DRONEE: Dual-radio opportunistic networking for energy...

Computer Communications xxx (2014) xxx–xxx

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Computer Communications

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DRONEE: Dual-radio opportunistic networking for energy efficiency

http://dx.doi.org/10.1016/j.comcom.2014.02.0140140-3664/� 2014 Elsevier B.V. All rights reserved.

⇑ Corresponding author. Address: Avenida del Mar Mediterraneo, 22, Leganes,Madrid 28918, Spain. Tel.: +34 91 481 6958; fax: +34 91 481 6965.

E-mail addresses: [email protected] (A. Asadi), [email protected] (V. Mancuso).

Please cite this article in press as: A. Asadi, V. Mancuso, DRONEE: Dual-radio opportunistic networking for energy efficiency, Comput. Commun.http://dx.doi.org/10.1016/j.comcom.2014.02.014

Arash Asadi ⇑, Vincenzo MancusoIMDEA Networks institute and University Carlos III of Madrid, Spain

a r t i c l e i n f o

Article history:Available online xxxx

Keywords:LTEWiFi DirectCooperative communicationsHybrid networksOpportunistic scheduling

a b s t r a c t

Reducing the power consumption of smartphones is becoming more and more important as smartphonesbecome an indispensable component of our daily activities. In this work, we propose a novel scheme, socalled DRONEE, that dramatically ameliorates energy efficiency for uplink transmissions, while achievingnear-optimal throughput and high fairness levels in cellular networks. Our proposal consists in a noveltwo-tier uplink forwarding scheme in which users cooperate by forming clusters of dual-radio mobilesfor hybrid wireless networks. The impact of our proposal is threefold: ðiÞ energy efficiency is boosted byallowing mobiles to relay the cellular traffic through intra-cluster ad hoc communications, which leadsto reduction of power-hungry cellular transmissions; ðiiÞ cellular capacity is augmented by schedulinguplink transmissions from mobiles with the best channel; ðiiiÞ almost perfect fairness is achieved by allow-ing users to share the cellular resources within their cluster. We corroborate the practical relevance of ourproposal by providing a first-order discussion on the implementability of DRONEE using LTE and WiFiDirect.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

In the past decade, the advent of smartphones and wirelessbroadband technologies has changed our daily habits and hobbies.Nowadays, people use smartphones for entertainment (e.g., chat-ting, reading news, social networking, and gaming) and work(e.g., email, stock market, etc.). Such a wide range of usage hasstressed mobile operator’s networks and smartphone’s battery life.New smartphones come with high power processors and supportsimultaneous WiFi and cellular data connectivity, which drainsbatteries fast. Moreover, 3/4G cellular data technologies (i.e., LTEand LTE-A) are more power consuming than the previous genera-tions. Since smartphone’s battery life is limited, solutions leadingto high energy efficiency are of utmost importance.

Prior works on energy efficiency in cellular data networksmostly proposed techniques such as traffic batching [1,2] and traf-fic pattern learning [3]. These techniques are meant to increase theperiods during which mobiles can switch off their wireless inter-faces. There are also proposals to delay data transmissions untilthere is an opportunity to offload the data to a WiFi network[4,5]. Existing proposals suffer from two drawbacks: ðiÞ the extradelay introduced in the system, which makes them impractical

for real-time and non-elastic services; and ðiiÞ the complexity im-posed to estimate future traffic patterns or to aggregate traffic fromdifferent applications. Moreover, existing proposals do not lever-age the possibility to use more than one wireless interface at a timeto improve energy efficiency. Some proposals do make use of multi-radio to improve performance, but they are not fully opportunisticand rather focus on coordinating multiple parallel connections forload balancing, P2P applications, multiplayer gaming, and multi-cast transmissions [6].

In contrast, in this article, we propose DRONEE (Dual-RadioOpportunistic Networking for Energy Efficiency), a novel schemewhich fully leverages real-time cooperation of dual-radio deviceswith opportunistic scheduling for hybrid networks (i.e., using LTEand WiFi).

Using DRONEE, energy efficiency benefits from the opportunityto use mobiles to relay cellular traffic by means of WiFi. Specifi-cally, LTE is used only for the data transmission of mobiles thathave the top channel quality. Those mobiles act as relay for theremaining nodes organized in clusters, as depicted in Fig. 1. Differ-ently from relay nodes, the other mobiles in the cellular networkonly use WiFi, which is much less energy consuming than LTE[7], to move their packets to and from relay nodes. Using an oppor-tunistic scheduling strategy to select relay nodes results in en-hanced energy efficiency while simplifying the scheduling tasksof the base station. Interestingly, DRONEE improves networkthroughput and fairness as well, without incurring the high com-plexity required by other technologies such as beamforming and

(2014),

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Fig. 1. An example of a cellular network with clusters of dual-radio mobiles. Users 1 and 2 transmit their packets (which are colored in orange and black, respectively) overthe WiFi network to the cluster head (i.e., User 3). Next, the cluster head forwards the packets to the base station. (For interpretation of the references to color in this figurelegend, the reader is referred to the web version of this article.)

2 A. Asadi, V. Mancuso / Computer Communications xxx (2014) xxx–xxx

MIMO [8]. Overall, DRONEE allows cellular users to enjoy seamlessconnectivity while spending more time on the WiFi interface, whichis less power consuming than LTE, and switch off their LTE interfacefor long periods during which the LTE user channel’s quality is notthe strongest.

This manuscript focuses on uplink transmission issues only,since the uplink represents the current technology bottleneck fortransmission capacity, and transmission consumes more batteryrather than reception. However, most of our proposal could be eas-ily extended to downlink as well. The main contributions of thisarticle are as follows: ðiÞ in DRONEE, an architecture is proposedwhich exploits smartphone’s multi-radio interfaces (i.e., LTE andWiFi) to seamlessly improve the energy efficiency of real-time cellu-lar uplink operations; ðiiÞ we propose and analyze a novel cluster-ing scheme for heterogeneous users, which is part of DRONEE, andwhich adopts opportunistic and dynamic cluster head selection;ðiiiÞ we provide the first analytic model for power consumptionof dual-radio mobiles in LTE-WiFi hybrid networks accountingfor power saving features of LTE and WiFi; ðivÞ via extensivenumerical simulation, we provide a performance evaluation ofDRONEE, in terms of energy efficiency, throughput, and fairnessin single-cell and multiple cell scenarios; ðvÞwe discuss the imple-mentation requirement and practicality of DRONEE in a real worldsystem using LTE and WiFi Direct specifications.

The remainder of this manuscript is organized as follows.Section 2 discusses the related work. The system model is pre-sented in Section 3. Section 4 numerically evaluates the clusteringgain in single-cell and multi-cell scenarios. Section 5 provides afirst-order discussion on the implementation feasibility of our pro-posal. Section 6 summarizes our findings and concludes the article.

2. Related work

Although energy efficiency and wireless relay schemes havebeen proposed and extensively studied, there is little or no litera-ture on jointly leveraging multi-radio, power saving capabilitiesof smartphones, and opportunistic scheduling to improve energyefficiency of uplink communications in cellular networks.

Energy efficiency. Cellular standards allow user equipment toswitch off the transmission circuitry temporarily in order to savepower. The majority of the proposals focus on leveraging thispower saving option to reduce the active time of wireless inter-faces. For instance, in [9], the authors analytically model powerconsumption of cellular mobile users and base stations, and illus-trate the significance of continuous connectivity in power savingof mobile users. The authors also show how to optimize the powersaving parameters to reduce the power consumption of mobile de-vices. However, [9] does not propose novel transmission schemesand does not consider capacity issues. Realistic models for the

Please cite this article in press as: A. Asadi, V. Mancuso, DRONEE: Dual-radio ohttp://dx.doi.org/10.1016/j.comcom.2014.02.014

power consumption of mobiles have been proposed for LTE andWiFi in [10,11], respectively. The proposed models are desirablefor two virtues: ðiÞ they include the baseline power required tokeep the interface up and running; ðiiÞ they account for the vari-ability of power consumption with transmission rate, while differ-entiating the cost of transmission from reception. However, thosemodels do not account for power saving operations. In our pro-posal, we build on top of such models in order to provide a noveland more accurate model for power consumptions of wireless de-vices. Moreover, LTE and WiFi power consumption models arecombined into a single generalized model for dual-radio devices.

