficient Machine-Type Com-munication (MTC) for 5G cellular systems, we developed asimulation platform to investigate the complete uplink proceduresof energy-harvesting MTC devices in LTE-A (Long Term Evolu-tion Advanced) system. Though the 3rd Generation Partnership(3GPP) has worked on LTE-A cellular standards in supportof MTC transmission, the standards have not taken energy-harvesting MTC devices into their scope. Energy-harvestingtechnology can be the candidate to support the MTC featuresby allowing the devices to harvest ambient energy and supporttheir own power usage without manual upgrade. The proposedEnergy-Aware LTE-A scheme, serving as power control andadmission control, prevents the devices from building the networkconnections greedily but ending up with energy shortage andpacket loss. This scheme not only can reduce the contentionlevel of control channels but also can decrease energy wastage onnetwork entry procedures, thereby improving the overall energyefficiency.

I. INTRODUCTION

Machine-Type Communication is an emerging technologyfor the next generation communication, which allows a hugenumber of autonomous devices to communicate with eachother or with a central controller via wired or wireless con-nections. MTC devices are usually low power consuming.Potential MTC applications [1] include healthcare, tracking,sensor monitoring, vehicular telematics, smart grid, etc. Oncethe devices are deployed, they operate automatically for a longperiod of time without human intervention. In view of this,energy harvesting technology can be a promising techniquefor those devices to function permanently without batteryreplacement.

Energy harvesting technology not only can meet the de-mands of MTC applications but also can make devices cost-effective and energy-efficient. Devices with energy-harvestingmodules harvest energy from external sources, such as solarenergy, mechanical vibration, heat or wind [2] [3]. Energyharvesting technology then converts the harvested energy toelectricity for devices’ power usage. Energy harvesting tech-nology has shown great advance to power wireless devices inindustries [4] and structural health monitoring [5], which aredifficult to access after deployment.

LTE-A, which is a foreseeable widely applied next genera-tion telecommunications standard led by 3GPP, also focuses onsupporting the non-negligible trend of Machine-Type Commu-nication. According to [6], MTC with diverse applications has

explosive growth in the number of devices and the numberof connections. Thus, it may cause an essential problem inradio access network [7], i.e., random access congestion. LTE-A standard began to look into overload problems in the randomaccess network [7] [8]. However, LTE-A standard has not takeninto account of energy-efficient protocol design for energy-harvesting MTC devices.

This work focuses on developing an LTE-A simulationplatform with a large number of energy-harvesting MTCdevices. To successfully transmit packets, these devices in theidle mode have to acquire uplink radio resources via the RACHprocedure. After successfully going through the RACH pro-cedure and NAS (non-access stratum) signalling connection,the devices are granted for the dedicated data channels touplink packets. We study the power consumption behaviour ofenergy-harvesting MTC devices in each state of LTE-A system.Considering the necessary signalling information exchange inthe control channel in order to gain the uplink radio resource,we propose Energy-Aware LTE-A scheme to prevent energy-harvesting MTC devices from accessing the control channelsarbitrarily.

II. RELATED WORK

Random access congestion occurs when numerous MTCdevices contend for random access resources. In the legacycellular network such as GSM, UMTS, or LTE, a UE (userequipment) sends a uniformly selected preamble sequenceon the random access channel to inform the base station ofits connection request to uplink data. If numerous devicesrequest for the network connection at the same time, theymay transmit preambles and cause preamble collision on allavailable random access channels. Since the base station isunlikely to successfully decode a collided preamble, all RACHresources are blocked by those collided MTC devices and thusthe QoS of human-to-human communication will be affected.Previous works have shown that without proper admissioncontrol or adaptive random access optimization, the collisionmay be so severe that almost all resources are wasted andno UEs can successfully establish the connection [9] [10]. Toinvestigate RACH contention situation resulting from MTCdevices, 3GPP has specified [8] to provide protocol-levelsimulation and evaluation. However, few literatures providethe performance evaluation for LTE-Advanced cellular systemwith energy-harvesting MTC devices.