The authors of [1] studied energy consumption in 3G and GSMnetworks. Their measurements reveal that significant amount ofenergy is wasted when the wireless interface is still active butthere is no data for transmission (i.e., tail energy). They proposean application layer protocol, namely TailEnder, that reduces theenergy wasted in tails by delaying delay-tolerant data. Using a sim-ilar approach, Liu et al. [2] propose TailTheft which attempts toaggregate traffic of different applications to reduce the amount ofthe tail energy. Deng et al. [3] use a different approach by predict-ing traffic patterns to decide about active or idle state transitions.Differently from [1–3], our proposal only requires the use of stan-dard defined power saving operations, while it does not requiretraffic coalescing, thus not incurring an excessive packet delay.The authors of [4,5] address the fact that WiFi transmissions re-quire less energy than 3G/LTE transmissions. Hence, they proposeto delay the cellular traffic until a WiFi access point is availablefor offloading. Their approach induces significant delays and is onlyapplicable to highly delay-tolerant applications. In contrast, ourproposal does not need WiFi access points and induces negligibleper-packet delay.

Wireless relay. The authors of [12,13] propose to form clustersamong mobile users with single antenna to emulate a MIMO de-vice. Such an architecture requires precise synchronization be-tween cluster members. Moreover, all cluster members have tomaintain active and power expensive connections to the base sta-tion. Furthermore, Dohler et al. [13] proposed to use a secondarywireless interface (e.g., Bluetooth or WiFi) for coordinating MIMOoperations. Yu et al. [14] propose Device-to-Device (D2D) commu-nications in cellular networks for local traffic handling. D2D trans-missions are meant to handle communications among two mobiledevices, however users do not help each other to relay traffic to thebase station. Also, all transmissions occur over the same interfaceas cellular communications, and D2D resources are allocated bythe base station.

Our extensive literature review indicates that none of the priorworks categorized under packet forwarding, relaying, cooperativenetworks, and hierarchal clustering have the characteristics ofour proposal, which are: ðiÞ parallel use of multiple wireless

pportunistic networking for energy efficiency, Comput. Commun. (2014),


A. Asadi, V. Mancuso / Computer Communications xxx (2014) xxx–xxx 3

interfaces (i.e., LTE and WiFi); ðiiÞ clustering of wireless cellular de-vices; ðiiiÞ opportunistic cluster head selection; ðivÞ seamless con-nectivity with real-time cluster operations; and ðvÞ increase powersaving opportunities, and thus energy efficiency, while achievinghigh cellular throughput and fairness.

3. DRONEE system model

In order to boost energy efficiency and to make channel utiliza-tion more efficient in LTE cellular networks, we propose DRONEE,using a novel paradigm to leverage clusters of mobile users. Inour clusters, cluster heads relay the cellular traffic generated withinthe clusters towards the base station (eNB in the LTE terminology),as shown in Fig. 1.

With DRONEE, LTE mobile users form clusters by exploitingtheir secondary wireless interface, i.e., WiFi. Clusters form whenthe WiFi connectivity between cluster members is good, i.e., WiFidata rates are higher than the load generated in the LTE uplinkby the cluster members. Among cluster members, only one nodeis allowed to transmit to the eNB, i.e., the cluster head. Differentlyfrom existing clustering schemes for sensor and vehicular net-works [15–17], we propose to select the cluster head as the clustermember with the highest cellular channel quality at a given sched-uling epoch. Therefore, a notable resource utilization incrementstems from opportunistically and timely selecting cluster heads,based on the quality of their uplink LTE channels. Note that, withour proposal, scheduling of users is replaced with the selection ofcluster heads. In particular, two possible cluster head selectionand scheduling schemes will be discussed and analyzed in theremainder of this section, while the set of network proceduresneeded to implement DRONEE will be discussed in Section 5.

In what follows, we first present our model assumptions, andthen it is shown how to compute the throughput of a cellular useri, namely E½Ti�, and its power consumption, denoted by W ðiÞ

tot , whenDRONEE is adopted. In the numerical evaluation presented later inSection 4, the energy efficiency g of a user i will be computed as thequantity E½Ti�=W ðiÞ

tot .

3.1. Model assumptions

We model uplink transmissions in an LTE-like [18] networkoperated by a single operator, with N cellular users generating up-link traffic. In our work, it is assumed that uplink resources Stot arefixed for each scheduling epoch (frame), and users always havepackets to transmit so that the network performance is evaluatedunder fully backlogged traffic conditions. In our analysis, the wholeuplink frame resources Stot are allocated to data transmission and ascheduled frame is allocated to one LTE transmitter only. The sched-uled LTE transmitter is a cluster head, which is the mobile experi-encing the best channel quality in its cluster. The cluster head isselected to connect to the base station on a per-frame basis. There-fore, the adopted scheduling strategy is channel-opportunistic. It isassumed that the base station is aware of cluster formation, so thatit can allocate resources to cluster heads only. We will introduce thedetails of two possible opportunistic cluster head selection andscheduling schemes in Section 3.2.

The uplink LTE channel between mobile user i and the basestation is characterized by stationary Rayleigh fading. Assumingthat the SNR of user i is a random variable Ci with mean valueci, the CDF of the SNR has the following expressions:

FiðzÞ ¼ 1� e�zci ; z P 0: ð1Þ

We assume that user channels are independently distributed butnot identically, and the channel state information is available atthe base station. Transmissions occur at different rates according


to M available modulation and coding schemes (MCSs), selectedas a function of the instantaneous SNR, i.e., for user i:

MCSi ¼ k() Ci 2 thk; thkþ1½ Þ; k ¼ 1 . . . M; ð2Þ

th1 ¼ 0; thp < thq () p < q; thMþ1 ¼ 1;

where the SNR thresholds thk are expressed in linear units.Therefore, the probability that a scheduled user i transmits a

frame encoded with the kth MCS is:

pðiÞk ¼Z thkþ1

thk

dFiðzÞ ¼ ethkci � e

thkþ1ci : ð3Þ

We denote bk by the number of data bits transferred in oneOFDMA symbol using the kth MCS, as reported in Table 1. Indexk ¼ 1 corresponds to SNR values below the minimum receiver sen-sitivity, for which no transmission is possible at all. The tableshows the list of possible MCSs with their corresponding SNRthresholds for an LTE-like network [19]. The ImplementationMargin (IM) in Table 1 is a value that represents the effects ofnon-ideal receiver. For the sake of tractability, here we assume thatmobile users belong to one of three predefined user channel qual-ity classes (referred to as user qualities). These user qualities arecharacterized by different mean SNR values, and correspond topoor, average, and good users. The designated mean SNR valuesfor different classes are chosen in a manner that the mean achiev-able rates for poor, average, and good users are 20%;50%, and 80%

of the maximum transmission rate achievable in the system,respectively. Considering the thresholds and MCS values reportedin Table 1, the designated mean SNR values are 7 dB, 16 dB and23 dB, respectively for poor, average, and good users. Note thatusing non-homogeneous channel qualities allows us to evaluatethe long-term system fairness under different (opportunistic)scheduling mechanisms. For simplicity, we report the notationused throughout the manuscript in Table 2.

For the proposed system, the analysis for throughput and powerconsumption is presented in what follows.

3.2. DRONEE throughput modeling

Here we detail two opportunistic cluster head selection andscheduling schemes, and model the throughput attained by mobileusers. In particular, we analyze two simple schemes, namely DRO-NEE-W and DRONEE-M. We focus on simple mechanisms since thecomplexity of throughput-fair opportunistic operations representsthe most serious obstacle towards the practical adoption of oppor-tunistic mechanisms in real systems [20]. Indeed, without usercooperation, pure opportunistic schedulers can be simple to imple-ment and run, but they would behave unfairly [21].