Design challenges of wireless sensor network with energy-harvesting devices are indicated in [11], including topologycontrol, Medium Access Control (MAC), reliable data de-livery, etc. Most researches focus on the design of MACprotocols for energy-harvesting devices in wireless local areanetworks instead of cellular networks. In [12], the authorsconsidered a linear topology of perceptually powered datasinks and energy-harvesting sensors. They evaluated the impactof transmit power control on the wireless sensor networkfor railway track monitoring. An optimal energy managementpolicy for wireless sensor network with solar-powered devicesis proposed in [13] and analysed by game theory approach.DeepSleep mechanism, proposed in [14] aimed at improvingthe energy efficiency for IEEE 802.11 power saving modewith energy-harvesting devices. DeepSleep mechanism forcesenergy-harvesting devices to sleep longer so as to reduce thepower consumption on idle listening and overhearing, whileguarantees those devices with higher transmission priority afterthey wake up. [15] studies different MAC protocols includingCSMA and polling techniques for wireless sensor networkwith energy-harvesting sensors. The proposed probabilisticpolling protocol considers the unpredictability of harvestedenergy and achieve better performance in terms of throughput,fairness and inter-arrival times. [16] shows the cooperativedata link automatic repeat request (ARQ) protocols can beapplied to improve the throughput of wireless sensor networkdeployed with energy-harvesting sensors. In the cooperativeARQ protocols, relaying packets can be viewed as a concept ofborrowing energy from one another and balancing the sensors’energy consumption to match their own battery recharge rate.

To study the power efficiency, [14] classifies the radiointerface states and uses the corresponding power consumptionvalues to analyse the performance of IEEE 802.11 PSM withenergy-harvesting devices. [17] studies the energy efficiencyof WiFi, 3G and LTE network with the measured powerconsumption values. The authors designed a tool, 4GTest forAndroid, and collected the power consumption values repliedby the users. However, the measured power consumptionvalues vary with the data rate and cannot show the differencebetween radio interface states. In our work, we evaluate thepower consumption of energy-harvesting MTC devices basedon LTE radio interface states in each signalling exchange stepso as to acquire the better understanding of the relationshipbetween the energy consumption on communications and theharvested energy.

III. SYSTEM DESCRIPTION

We developed an MATLAB platform with the LTE-Advanced eNB (evolved NodeB) and thousands of energy-harvesting MTC devices described in [18]. Those devices actas environmental monitors, collect information and uplink toa central controller, which is the eNB. The data traffic ofMTC applications such as healthcare monitoring, tracking andtracing is mainly uplink to a data sink, so we study thecommunication behaviour and power consumption of thosedevices for uplink data only. Those energy-harvesting devices,once deployed, harvest the ambient energy and convert themto electricity for power usage without human intervention.

We further assume the devices have already selected thedesired cell after being powered up. The core network has

already anchored the devices in MME (Mobility ManagementEntity). So, the investigated system focuses on what proceduresthe devices should perform when the packets arrive in buffer.The procedures of uplink data in the MATLAB platforminclude the idle mode, network entry and the connected mode,shown in Fig. 1. After cell selection, the devices enter the idlemode, in which the radio interfaces can be turned off for powersaving. No signalling exchanges between the devices and theeNB. No uplink resources are reserved for the devices. Whenpackets generated from the application layer arrive in buffer,the devices then turn on their radio interfaces to request uplinkradio resources from the eNB.

Idle ModeIdle Mode Network EntryNetwork Entry Connected ModeConnected Mode

No data in buffer when the inactivity timer expires.

EHD: Energy-harvesting MTC Device

EHD has data in buffer to uplink. EHD gets the UL resource successfully.