It is shown that even very simple opportunistic mechanisms canachieve high energy efficiency when using DRONEE, without sacrific-ing throughput fairness. Moreover, our DRONEE proposal reducesthe complexity of scheduling in general, since only cluster headscan be scheduled by the base station.

Since the base station only communicates with the cluster head,clusters are treated and scheduled as regular users characterized bythe aggregate traffic demand of cluster members. From a modelingperspective, a cluster can be considered as a user whose channelstate is the highest of the channel states among cluster members.In DRONEE, we consider the case in which the base station sched-ules Nc clusters instead of N normal users. This means that the basestation decides which clusters have to be served, and then transmis-sions will be managed by the current cluster head.

Defining Xn as the SNR of cluster n, we have:

Xn ¼ maxfCj; j 2 Cng; n 2 f1 . . . Ncg: ð4Þ



Table 1Modulation and coding schemes and their thresholds.

Modulation Coding Rate SNR (dB) IM (dB) SNR + IM (dB) SNR bk(Bits per symbol)

- – �1 - �1 b1 ¼ 0

QPSK 1/8 �5.1 2.5 �2.6 0.54 b2 ¼ 0:251/5 �2.9 �0.4 0.91 b3 ¼ 0:41/4 �1.7 0.8 1.2 b4 ¼ 0:51/3 �1 1.5 1.41 b5 ¼ 0:671/2 2 4.5 2.81 b6 ¼ 12/3 4.3 6.8 4.78 b7 ¼ 1:33/4 5.5 8.0 6.3 b8 ¼ 1:54/5 6.2 8.7 7.41 b9 ¼ 1:6

16QAM 1/2 7.9 3 10.9 12.30 b10 ¼ 22/3 11.3 14.3 26.91 b11 ¼ 2:663/4 12.2 15.2 33.13 b12 ¼ 34/5 12.8 15.8 38.01 b13 ¼ 3:2

64QAM 2/3 15.3 4 19.3 85.11 b14 ¼ 43/4 17.5 21.5 141.25 b15 ¼ 4:54/5 18.6 22.6 181.97 b16 ¼ 4:8

Table 2Notation used within this manuscript.

Notation Description

N Total number of users.M Number of MCSs.Nc Number of clusters.Cn Set of users in the nth cluster.Nn Number of members of cluster Cn .Stot Total uplink resources (symbols/frame).ti

kResources allocated to user i when MCSi ¼ k.

ci Mean SNR of user i.thk Minimum SNR for transmitting with the kth MCS.bk Data bits per symbol in the kth MCS.pk Probability to transmit with the kth MCS.

Pih

Probability of user i being cluster head.

Pa Probability of interface being active.Wð:Þ Power consumption.bð:Þ Baseline power of the interface in active mode.

bidleð:Þ

Baseline power of the interface in idle (power saving) mode.

að:Þ Power consumption per Mbps over LTE.fð:Þ Power consumption per Mbps over WiFi.sð:Þ Fraction of time spent on transmission over WiFi.jð:Þ Power consumed due to packet processing over WiFi.kð:Þ Packet rate.Lp Average packet size.

Ri;ltetx

Average data rate of user i over LTE.

Ri;wifitx

Average transmission data rate of user i over WiFi.

Ri;wifirx

Average reception data rate of user i over WiFi.

Rwifi Achievable rate of WiFi connection.Ti Throughput of user i.TCn Throughput of cluster n.di Fraction of total cluster throughput belonging to user i.FXð:Þ CDF of random variable X.


Considering that the random variables Cj are all independent, theCDF of Xn can be computed as follows:

FXnðzÞ ¼Yj2Cn

FjðzÞ ¼Yj2Cn

1� e� z

cj

� �; z P 0: ð5Þ

The adopted MCS scheme, for each transmission, only depends onthe instantaneous SNR of the best channel in the scheduled cluster,i.e., it only depends on Xn at the scheduling epoch. The probabilityto transmit with kth MCS is given by:

pðCnÞk ¼

Z thkþ1

thk

dFXn ðzÞ: ð6Þ


3.2.1. DRONEE-WThe first proposed scheme schedules clusters in a Weighted

Round Robin (WRR) fashion, and selects the cluster head as theuser with the strongest channel quality in its cluster. We name thisscheme DRONEE-W. The weights wn associated to each cluster Cn

can be assigned in a variety of methods. For simplicity, we willassociate weights to clusters based on their size. Thus, denotingNn as the number of members of cluster Cn, we can compute theweights as wn ¼ Nn=N.

The per-cluster scheduling probability is wn; n 2 f1 . . . Ncg,while the transmission rate depends on the channel seen by thecluster head, as given by the probability mass function describedin (6). Since resources Stot are allocated in WRR style, the averagecluster throughput and the average per-user throughput are givenby the following Propositions 1 and 2, whose proofs are immediate,so we omit them.

Proposition 1. Under DRONEE-W, the average throughput receivedby cluster Cn is

E½TCn � ¼ wn Stot

XM

k¼1

pðCnÞk bk; n 2 f1 . . . Ncg: ð7Þ

Proposition 2. Under DRONEE-W, the average throughput receivedby user i 2 Cn can be expressed as

E½Ti� ¼Stot

N

XM

k¼1

pðCnÞk bk; i 2 Cn; n 2 f1 . . . Ncg: ð8Þ

The probability that a user i is scheduled is given in the follow-ing proposition.

Proposition 3. Under DRONEE-W, a user i 2 Cn is scheduled withprobability

PðiÞh ¼ wn

XM

k¼1

pðCnÞk

Z 1

01� FiðzjMCSi ¼ kÞ½ �dFYi

ðzÞ; ð9Þ

where Yi ¼maxj2CnnfigfCjg; i 2 Cn.

The proof of Proposition 3 is reported in Appendix A. Note that,under Rayleigh fading assumptions, the conditional probabilityFiðzjMCSi ¼ kÞ is simply given by the following formula:

FiðzjMCSi ¼ kÞ ¼ Fi min z; thkþ1ð Þð Þ � Fi thkð ÞpðiÞk

; z P thk: ð10Þ




3.2.2. DRONEE-MWith this second scheme, in each frame, our system selects the

cluster which has the user with the best channel quality and as-signs all resources Stot to it. That user is selected as cluster headfor its cluster. Therefore, DRONEE-M performs the cluster headselection and scheduling according to a pure MaxRate approach[22].

The average cluster throughput and the average per-userthroughput achieved under DRONEE-M are given by the followingPropositions 4 and 5.

Proposition 4. Under DRONEE-M, the average throughput receivedby cluster Cn is

E½TCn � ¼ Stot

XM

k¼1

pðCnÞk bk

h

�R1

0 1� FXn ðzjMCSCn ¼ kÞ½ �dFYn ðzÞ�;

ð11Þ

where n 2 f1 . . . Ncg;Xn is defined in (4), and Yn ¼maxjRCnfCjg.

The proof of Proposition 4 is reported in Appendix A.

Proposition 5. Under DRONEE-M, the average throughput receivedby user i 2 Cn is

E½Ti� ¼ StotNn

XM

k¼1

pðCnÞk bk

h

�R1

0 1� FXnðzjMCSCn ¼ kÞ½ �dFYn ðzÞ�;

ð12Þ

where n 2 f1 . . . Ncg;Xn is defined in (4), and Yn ¼maxjRCnfCjg.

The proof of Proposition 5 is like the proof of Proposition 4.The probability that a user i is scheduled is given in the follow-

ing proposition, which is proven in Appendix A.

Proposition 6. Under DRONEE-M, a user i is scheduled withprobability

PðiÞh ¼XM

k¼1

pðiÞk

Z 1


ðzÞ; ð13Þ

where Yi ¼maxj – ifCjg and FiðzjMCSi ¼ kÞ is given by Eq. (10).

3.3. DRONEE power consumption

We derive the power consumption of mobiles in DRONEE fromthe empirical power models proposed for LTE and WiFi in [10,11].Differently from the existing models, our proposed power modeldistinguishes between the power consumption in active and idleperiods, the transmission power, and the reception power. Bydoing so, our model accounts for the power saving features thatLTE and WiFi technologies incorporate, which allows users toswitch off part of the circuitry for most of idle-interval duration.

In what follows, we distinguish between average throughputE½T� and data rate R of a user. The former is the amount of user-application local data received by a user directly via LTE or via WiFirelay, and it is computed via (8) and (12). The latter is the amountof data handled by a user over a wireless interface, and it includesnon-local traffic to be relayed.

3.3.1. Power saving in LTE and WiFiIn LTE, idle periods are handled by discontinuous reception DRX

and discontinuous transmission DTX mechanisms [23]. In WiFi,users can turn off the wireless interface during idle periods andonly turn on the interface to receive beacons [24]. In both LTEand WiFi, interfaces in power saving mode periodically wake upto transmit/receive control information even if there is no data


traffic to handle. However, it has been shown that the periodicwake-up of power saving mechanisms in LTE and WiFi impactsat most 5% of the idle time [10]. Therefore, for simplicity, weignore the periodic wake-up operation. We assume that wirelessinterfaces can instantaneously switch from active to power savingmode as soon as there are no packets to be handled by that inter-face. Interfaces switch back to active mode as soon as a packet ispresent in the transmission queue. Therefore, in our model, inter-faces stay in power saving mode during the entire idle interval.In light of this assumption, we interchangeably use the expressionspower saving mode and idle mode.

3.3.2. LTE consumptionAccording to [10], the LTE power consumption results from the

sum of a baseline power and a term which is proportional to thetransmission rate of the device. Here, we extend the model pro-vided in [10] by considering the probability that user i is in activemode over the LTE interface, which is equivalent to the probabilityPðiÞh of being the cluster head given in Eqs. (9) and (13). The powerspent by user i over LTE can be computed as follows:

W ðiÞlte ¼ PðiÞh blte þ 1� PðiÞh

� �bidle

lte þ atx Rði; lteÞtx ; ð14Þ

where, blte and bidlelte are the baseline powers in active and idle mode,

respectively; atx is the power consumption per Mbps in uplink, andRði; lteÞtx is the average data rate transmitted by user i over the LTEinterface. The value of Rði; lteÞtx is computed using the following twopropositions.

Proposition 7. Using DRONEE-W, the uplink LTE data rate of useri 2 Cn is given by

Rði; lteÞtx ¼ wn Stot

XM

k¼1

pðCnÞk bk

Z 1


ðzÞ; ð15Þ

where Yi ¼maxj2CnnfigfCjg; i 2 Cn.

The proof of Proposition 7 is omitted since it follows the samescheme of the proof of Proposition 3, considering that, with DRO-NEE-W, in the kth MCS the achieved data rate is wn Stot bk.

Proposition 8. Using DRONEE-M, the uplink LTE data rate of useri 2 Cn is given by

Rði; lteÞtx ¼ Stot

XM

k¼1

pðiÞk bk

Z 1


ðzÞ; ð16Þ

where Yi ¼maxj – ifCjg; i 2 Cn.

The proof of Proposition 8 is omitted since it follows the samescheme of the proof of Proposition 6, considering that, with DRO-NEE-M, in the kth MCS the achieved data rate is Stot bk.

3.3.3. WiFi consumptionAs for WiFi consumption, we enhance the model proposed in

[11], which accounts for the power required for packet processingas well as for transmission. We additionally add the probabilitythat the WiFi interface of user i is in active mode PðiÞa . The resultingWiFi power consumption can be expressed as follows:

W ðiÞwifi ¼ PðiÞa bwifi þ 1� PðiÞa

� �bidle

wifi

þftxstx þ frxsrx þ jtxktx þ jrxkrx;ð17Þ

where bwifi and bidlewifi are the WiFi baseline powers in active and idle

mode, respectively; ftx and frx represent the power consumptionsdue to transmission and reception, respectively; stx and srx are thefractions of time spent in transmission and reception, respectively;jtx and jrx are the power consumptions due to packet processing in



Table 3Parameters used in the power model.

LTE WiFi

blte bidlelte

atx bwifi bidlewifi

ftx frx jtx jrx

1:29 0.59 438:39 0:14 0.08 0:46 0:44 0:11 0:09[W] [W] [nW/bps] [W] [W] [W] [W] [mJ] [mJ]


transmission and reception, respectively; eventually, ktx and krx arethe packet rates, respectively in transmission and reception.

The WiFi power consumption related parameters introduced inEq. (17) are computed as follows: ðiÞ srx is the ratio between thetransmission rate over the WiFi interface and the achievable rate

of the WiFi connection, i.e., for user i, we have sðiÞrx ¼ Rði;wifiÞrx =Rwifi;

ðiiÞ similarly, sðiÞtx is given by Rði;wifiÞtx =Rwifi; ðiiiÞ user i receives kði;wifiÞ

rx

packets per second over the WiFi interface, which can be computed

as the ratio between the rate Rði;wifiÞrx and the average packet size Lp;

and ðivÞ similarly, user i transmits kði;wifiÞtx ¼ Rði;wifiÞ

tx =Lp packets persecond. We assume that the achievable WiFi rate in each clusteris independent from the cellular network status and its average va-lue Rwifi is the same for all clusters (i.e., this is an input parameterfor our problem). If the achievable WiFi rate is larger than the in-

tra-cluster traffic (i.e., Rwifi >P

i2CnRði;wifiÞ

rx ¼P

i2CnRði;wifiÞ

tx ), then toevaluate the WiFi power consumption, we need to compute the

WiFi data rates Rði;wifiÞrx and Rði;wifiÞ

tx , and the probability PðiÞa that theWiFi interface of user i be active.

The following proposition tells how to compute Rði;wifiÞrx and

Rði;wifiÞtx .

Proposition 9. The WiFi data rate of user i 2 Cn is given by thefollowing expressions, which hold for the received and transmittedtraffic, respectively:

Rði;wifiÞrx ¼ ð1� diÞ � Rði; lteÞtx ; ð18Þ

Rði;wifiÞtx ¼ di �

Xj2Cnnfig

Rðj;lteÞtx ; ð19Þ

where

di ¼E½Ti�

E½TCn �: ð20Þ

The proof of Proposition 9 is given in Appendix A.

For the probability PðiÞa that the WiFi interface of user i be in ac-tive mode, we use the following result:

Proposition 10. The WiFi interface of user i is active with probabilityPðiÞa that is computed as follows:

PðiÞa ¼E½Ti� þ ð1� 2diÞRði; lteÞtx

Rwifi; ð21Þ

with di defined in (20).

The proof of Proposition 10 is reported in Appendix A.

3.3.4. Total consumptionUsing the results of Sections 3.3.2 and 3.3.3, the total power

consumption due to LTE and WiFi for a clustered user is expressedas follows:

W ðiÞtot ¼ bidle

lte þ bidlewifi þ blte � bidle

lte

� �PðiÞh

þ bwifi � bidlewifi

� � E½Ti� þ ð1� 2diÞRði; lteÞtx

Rwifiþ atx Rði; lteÞtx

þ frx þjrx

Lp

� �ð1� diÞ

Rði; lteÞtx

Rwifi

þ ftx þjtx

Lp

� �E½Ti� � diR

ði; lteÞtx

Rwifi: ð22Þ

The first term in Eq. (22) represents the baseline powerconsumption due to the presence of the two wireless interfaces;the second and third terms are due to increased baseline power


consumption, respectively on LTE and WiFi interfaces, when theinterfaces are active rather than idle; the forth term accounts forLTE uplink transmissions, while the fifth term is due to the recep-tion of packets over the WiFi interface when the user is clusterhead; finally, the last term in Eq. (22) represents the power spentto send WiFi traffic to the cluster head.