Fig. 1: State Diagram for Energy-harvesting MTC Devices

Before data transmission, the devices should first buildthe network connection via network entry procedures, definedas the RACH procedure and NAS signalling exchange. Tocomplete the RACH procedure, the device should go throughfour-step message exchange with the eNB described in [18].First, the device transmits the RACH preamble, selected uni-formly from a pool of preamble sequences, R, to the eNB.RACH preambles with different sequences can be detected bythe eNB simultaneously due to their orthogonality property.However, when more than one device send RACH preambleswith the same preamble sequence, those preambles collide witheach other and thus the eNB cannot reply the random accessresponse to the devices. After sending the RACH preamble, thedevice will set a timer, i.e., random access response (RAR)window, to wait for the expected random access responsefrom the eNB. Upon receiving the random access responsewithin RAR window, the devices send an RRC (radio resourcecontrol) connection request and the time alignment (TA) infor-mation together in the dedicated uplink resource (UL grant)specified in the random access response. After sending theRRC connection request, the device activates a contentionresolution timer, known as mac-ContentionResoulutionTimeror Msg4 timer. Finally, if the device receives Msg4 from theeNB within Msg4 timer, the RACH procedure accomplishessuccessfully. That is, the energy-harvesting MTC device hascompleted the RACH procedure and gain the uplink radioresources from the eNB.

Any failures in the four-step message exchange cause theenergy-harvesting MTC devices to enter the backoff process.The failures can be resulted from no random access responsewithin RAR window or no Msg4 reception within Msg4 timer.In the backoff process, the device uniformly selects a period oftime from the window of [1,W ], to count down. The value ofW is specified by Backoff Indicator (BI), which is assignedby the eNB through attaching a subheader to the RAR onthe PDSCH (Physical Downlink Shared Channel). During thebackoff process, the power consumption of the device becomeslower, and the preamble contention level is reduced as well.

After counting down, the device restarts the RACH procedurewith preamble transmission as long as the number of trialsis less than the maximal number of preamble transmissiontrials. After the devices are granted with uplink data radioresources, they establish NAS signalling connections with thecore network and then enter the connected mode.

In the connected mode, the device with packets in buffercan perform uplink transmission via the dedicated uplink datachannel without collision. When the packets in buffer are alltransmitted, the device activates the inactivity timer. Wheneverthe packets arrive before the inactivity timer expires, the devicetransmits the incoming packets immediately, and then restartsthe inactivity timer again once all packets are transmitted. Ifthe inactivity timer expires, the devices release the uplink dataradio resources within the disconnect timer. Then, they enterthe idle mode, where the radio interfaces are turned off forpower saving.

IV. EVALUATION METHODOLOGY

A. Energy Harvesting Model and Traffic Model

We applied Bernoulli process to model the harvested en-ergy [19] [20] and data traffic of an energy-harvesting MTCdevice. [21] has shown that solar radiation can be modelled asfirst-order two-state Markovian model and Bernoulli process.A sequence of independently identically distributed Bernoullitrials {X1, X2, X3, ..., Xi} forms Bernoulli process, wherei represents the time slot index. We model the amount ofharvested energy in each time slot and the amount of packetarrival as Bernoulli trials. The independent Bernoulli trialsimply Bernoulli process to be memoryless. In each time slot,the device can harvest Eh energy with probability pe, and noenergy with probability 1−pe. The mean of energy harvestingmodel is 49.19 mW. In each time slot, one packet comes tothe data buffer of the device with probability pt, and no packetarrives with probability 1 − pt. The mean of traffic model is1 packet per second, which is suitable for MTC applicationscenarios. Table I shows the parameters of the traffic modeland energy harvesting model in detail.

B. Naı̈ve LTE-A Scheme

We simulate LTE-A system with thousands of energy-harvesting MTC devices in the Matlab platform. The simula-tion platform involves the idle mode, RACH procedure, NASsignalling connections, data transmission and uplink radioresources release following [18]. In naı̈ve LTE-A scheme, theenergy-harvesting MTC devices perform the original proce-dures defined in LTE-A system to uplink data from the idlemode to the connected mode. However, LTE-A system isdesigned for battery-powered devices with most of the trafficas voice call. The traffic type is quite different from thatof the energy-harvesting MTC devices. If packets arrive inthe buffer of devices in the idle mode, the devices will gothrough the control plane signalling exchange to acquire theuplink radio resources so that they can finally transmit uplinkpackets. The power consumption to gain the uplink resource isinevitable but quite high compared to the amount of harvestedenergy. Therefore, we propose Energy-Aware LTE-A schemeto control the energy usage of the energy-harvesting MTCdevices, referred to Algorithm 1.

Algorithm 1 Energy-Aware LTE-A Scheme

1: if in idle mode and packets in queue then2: if E ≥ Eaware then3: turn on radio interface4: perform the RACH procedures for data transmission5: else6: the radio interface is off for power saving7: end if8: end if

C. Energy-Aware LTE-A Scheme

In Energy-Aware LTE-A scheme, the devices in the idlemode should be aware of their energy level to decide whetherto enter the network entry procedures right away. Instead ofoften encountering energy shortage and wasting energy onnetwork entry procedures, the devices should perform networkentry procedures only when they harvest enough energy tobreak the value of Eaware shown in Table I, which is designedby the balanced policy.

Lemma 1. By following the balanced policy, the value ofEaware enables the energy-harvesting MTC devices to harvestsufficient energy for transmssion of all queued packets whenthe connection establishment completes.

Proof: If the device is not able to transmit all its queuedpackets due to energy shortage, the packets will be queued untilthe next connection establishment completion. Without theappropriate energy control, the device will keep encounteringthe energy shortage. Thus, the queue length keeps increasing,causing packet loss. The goal of the balanced policy is bal-ancing the long-term averaged harvested energy and energyconsumption to decrease packet loss due to energy short-age. In the balanced policy, the appropriate energy thresholdEaware is found based on the average time period betweentwo connection establishments, Tcon. The expected numberof arrival packets during Tcon is Tcon × pt. The requiredenergy, Eout, to transmit all these packets is expressed asEout(Tcon) = ERACH + EPkt × Tcon × pt, where ERACH

is assumed to be the energy consumption on connectionestablishment without retransmission. EPkt is assumed to bethe energy consumption on transmitting one packet. From theenergy harvesting model, the harvested energy during Tcon canbe computed as Ein(Tcon) = pe × Eh × Tcon. Based on thebalanced policy, T ∗

con is found by Ein(T∗con) = Eout(T

∗con).

Hence, T ∗con = ERACH

pe×Eh−EPkt×ptand the corresponding energy

threshold of the balanced policy is Eaware = pe ×Eh ×T ∗con.

D. Simulation Setting

In the beginning of the simulation, all energy-harvestingMTC devices are in the idle mode, where the devices havealready completed cell selection. Upon packet arrival, the de-vices start to do network entry procedures for uplink transmis-sion resources. After successful RACH procedure, the devicesperform NAS signalling exchange with the MME within theNAS signalling duration, and then they can enter the connectedmode to uplink data. If the inactivity timer expires, the devicesdisconnect to the eNB within the disconnect timer and go backto the idle mode for power saving. The slot time is set to be 5

ms based on the setting of PRACH (Physical Random AccessChannel) configuration 6 in [8]. The total simulation durationis set to be 1000 sec. Table I shows the parameter settings.

TABLE I: Simulation Parameters

Parameter ValueSlot time 5 msPreamble sequences, R 54RAR window 5 msMsg4 timer 50 msBackoff Indicator, W 20 msPreamble transmission retry limit 9NAS signalling duration 60 msInactivity timer 50 msDisconnect timer 20 msSimulation duration 1000 secTraffic model Bernoulli process

pt = 0.005, mean = 1 packet/secEnergy model Bernoulli process

pe = 0.3935, Eh = 0.625 mJ, mean = 49.19 mWEaware 44 mJData buffer 20 packets

Based on the communication properties, the power con-sumption of the energy-harvesting devices can be classifiedinto four categories in Table II. We refer [14] for the powerconsumption parameter settings based on the radio interfacestatus, shown in Table II. Only in the idle mode, the radiointerface can be turned off for power saving. In other situations,the radio interface is always turned on for transmission, recep-tion or being inactive. For example, the power consumption ofdata transmission and preamble transmission is classified to beTransmission state. The power consumption of counting downthe inactivity timer is consider to be Inactivity state.