4. Numerical evaluation

In this section we use the model presented in Section 3 tonumerically simulate networks with single and multiple base sta-tions. We use LTE uplink frame parameters for a network operatingon a 20 MHz bandwidth in FDD mode [18]. Each scenario is simu-lated 5000 times and the user channel quality changes at randomin each simulation (according to a probability distribution that willbe specified for each described experiment). Final results are ex-pressed using averages, 25th and 75th percentiles computed overthe simulated runs.

The performance of DRONEE-W and DRONEE-M is benchmarkedagainst Round Robin (RR) and proportional fair (PF) schedulers. Theimplementation details of these schedulers in our simulations canbe found in Appendix B. RR and PF do not use clustering, and thusthey can be used to compute per-user throughputs only. However,for comparison reasons, for each cluster formed under DRONEEoperations, we will present the sum of RR or PF throughputs thatcluster members would achieve if DRONEE were not used. Suchan aggregate throughput is referred to as the per-cluster RR or PFthroughput. To obtain the power consumption of legacy schedulers,we use Eq. (14), in which we replace PðiÞh with the probability of user ibeing scheduled under RR or PF, respectively.

For our computations, we use an average packet size Lp ¼ 1000B and average WiFi net rate Rwifi ¼ 30 Mbps, which is a reasonablenet data rate achievable for 802.11a/g standards [25]. The valuesfor power related parameters used in the evaluation can be foundin Table 3 and are derived from [10,11].

4.1. Clustering impact

Before evaluating DRONEE, let us first evaluate the fundamentalfactors that can affect its performance: ðiÞ the channel quality ofthe cluster members (see Fig. 2(a)); and ðiiÞ the cluster size (seeFig. 2(b)). The importance of the former factor can be observedby comparing the user’s channel state probabilities, as defined inEq. (3). The primary effect of opportunistic cluster head selectionis to increase the probability of transmitting with higher MCS val-ues. This effect magnifies in clusters with poor and good userswhere the good users help the poor ones and increase the probabil-ity of transmission with a high MCS. In Fig. 2(a), we observe that,with a cluster composed of one user from each quality class (C1in the figure), the transmission rate of poor and average usershighly boosts. Since transmission probabilities for poor and averageusers are mostly accumulated in low transmission rates, the clus-tering improvement for good users is marginal. We note thatincreasing the cluster size significantly increases the probabilityof using high transmission rates (see case C2 in the figure).

Fig. 2(b) illustrates the achieved spectral efficiency for differentcluster sizes. Here we use the spectral efficiency instead of theachieved throughput so that results are independent on the airtime



0

0.1

0.2

0.3

0.4

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0.7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Prob

abilit

y M

ass

Func

tion 1 Poor user

1 Avg user1 Good user

C1 (1 Poor + 1 Avg + 1 Good users)

MCS index

C2 (5 Poor + 5 Avg + 2 Good users)

(a) Impact of clustering on channel state probabilities.

2

2.5

3

3.5

4

0 5 10 15 20 25 30 35 40 45 50

Spec

tral E

ffici

ency

[bps

/Hz]

Number of users(b) Impact of cluster size on spectral efficiency of the cluster.

Fig. 2. Impact of clustering on channel state probabilities and spectral efficiency.


allocated to the cluster in presence of other clusters. Since eachaddition of an extra cluster member increases the probability oftransmitting with higher MCSs, it is natural to expect that thisprobability will eventually approach to one. In Fig. 2(b), such satu-ration occurs after the cluster size reaches 6 users with uniformrandom user quality distribution. As the cluster size increases from7 to 50 users, the spectral efficiency experiences a saturation effect,which consists in marginally small successive improvements.Therefore, forming very large clusters is not beneficial in terms ofspectral efficiency.

4.2. Clustering in a single cell network

We begin the evaluation of energy efficiency and resource utili-zation with a simple scenario comprising three fixed-size clusters,which are attached to the same eNB, as shown in Fig. 3. Clustershave different sizes and cluster members have independent butnot identically distributed SNRs. Unless otherwise specified, userqualities are chosen according to a uniform random distribution.Fig. 4 illustrates the throughput performance of different schedul-ing algorithms.

In Fig. 4(a), it can be observed that the average per-userthroughput is substantially higher under DRONEE variants thanunder legacy schedulers. DRONEE outperforms RR and PF due toits more efficient use of spectrum, which is the result of opportu-nistic selection of cluster heads (cooperative gain). Since the

Fig. 3. Evaluation topology for static clusters.


cluster head is the user with the highest instantaneous channelquality, the cluster resource utilization is maximized at eachscheduling epoch. The high variation observed under DRONEE-Mis due to its greedy behavior that can lead to starvation of clusterswith only poor and average users and over-serving the clusterswith good users. RR performs the worst because it schedules theusers regardless of their channel quality. In contrast, PF uses anopportunistic scheduling technique that results in remarkablethroughput enhancement in comparison to RR. Nevertheless, DRO-NEE-M and DRONEE-W largely outperform PF.

Fig. 4(b) shows that all clusters receive higher throughput withDRONEE than with legacy schedulers. Bigger clusters receive moreairtime under RR, PF, and DRONEE-W because the airtime allottedto clusters is deterministic and proportional to the number of clus-ter members. Under DRONEE-M the airtime grows statistically withthe cluster size because adding an extra member increases theprobability of having the user which has the best MCS in the cell.Therefore, with a pure MaxRate approach, such as in DRONEE-M,cluster throughputs will suffer from the saturation effect discussedearlier and depicted in Fig. 2(b). In contrast, distributing resourcesproportionally to cluster sizes, as in DRONEE-W, delays the occur-rence of saturation. This effect can be observed in Fig. 4(b): as thecluster size increases from 6 in C1 to 10 in C3, throughputs growfaster with DRONEE-W than with DRONEE-M.

Fig. 4(c) illustrates aggregate cell throughputs under differentuser quality distributions. In order to evaluate the impact of userquality distribution on the performance of our proposal, we con-sider three scenario sub-cases, characterized by different userquality distributions as stated in Table 4.

The results shown in Fig. 4(c) confirm that DRONEE has higherthroughput than user-based schedulers regardless of user qualitydistributions. DRONEE-M has better throughput performance thanDRONEE-W because it always schedules the cluster head with thebest channel quality (opportunistic gain). In contrast, DRONEE-Wdistributes the resources among clusters based on their size andnot the channel quality of cluster heads. Nevertheless, the through-put gains are affected by user quality distribution. The throughputgain of DRONEE over RR increases from 50% to 162% as we movefrom sub-case SC III to SC II and SC I, i.e., as the percentage of goodusers reduces. Similarly, the gain over PF ranges approximatelyfrom 12% to 50% over the different sub-cases. The increment inthroughput gain is due to the fact that increasing the number ofpoor users, also increases the opportunity for DRONEE to enhancethe spectral efficiency of the system. The throughput gain of DRO-NEE over PF is less than that of RR due to the opportunistic natureof the PF scheduler. Fig. 4(c) also shows that the system has nearoptimal performance with DRONEE when as few as 33% of theusers are good (sub-case SC II).

Fig. 5(a) confirms the energy efficiency significance of our pro-posal. It can be seen that DRONEE variants are much more energyefficient than legacy mechanisms. Indeed, using DRONEE providesa minimum gain of 30% in energy efficiency with respect to PF(sub-case SC III), and the gain can reach up to 100% in presenceof more poor users (sub-case SC I). The energy efficiency gain withrespect to RR is even more significant in all sub-cases depicted inFig. 5(a). Therefore, DRONEE not only highly increases the systemthroughput, but also the cost (energy per bit) of transmission is re-duced considerably.