TABLE II: Power Consumption Parameters

State Definition ValueTransmission The radio interface is on and transmitting 550 mWReception The radio interface is on and receiving 250 mWInactivity The radio interface is on, no transmission and no reception 200 mWIdle mode The radio interface is off 40 mW

V. PERFORMANCE EVALUATION

Based on the evaluation methodology, the characteristicsof the control plane and data plane are comprehensivelyconsidered, including network entry procedures, applicationlayer packet delay, packet loss and energy efficiency.

First of all, we evaluate the preamble transmission suc-cess probability and the network entry success probabilityin the control channels. Because of the restricted preamblesequences, if the number of devices to do network entry in-creases, the preamble collision probability is supposed to rise,leading to lower preamble transmission success probability,shown in Fig. 2(c). In LTE-A system, successful preambletransmission, as a initial step, does not guarantee a deviceto acquire the dedicated data channel. The device should gothrough the complete network entry process and signallingexchange to build the network connection. Without appropriatepower control, successful preamble transmission may still endup with high probability of network entry failure, shown inFig. 2(d). The reason could be that the devices in the naı̈veLTE scheme encounter energy shortage in the network entry

process. Energy-Aware LTE-A scheme not only prevents theenergy-harvesting MTC devices from greedily entering thenetwork and wasting energy on the network process, but alsoserves as an admission control mechanism to increase thesuccessful preamble transmission probability and successfulnetwork entry probability by reducing the number of devicesto contend uplink radio resources.

The energy efficiency is also significantly improved byEnergy-Aware LTE scheme, shown in Fig. 2(e). Once thedevices gain the dedicated data channel in the connected mode,continuous data transmission is possible if the devices still havesufficient energy. Building network connection from the idlemode is like a bottleneck in the cellular system, which not onlyconsumes high power but also may introduce a large amountof delay to successfully transmit the first packet in buffer.However, if the network connection can be built successfully,the devices with enough harvested energy can transmit thebuffered packets continuously. That is, one of the availablemethods to improve energy efficiency is reducing the energyconsumption on the control plane and utilizing the dedicatedresources in the data plane.

In Fig. 2(a), the application layer packet delay of naı̈veLTE-A scheme is calculated by the few successfully transmit-ted packets shown in Fig. 2(e), thereby the variance is high.That is, once the devices are short of energy, naı̈ve LTE-Ascheme cannot allow them to harvest and save enough energyfor network entry requirement. These devices are backloggedin the idle mode. The application layer packet delay in Energy-Aware LTE-A scheme is roughly 5 minutes, which is usuallytolerant in the MTC application scenarios. This phenomenais caused by the balance between the traffic arrival rate andenergy harvesting rate. Fig. 2(b) shows that the applicationlayer loss is explicitly improved. Thus, Fig. 2(a) and Fig. 2(b)suggest that Energy-Aware LTE-A scheme sacrifices the toler-ant delay with improved packet loss and enhanced reliability.

The outage probability, defined as the time duration whendevices with packets in buffer are in the idle mode divided bythe total simulation time, indicates that the energy-harvestingMTC devices are backlogged in a low power consumptionstate on fully harvesting energy instead of communicationswith the networks. In Energy-Aware LTE-A scheme, the outageprobability is less shown in Fig. 2(f). It means that settingEaware to make devices spend time on saving and harvestingsufficient energy before communicating with the network willnot increase the outage probability.

To sum up, the network entry success probability, the en-ergy efficiency, the application layer packet loss and the outageprobability of Energy-Aware LTE-A scheme are superior tothat of naı̈ve LTE-A scheme. Harvesting and storing sufficientenergy in low power consumption mode before performingnetwork entry process is essential, especially in LTE-A sys-tem, which requires heavy control signalling exchange beforesuccessful data transmission.

VI. CONCLUSION

To gain insights for 5G communications, we investigatedthe comprehensive procedures in LTE-A system for the energy-harvesting MTC devices to upload data. We considered thepower consumption based on the radio interface states in

(d) Network Entry Success Probability (e) Energy Efficiency (f) Outage Probability

Fig. 2: Evaluation Results

the signalling exchange process. In LTE-A system with bothcontrol and data planes, saving and harvesting sufficient energybefore doing network entry is quite essential to guaranteeenough energy for data transmission. Considering both theproperty of energy harvesting technology and the high powerconsumption in the control channel, Energy-Aware LTE-Ascheme, as a power control and admission control mechanism,shows superior network performance and energy efficiency bywisely managing the power usage of the energy-harvestingMTC devices.

ACKNOWLEDGEMENT

This work was also supported by National Science Council,National Taiwan University and Intel Corporation under GrantsNSC102-2911-I-002-001 and NTU103R7501.

REFERENCES

[1] Geng Wu, S. Talwar, K. Johnsson, N. Himayat, and K.D. Johnson.M2M: From Mobile to Embedded Internet. IEEE Commun. Mag.,49(4):36–43, 2011.

[2] S. Chalasani and J.M. Conrad. A Survey of Energy Harvesting Sourcesfor Embedded Systems. In IEEE Southeastcon, pages 442–447, 2008.

[3] S. Sudevalayam and P. Kulkarni. Energy Harvesting Sensor Nodes:Survey and Implications. IEEE Communications Surveys & Tutorials,13(3):443–461, 2011.

[4] J. Nuffer and T. Bein. Applications of Piezoelectric Materials inTransportation Industry. In Global Symposium on Innovative Solutionsfor the Advancement of the Transport Industry, volume 4, 2006.

[5] G. Park, T. Rosing, M.D. Todd, C.R. Farrar, and W. Hodgkiss. EnergyHarvesting for Structural Health Monitoring Sensor Networks. Journalof Infrastructure Systems, 14(1):64–79, 2008.

[6] Cisco Visual Networking Index: Global Mobile Data Traffic ForecastUpdate, 2011-2016. http://www.cisco.com.

[7] 3GPP TS 22.368 v11.2.0. Service Requirements for Machine-TypeCommunications (MTC). Jun. 2011.

[8] 3GPP TR 37.868 v11.0.0. Study on RAN Improvements for Machine-Type Communications. In Release 11, Sep. 2011.

[9] S.Y. Lien, K.C. Chen, and Y. Lin. Toward Ubiquitous Massive Accessesin 3GPP Machine-to-Machine Communications. IEEE Commun. Mag.,49(4):66–74, 2011.

[10] M.Y. Cheng, G.Y. Lin, H.Y. Wei, and A.C. Hsu. Overload Controlfor Machine-Type-Communications in LTE-Advanced System. IEEECommun. Mag., 50(6):38, 2012.

[11] W.K.-G. Seah, Zhi Ang Eu, and H. Tan. Wireless Sensor NetworksPowered by Ambient Energy Harvesting (WSN-HEAP) - Survey andChallenges. In 1st International Conference on Wireless Commu-nication, Vehicular Technology, Information Theory and AerospaceElectronic Systems Technology, 2009., pages 1–5.

[12] H.P. Tan, P.W. Lee, W.K.G. Seah, and Z.A. Eu. Impact of PowerControl in Wireless Sensor Networks Powered by Ambient EnergyHarvesting (WSN-HEAP) for Railroad Health Monitoring. In IEEEInternational Conference on Advanced Information Networking andApplications Workshops, 2009., pages 804–809.