Eventually, we comment about fairness, using the well knownJain’s index [26]. Fig. 5(b) shows that fairness is the lowest whenthere are more poor users in the system (sub-case SC III). DRO-NEE-W achieves the highest per-user fairness level in the system,even higher than PF, which is a scheduler designed for fairness.In general, DRONEE has an extra fairness advantage because it al-lows to distribute the throughput gain achieved via clusteringamong all cluster members, which leads to smoothening the



0 0.5

1 1.5

2 2.5

3 3.5

4 4.5

5

C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3

Use

r Thr

ough

put T

i [M

bps]

RR PF DRONEE-W DRONEE-M

(a) Per-user per-cluster throughput.

0

5

10

15

20

25

30

35

40

C1 C2 C3

Clu

ster

Thr

ough

put T

Cn [

Mbp

s]


(b) Aggregate per-cluster throughput.

20

30

40

50

60

70

80


Aggr

egat

e Th

roug

hput

[Mbp

s]

SC I SC II SC IIIMaximum achievable throughput

(c) Aggregate cell throughput.

Fig. 4. Per-user, per-cluster, and aggregate throughput for single cell scenario (Fig. 3).

Table 4User quality distributions used in different scenarios

Scenariosub-case

% of poorusers (%)

% of avgusers (%)

% of goodusers (%)

SC I 60 30 10SC II 33:3 33:3 33:3SC III 10 30 60


throughput difference among poor and good users within the samecluster. However, DRONEE-M still has the worst fairness which isdue to its bias towards serving the cluster with the best user inthe network, which exceeds the smoothening effect of clustering.

We can summarize the performance results reported in this sce-nario as follows: ðiÞ DRONEE is significantly more energy efficientthan RR and PF (up to 100% efficiency gain over PF); ðiiÞ DRONEEprovides a high throughput gain with respect to legacy RR and PFschedulers (up to 50% throughput gain over PF and 162% overRR); ðiiiÞ between DRONEE variants, DRONEE-M shows poor fairnessin presence of more poor users, whilst DRONEE-W outperforms PFand RR in terms of fairness and nearly achieves perfect fairness.

4.3. Clustering across cells

Another interesting scenario in which clustering can be benefi-cial to uplink efficiency is the case of clusters formed by users asso-ciated to different eNBs. This scenario is of paramount importancefor users located at the edge of their cells, whose channel can sufferfrom deep fading fluctuations. Clustering is in particular advanta-geous here because it improves the spectral efficiency of the userswith poor channel quality [22].

We evaluate a scenario with three neighboring cells as depictedin Fig. 6. In the figure, clusters C3, C4, and C7 result from the merg-ing of two sub-clusters (namely, Cia and Cib, i 2 f3;4;7g) formedby users at the edge of the cells. Each sub-cluster is connected toits corresponding eNB (e.g., C3a and C4a are connected to eNB1).

0

0.5

1

1.5

2

2.5

3


Ener

gy E

ffici

ency

η [M

b/J]

SC I SC II SC III

(a) Per-user energy efficiency.

Fig. 5. Energy efficiency and fairness performanc


Although sub-clusters are connected to different eNBs, they sharetheir resources within the entire cluster via WiFi connectivity. Notethat in this scenario, clusters composed of sub-clusters have a dif-ferent cluster head for each cell in which they have members. Sinceeach user can access two cluster heads in two different cells, theycan distribute their load between cells.

In this set of simulations, clusters that are not located at the celledge have uniform user quality distribution. In contrast, clusters atthe edge of cell can have poor, average and good users with proba-bility 60%;30%;10%, respectively. In each experiment, clustersizes change randomly with a uniform distribution, using theranges reported in Fig. 6. These intervals are chosen so that eNB1has more users than eNB2, and eNB3 has the fewest users, on aver-age. With the multi-cell scenario we add to our performance eval-uation the study of the impact of heterogeneity in the geographicaldistribution of mobile users.

Fig. 7 illustrates that DRONEE achieves better throughput per-formance than legacy schedulers in the multi-cell scenario, underall combinations of cluster sizes and locations. DRONEE-W outper-forms PF and RR in terms of per-user and per-cluster throughput inall clusters, see Fig. 7(a) and (b). In contrast, under DRONEE-M,users located in cell edge clusters (C3, C4, and C7) achieve slightlyless throughput than DRONEE-W (� 0:3 Mbps less); at the sametime, users in the other clusters experience better throughput thanDRONEE-W (up to � 1:8 Mbps more). This happens because DRO-NEE-M prioritizes the clusters with the best channel quality, sothat cell edge clusters have less chance to be scheduled. We canobserve in Fig. 7(c) that per-cell throughput gain is substantialwith DRONEE. Indeed DRONEE-M almost achieves the maximumthroughput. Although DRONEE-W achieves less throughput thanDRONEE-M, it still brings a considerable throughput gain(� 20%) with respect to PF. Therefore, the results confirm thatour proposal achieves higher resource utilization even under het-erogeneous distribution of user locations and with heterogeneouscluster compositions. Moreover, our results point out that clustersize matters, as witnessed by the fact that users under eNB3achieves lower throughputs than users in other cells. In fact,

0.5

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0.75

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0.85

0.9

0.95

1


Jain

’s In

dex

SC I SC II SC III

(b) Per-user Jain’s fairness indexes.

e under different user quality distributions.



Fig. 6. Three base stations with nine clusters. Clusters C3, C4, C7 are composed bytwo sub-clusters belonging to two adjacent cells.


eNB3 has the smallest user population among the three cells,which leads to smaller cooperative gain (see Fig. 2(b)).

Fig. 8(a) shows that our proposal highly improves energyefficiency of users (54% to 77% with respect to PF). DRONEE hasbetter energy efficiency because it reduces the energy per bit trans-mission cost by increasing the spectral efficiency of the network. Ingeneral, we observe that with fewer users we can achieve higherenergy efficiency under all scheduling disciplines (the efficiencyin eNB3 is always the highest, with or without clusters). This canbe explained by looking into the definition of energy efficiency,which is given by per-user throughput over power consumption.Now, while increasing the number of users significantly reducesthe per-user throughput, the per-user power consumption—whichis mainly due to the baseline power required by the wireless inter-faces—suffers little variations. Therefore, large cell populations areinherently energy inefficient, which makes it of paramountimportance to boost the energy efficiency of dense networks. Nota-bly, our proposal introduces such a significant leap in efficiency.

0

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3

4

5

6

7

8

9

C1 C2 C3 C4 C5 C6 C7 C8 C9

Use

r Thr

ough

put T

i [M

bps]


(a) Per-user throughput.

0

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50

C1 C2 C3 C4 C

Clu

ster

Thr

ough

put T

Cn [

Mbp

s]

RR PF DRONE

(b) Per-cluste

Fig. 7. Throughput under different scheduling

0

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Ener

gy E

ffici

ency

η [M

b/J]

eNB1 eNB2 eNB3 net

(a) Per-cell energy efficiency.

Fig. 8. Energy efficiency and per-user fairness under different scheduling mechanisms forperformance observed over the three-cell network of Fig. 6.


Moreover, according to our results, clustering boosts energy effi-ciency, but large clusters are not needed to achieve high energyefficiency.

Eventually, Fig. 8(b) illustrates the fairness achieved in eachcell. DRONEE-W provides the highest fairness in the system. Asdiscussed earlier, DRONEE-M prioritizes the clusters with highchannel quality over cell edge clusters, which results in bigthroughput differences among users and poor fairness perfor-mance in the multi-cell scenario. Cells with lower user populationare generally prone to lower user fairness due to the compound ef-fect of the following factors: ðiÞ per-user allocated airtime is largerin small cells; ðiiÞ good users can exploit the extra airtime obtainedthanks to clustering better than poor users; and ðiiiÞ the advantagederiving from the presence of good users in a cluster is not sharedamong clusters. As a result, the throughput difference among usersin small and heterogeneous cells is higher and results in lower fair-ness indexes if compared to the results for larger cells.