[13] D. Niyato, E. Hossain, M.M. Rashid, and V.K. Bhargava. Wireless Sen-sor Networks with Energy Harvesting Technologies: a Game-TheoreticApproach to Optimal Energy Management. IEEE Wireless Commun.,14(4):90–96, 2007.

[14] H.H. Lin, H.Y. Wei, and R. Vannithamby. DeepSleep: IEEE 802.11Enhancement for Energy-Harvesting Machine-to-Machine Communica-tions. In 2012 IEEE Global Communications Conference, pages 5231–5236.

[15] Z. A. Eu, H. P. Tan, and W. K. Seah. Design and Performance Analysisof MAC Schemes for Wireless Sensor Networks Powered by AmbientEnergy Harvesting. Ad Hoc Networks, 9(3):300–323, 2011.

[16] M. Tacca, P. Monti, and A. Fumagalli. Cooperative and Reliable ARQProtocols for Energy Harvesting Wireless Sensor Nodes. IEEE Trans.on Wireless Commun., 6(7):2519–2529, 2007.

[17] J. Huang, F. Qian, A.e Gerber, Z. M. Mao, S. Sen, and O. Spatscheck.A Close Examination of Performance and Power Characteristics of 4GLTE Networks. In Proceedings of the 10th international conference onMobile systems, applications, and services. ACM, 2012.

[18] 3GPP TS 36.321 v10.2.0. E-UTRA Medium Access Control (MAC)Protocol Specification. Jun. 2011.

[19] Jing Lei, Roy Yates, and Larry Greenstein. A Generic Model forOptimizing Single-Hop Transmission Policy of Replenishable Sensors.IEEE Trans. on Wireless Commun., 8(2):547 –551, 2009.

[20] Nicolo Michelusi, Kostas Stamatiou, and Michele Zorzi. On OptimalTransmission Policies for Energy Harvesting Devices. In InformationTheory and Applications Workshop (ITA), 2012. IEEE.

[21] A Maafi and A Adane. Analysis of the Performances of the First-OrderTwo-State Markov Model Using Solar Radiation Properties. Renewableenergy, 13(2):175–193, 1998.

0 1000 2000 3000 4000 500050

100

150

Device number

Del

ay (m

s)

Naive LTE−A Scheme

0 1000 2000 3000 4000 50000

5000

10000

Device number

Del

ay (m

s)

Energy−Aware LTE−A Scheme

Application Layer Packet Delay

(a) Packet Delay

0 1000 2000 3000 4000 50000.9997

0.9998

0.9999

1

Device number

prob

abilit

y

Naive LTE−A Scheme

0 1000 2000 3000 4000 50000

0.005

0.01

0.015

Device number

prob

abilit

y

Energy−Aware LTE−A Scheme

Packet Loss Probability

(b) Packet Loss

0 1000 2000 3000 4000 50000

0.2

0.4

0.6

0.8

1Preamble Transmission Success Probability

Device number

Pro

babi

lity

Naive LTE−A SchemeEnergy−Aware LTE−A Scheme

(c) Preamble Transmission Success Probability

0 1000 2000 3000 4000 50000

0.5

1

1.5x 10

−4

Device number

Prob

abilit

y

Naive LTE−A Scheme

0 1000 2000 3000 4000 50000.995

1

1.005

Device number

Prob

abilit

y

Energy−Aware LTE−A Scheme

Network Entry Success Probability

(d) Network Entry Success Probability

0 1000 2000 3000 4000 50000

2

4

6x 10

−3

Device number

pkt/J

oule

Naive LTE−A Scheme

0 1000 2000 3000 4000 500019.5

20

20.5

Device numberpk

t/Jou

le

Energy−Aware LTE−A Scheme

Number of Successful Packet Transmission per Joule

(e) Energy Efficiency

0 1000 2000 3000 4000 50000.8

0.85

0.9

0.95

1Outage Probability (Idle mode with queued pkts)

Device number

Rat

io

Naive LTE−A SchemeEnergy−Aware LTE−A Scheme

(f) Outage Probability