Summarizing the results discussed in this sub-section, we con-clude that: ðiÞ DRONEE highly improves the energy efficiency withrespect to RR and PF, which is particularly useful in case of densenetworks, where the efficiency is impaired by the baseline con-sumption of (idle) users; ðiiÞ DRONEE provides a high throughputgain with respect to legacy RR and PF schedulers, irrespective ofthe cell size; ðiiiÞ between DRONEE variants, DRONEE-M showspoor fairness performance due to its bias towards serving goodusers; whilst ðivÞ DRONEE-W outperforms PF and RR and achievesthe highest fairness levels in a variety of scenarios, i.e., underdifferent distributions of users over cells and clusters.

5. DRONEE implementability over LTE and WiFi Direct

In this section, we corroborate the practical relevance of DRO-NEE by providing a first-order discussion on its implementability.We use LTE release 10 [18] and WiFi Direct [27] specifications toshow that our proposal can be implemented on top of existingand widespread technologies.

5 C6 C7 C8 C9

E-W DRONEE-M

r throughput.

30 35 40 45 50 55 60 65 70 75 80

eNB1

eNB2

eNB3

eNB1

eNB2

eNB3

eNB1

eNB2

eNB3

eNB1

eNB2

eNB3

Aggr

egat

e Th

roug

hput

[Mbp

s]

RR PF DRONEE-W DRONEE-MMaximum achievable throughput

(c) Aggregate throughput per cell.

mechanisms for the multi-cell scenario.

0.5

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0.8

0.9

1


Jain

’s In

dex

eNB1 eNB2 eNB3 net

(b) Per-user Jain’s fairness indexes.

the multi-cell scenario. The net values reported in the figures represent the average




The key procedures composing the protocol needed to imple-ment opportunistic clustering using LTE and WiFi Direct are thefollowing: ðiÞ cluster formation, which allows mobile devices to findand join existing clusters or setup new clusters using the WiFiinterface; ðiiÞ registration, to make the LTE eNB aware of the clusterexistence and composition; ðiiiÞ grant request, to allow the clusterhead to request a cumulative traffic grant which covers the de-mand of the entire cluster; ðivÞ Channel State Information (CSI) col-lection, to collect channel state information at the LTE eNB; ðvÞcluster head selection, to decide which user behaves as cluster head,based on the collected channel state information; ðviÞ bearerallocation, to allocate portions of the uplink frame to cluster headsinstead of any cluster members; ðviiÞ scheduling, to allocate re-sources among clusters (scheduling of clusters as if they wereusers); ðviiiÞ resource sharing, to share resources among clustermembers, using WiFi Direct (intra-cluster resource sharing proce-dure); and ðixÞ security, to protect relayed traffic.

Before presenting the network procedures needed to imple-ment DRONEE, it is crucial to recall how LTE handles user commu-nications. In LTE, mobiles are connected to the network via bearers.A bearer is a data pipe that is used for data transfer between theuser and the network. Each bearer has a specific Quality of Service(QoS) that depends on the type of the traffic carried over the bearer[28]. For example, a VoIP connection and an HTML connection fromthe same device are established on separate bearers.

Cluster formation. The cluster formation can be easily per-formed using WiFi Direct group formation capabilities. The mobiledevice willing to form a cluster can transmit a Probe Request onWiFi Social Channels, i.e., channels 1;6 and 11 in the 2:4 GHz ISMband. Alternatively, the device can listen to Probe Requests on So-cial Channels in order to join existing clusters or other devices will-ing to form a cluster. WiFi Direct specifications state that the groupformation shall not take more than fifteen seconds. Moreover, clus-ter members meeting each other on a regular basis, can use persis-tent groups as defined in the standard to reduce the clusterformation time. Given the current group formation speed of WiFiDirect, our proposal is attractive in scenarios in which the expectedcluster life is in order of minutes. For instance, the cluster forma-tion overhead is negligible for users sharing significant time inthe public transportation system or during dense public eventssuch as football matches or concerts, in which users usually expe-rience low quality service due to overloaded infrastructure.

Registration. Once a cluster is formed, the cluster head sends acluster formation notification message to the eNB over the common/dedicated control channel, which is used for RRC connection estab-lishment. Next, the eNB forwards the notification message, whichcontains the identities of cluster members, to the Policy and Charg-ing Enforcement Function (PCEF). Finally, the PCEF checks the sta-tus of the current bearers associated to the cluster members andallocates new cluster specific bearers accordingly. Depending onthe number and the type of applications used in the cluster, a clus-ter can be allocated one or more bearers. When a member joins orleaves the cluster, the current cluster head can use the Physical Up-link Control CHannel (PUCCH) to send a notification to PCEF to up-date the cluster membership list and the QoS profile of the bearersallocated to the cluster.

Grant request. LTE devices in a cluster, but the cluster head, pre-pare their grant requests for uplink traffic and forward it to the clus-ter head, encapsulating the request in a normal WiFi packet. Thenthe cluster head sends a single LTE uplink traffic grant request forthe bearer associated to the cluster head. Alternatively the clusterhead can just forward all traffic grant requests to the eNB, which willmap all traffic grant requests to the special cluster bearers. In anycase, the eNB is not responsible for the repartition of resources with-in the cluster. Note that using special cluster bearers at the eNBdramatically simplifies the scheduling operation of the base station.


CSI collection. Since mobile devices in LTE regularly performchannel quality measurements, they can inform the cluster headregarding their channel state information by sending the CSI usingWiFi Direct. The cluster head collects the channel state informationfor the entire cluster, but it sends to the eNB only the channel stateinformation for the node who will be the next cluster head (see be-low for the detail on the procedure to select the cluster head).

Cluster head selection. The cluster head selection procedure isperformed locally in each cluster, without imposing extra burdenon the eNB. The cluster head collects LTE channel state informationfrom the other cluster members, and decides whether in the nextcluster scheduling interval it should keep the role of cluster heador select a new cluster head. The cluster head is responsible tobroadcast a WiFi message regarding the identity of the next clusterhead to all cluster members. Moreover, it has to notify the eNBabout the change of cluster head. The message sent to the eNB tonotify it of the identity of the next cluster head also contains thechannel state information of the next cluster head. This is benefi-cial for LTE capacity, because it eliminates the need to allocatebandwidth for feedback from other cluster members. Note thatWiFi Direct does not allow cluster members to transfer the groupownership among themselves, however, it is possible to have par-allel overlay groups, each with a different group owner. Thus, everycluster member can create a group where it is the group owner andthe rest of the cluster members are WiFi Direct clients. In everyframe, the group owner which is also the cluster head controlsthe channel. Since the rest of the cluster members are notifiedabout the next cluster head, there will be no interference and col-lisions among overlay groups. According to the results presented inSection 4, forming clusters with more than ten members has mar-ginal benefits in terms of efficiency; hence, the number of neededoverlay WiFi Direct groups is practically limited to a few units.

Bearer allocation. Once the cluster is registered at the eNB, all LTEtraffic bearers associated to any cluster member are mapped onto thespecial cluster bearers. This way, the eNB frame builder can allocateuplink resources to the cluster bearers by using the channel stateinformation of the cluster head at any scheduling epoch.

Scheduling. In presence of multiple clusters, the amount of re-sources allocated to each cluster head is decided based on DRO-NEE-M or DRONEE-W. This procedure requires the knowledge ofthe CSI of the cluster heads, and, for the case of DRONEE-W, thenumber of cluster members in each cluster. This information isavailable at the eNB, thanks to the above described proceduresused to collect channel state information and to register clusters(and cluster members) at the eNB.

Resource sharing. Fig. 9 represents the data plane of the proto-col stack used in DRONEE. As depicted in the figure, the clusterhead keeps a queue Q i for each cluster member i, to separatelystore the traffic received over the WiFi interface, plus a queue forits own uplink traffic. As described above, the resource distributionamong clusters is handled by the eNB as if clusters were normalusers. The eNB uses the Physical Downlink Control CHannel(PDCCH) to inform the cluster head about the granted resourcesand their owners. Alternatively, the cluster head can receive acumulative grant and locally distribute the resources among theuplink queues using simple policies, e.g., equal time, equal rate, etc.

Security. DRONEE requires cluster members to cipher their traf-fic according to LTE specifications. In LTE, ciphering is done in thePacket Data Convergence Protocol (PDCP) layer before the RadioLink Control (RLC) and MAC layers. As shown in Fig. 9, we extendthe ciphering requirement to the traffic that has to be relayed,although that traffic has to go through the WiFi protocol stack.Therefore, in DRONEE, cluster members use ciphered PDCP PDUsas payload of the WiFi frames that have to be sent to the clusterhead for relay. Although the cluster head cannot read the ciphereddata, it can process and forward each PDCP PDU through RLC, MAC



Fig. 9. Data flow for uplink traffic generated at the cluster head and at other cluster members in a cellular network using DRONEE.


and PHY layers, thus relaying it to the eNB. However, the relayedtraffic has to carry the MAC identifier of the original sender, so thatthe eNB can identify the source of the data and thus decipher itwith the correct key. Since the deciphering key is only known tothe eNB, the integrity of the relayed traffic will be protected andany data manipulation can be detected by the eNB. Note that, withthe described procedure, all PDUs are transmitted exactly as in alegacy LTE system, with no extra protocol overhead being causedby DRONEE. Thanks to this mechanism, DRONEE does not intro-duce any new security risks to the current LTE architecture.

6. Conclusions

In this article, we have proposed a new approach to seamlesslyinterwork LTE and WiFi in hybrid networks. Our approach bringssignificant improvement not only in terms of energy efficiency,but also in terms of throughput and fairness. We have also shownthat our proposal could be implemented in a real system using LTEand WiFi technologies. It is shown that our proposal is effective inseveral scenarios, spanning from single cell scenarios to multiplecells, with variable cell populations and with variable and hetero-geneous distributions of user qualities. In particular, DRONEE-Whas very desirable properties compared to other schemes. DRO-NEE-W is simple and scales with network size because it buildson a simple weighted round robin scheduler. It achieves through-puts close to the ones achieved by throughput optimal scheduler,e.g., MaxRate, and largely outperforms other mechanisms in termsof energy efficiency and achieved fairness. Moreover, our proposedschemes reduce the complexity of scheduling operations, and helpboth operators—by increasing resource utilization efficiency—andmobile users—by increasing throughput and battery life of theirsmartphones. Therefore, DRONEE is key to design future energyefficient wireless networks.

Acknowledgements

The research leading to these results has received funding fromthe European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n� 318115 (CROWD). This publica-tion was supported in part by the Comunidad de Madrid MEDIA-NET Project, Ref.: S2009/TIC-1468.

Appendix A. Proofs of propositions

Proof of Proposition 3

In DRONEE-W, each cluster Cn is scheduled with probability wn.When cluster Cn is selected by the scheduler, its user with thehighest SNR is actually scheduled. For each possible MCS k, useri 2 Cn is the one experiencing the highest SNR in its cluster with


probability PðijkÞh ¼ PrðCi > YijMCSi ¼ kÞ;Yi ¼maxj2CnnfigfCjg. Sincechannels are independent, using the total probability formula

yields PðijkÞh ¼R1

0 1� FiðzjMCSi ¼ kÞ½ �dFYiðzÞ. Given that pðCnÞ

k repre-sents the probability that cluster Cn can be scheduled with the k-th MCS, the result follows by applying again the total probabilityformula for the discrete set of MCS values. h


The proof is similar to the proof of Proposition 3. In DRONEE-M,the scheduled cluster Cn receives all resources Stot , given that thecluster contains the user with the highest SNR in the system. Foreach possible MCS k, cluster Cn is the one containing the user expe-

riencing the best SNR with probability PðCn jkÞh ¼ PrðXn > Ynj

MCSCn ¼ kÞ, with Xn ¼maxj2CnfCjg, and Yn ¼maxj:2CnfCjg. Sincechannels are independent, using the total probability formula

yields PðCn jkÞh ¼

R10 1� FXn ðzjMCSCn ¼ kÞ½ �dFYn ðzÞ. Given that pðCnÞ

k

represents the probability that cluster Cn can be scheduled withthe k-th MCS, with which the transmitted bits per symbol are bk,the result follows by applying again the total probability formulafor the discrete set of MCS values. h


In DRONEE-M, a user is scheduled when it has the highest SNR.Therefore, for each possible MCS k, user i is scheduled with proba-bility PðijkÞh ¼ PrðCi > YijMCSi ¼ kÞ, with Yi ¼maxj:¼ifCjg. Sincechannels are independent, using the total probability formulayields PðijkÞh ¼

R10 1� FiðzjMCSi ¼ kÞ½ �dFYi

ðzÞ. Given that pðiÞk repre-sents the probability that user i can be scheduled with the k-thMCS, the result follows by applying the total probability formulafor the discrete set of MCS values. h


Due to our stationary traffic and channel quality assumptions,the traffic distribution over a scheduling interval is the same asthe long term distribution of throughputs within the cluster Cn.Let us denote by di the ratio between the user’s throughout E½Ti�and the total cluster throughput E½TCn �. Therefore, the traffic re-ceived over WiFi, Rði;wifiÞ

rx , is a fraction 1� di of the traffic transmittedover the LTE interface by user i, which yields Eq. (18).

Similarly, the WiFi transmission data rate Rði;wifiÞtx corresponds to

a fraction di of all the traffic delivered by LTE from the other clustermembers to the base station, when user i is not the cluster head,which yields Eq. (19). h





PðiÞa is the sum of two terms: the probability that user i is thecluster head and receives traffic from other cluster members, andthe probability that user i is not cluster head and transmits itspackets to the cluster head. Since such probabilities can beinterpreted as the average fraction of time spent in either in recep-tion or transmission over the WiFi interface, we have the followingexpression for PðiÞa :

PðiÞa ¼ ð1� diÞRði; lteÞtx

Rwifiþ di

E½TCn � � Rði; lteÞtx

Rwifi; ðA:1Þ

which leads to the result. h

Appendix B. Implementation details of RR and PF

As for the throughput of RR and PF, we use the followingapproach. RR is a simple scheduling method which distributesthe available resources equally among all users. RR can distributeresources in a manner that each user receives equal airtime (i.e.,equal time) or equal throughput (i.e., equal rate). In this paper, weuse the equal time policy because the equal rate policy would dras-tically reduce the system throughput in presence of poor users.With RR, the throughput of each user only depends on the totalnumber of users in the system, the probability to transmit with agiven MCS, and the total amount of resources Stot:

E½Ti� ¼1N

Stot

XM

k¼1

pðiÞk bk; i 2 f1 . . . Ng; ðB:1Þ

where pðiÞk values are computed through Eq. (3), and 1=N is theprobability of any user being scheduled.

PF is a priority-based opportunistic scheduler with fairnessconstraints. Under PF, scheduling priorities are determined bythe ratio of feasible data rate to average throughput at each timeinstant t (i.e., RiðtÞ=liðtÞ; 8i 2 f1 . . . Ng). In this work, we compareour proposal against PF because of its popularity in research com-munity and because it has been partially implemented in 3G sys-tems [29]. Unfortunately the majority of analytical models with aclosed form expression for throughput of PF does not produceaccurate results in scenarios in which data rates do not followthe Shannon capacity formula. Therefore we use a home grownC++ simulator to evaluate the performance of PF, in which weimplement a PF averaging window [29] equal to 100 frames, andschedule only one user per frame.

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http://refhub.elsevier.com/S0140-3664(14)00067-X/h0080

































http://dx.doi.org/10.1109/SURV.2013.011413.00082

http://dx.doi.org/10.1109/SURV.2013.011413.00082

http://www.3gpp.org/ftp/Specs/html-info/36321.htm

http://www.3gpp.org/ftp/Specs/html-info/36321.htm

http://www.wi-fi.org/wi-fi-peer-peer-p2p-specification-v11








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