Multicasting in LTE-A Networks Enhanced byDevice-to-Device Communications

Massimo Condoluci, Leonardo Militano, Giuseppe Araniti, Antonella Molinaro, Antonio IeraUniversity Mediterranea of Reggio Calabria, Italy, DIIES Dep.

e-mail: [massimo.condoluci|leonardo.militano|araniti|antonella.molinaro|antonio.iera]@unirc.it

Abstract—The growing demand for group-oriented serviceshas recently attracted the interest of the research community.Several proposals have been designed for the most promisingbroadband wireless system, Long Term Evolution-Advanced(LTE-A), to enhance key performance figures such as spectrumefficiency, data rate and user satisfaction. Starting from thestandard proposals for multicasting and broadcasting systems,in this paper we investigate the potentialities of Device toDevice (D2D) communications for enhancing the performanceof multicast communications. While keeping the objective ofserving all users in a multicast group, as for the ConventionalMulticast Scheme (CMS), more performing Modulation andCoding Schemes (MCS) are adopted in the path from the basestation to users, by leveraging D2D links to serve nodes with worsechannel conditions. Radio resources for the activated transmissionlinks from the base station and for the D2D links are managedin order to maximize the aggregate data rate. A simulativeperformance evaluation in a wide set of scenarios shows thesignificant achievable improvements.

Keywords—LTE-Advanced, Multicast, Device-to-Device, RadioResource Management

I. Introduction

THE growing demand for group-oriented services (i.e.,multicast and broadcast) has led to the definition of new

standards and multimedia applications for mobile terminals.Long Term Evolution-Advanced (LTE-A) [1] seems to be themost promising broadband wireless system able to supportsuch services with several benefits to the user and the networksides. For instance, it guarantees high data rates in downlinkand uplink directions, efficient Quality of Service (QoS) man-agement, high spectrum efficiency and high system capacity.While these features make LTE-A very attractive for group-oriented services, researchers are active in studying solutions toeffectively handle the diversity of channel quality experiencedby users in the same Multicast Group (MG) and to efficientlyuse the available resources [2].

The baseline proposal for multicasting and broadcastingsystems in LTE [3] is also called Conventional MulticastScheme (CMS) in the literature, where all User Equipments(UEs) are served in every Transmission Time Interval (TTI),but the total data rate is limited by the user with the worstchannel conditions. As a consequence, a poor performancelevel is achieved in terms of network data rate and satisfaction

The research of Massimo Condoluci is supported by European Union,European Social Fund and Calabria Regional Government. This paper reflectsthe views only of the authors, and the EU, and the Calabria RegionalGovernment cannot be held responsible for any use which may be made ofthe information contained therein.

experienced by the users with good channel conditions. Alter-native approaches exist, such as for example the OpportunisticMulticast Scheme (OMS) [4]. In line with OMS, not allUEs are served in a given time interval and the system datarate is optimized according to the channel quality. A furtherinvestigated approach is based on the multicast subgroupingpolicies [5] [6], [7], whereby the multicast destinations aregrouped into different subgroups depending on the UE channelquality.

In this paper, a scheme is proposed which exploits an-other flourishing research field within LTE-A systems, namelyDevice-to-Device (D2D) communication. In particular, UEsbeing close to each other can activate direct links by usingcellular communication resources [8].There are a number ofscenarios where D2D communications can provide significantimprovements beyond classic relaying approaches in LTE-context. For example, data offloading for proximity basedapplications is investigated in [9]. Whenever communicationis inherently local in scope, D2D links could be substantiallymore efficient than conventional traffic forwarding through abase station [10] and [11]. Furthermore, D2D communicationscould also enable mobile terminals to act as relays, thusextending the network coverage or supporting content sharingamong users [12]. These observations led us to the ideaof bringing D2D communications into the multicast servicesframework, and investigating the potentialities of adoptingD2D communications to improve the mentioned CMS solution.In particular, the proposed scheme keeps the philosophy ofthe standard CMS to serve all UEs in every TTI, but it takesinto consideration the possibility that not all UEs are to beserved directly by the base station and D2D communicationscan be leveraged to reach all users in the group. This allows thebase station to use better performing Modulation and CodingSchemes (MCS), while nodes with worse channel conditionsare served through D2D links. A simulative performanceevaluation in a wide set of scenarios will show significant datarate improvements when comparing the proposed solution withthe CMS solution, given a fixed amount of globally allocatedresources.

The remainder of the paper is organized as follows. In thenext section, the reference system model and service scenarioare introduced. Section III introduces the proposed algorithmin details, while performance evaluation results are given insection IV. The reader will find some conclusive remarks inthe last section.

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II. Reference System and Service Scenario

Focus of this paper is on a multicast single-cell scenarioin LTE-A networks. The LTE-A downlink air interface isbased on the Orthogonal Frequency Division Multiple Access(OFDMA). Spectrum is managed in terms of Resource Blocks(RBs) and in the frequency domain each RB corresponds to12 consecutive and equally spaced sub-carries. One RB is thesmallest frequency resource, which can be assigned to a UE.The overall number of available RBs depends on the systembandwidth configuration and can vary between 6 (1.4 MHzchannel bandwidth) and 100 (20 MHz). Through a carrieraggregation scheme, up to five Component Carriers (CCs) canbe aggregated in order to reach a 100 MHz channel bandwidth.

A Packet Scheduler (PS) is implemented at the MediumAccess Control (MAC) layer [13]. The main functionality ofthe PS is to efficiently handle the resource allocation in thetime and frequency domains. The Frequency Domain PacketScheduler (FDPS) is in charge of the spectrum management,by assigning the adequate number of RBs to each scheduleduser and by selecting the MCS for each RB. These proceduresare conducted based on the Channel Quality Indicator (CQI)feedback transmitted by the UE to the base station. The CQI isassociated to the maximum supported MCS [1]; Table I showsthe CQI values defined by the LTE-A standard. Transmissionparameters (i.e., MCS) are adapted at every CQI FeedbackCycle (CFC), which can last one or several Transmission TimeInterval (TTI, equal to 1 ms) [1]. Also for D2D communica-tions to be activated in the cell, the base station is in charge ofallocating the resources and determining the associated MCSs.

TABLE I. CQI-MCS Mapping [14]

CQI Modulation Code rate Efficiency Minimum Rateindex Scheme x 1024 [bit/s/Hz] [kbps]

1 QPSK 78 0.1523 25.592 QPSK 120 0.2344 39.383 QPSK 193 0.3770 63.344 QPSK 308 0.6016 101.075 QPSK 449 0.8770 147.346 QPSK 602 1.1758 197.537 16-QAM 378 1.4766 248.078 16-QAM 490 1.9141 321.579 16-QAM 616 2.4063 404.2610 64-QAM 466 2.7305 458.7211 64-QAM 567 3.3223 558.7212 64-QAM 677 3.9023 655.5913 64-QAM 772 4.5234 759.9314 64-QAM 873 5.1152 859.3515 64-QAM 948 5.5547 933.19

The reference service scenario for this paper is a multicastreal-time service, with a group of UEs interested in the samecontent served by an LTE-A cell, for instance students on-campus who are accessing a content of common interest. Tohandle the multicast service, the eNodeB collects the CQIfeedbacks from each UE belonging to the group, determinesthe corresponding CQI level, and then sends the data to all ora subset of UEs. Conventional Multicast Scheme (CMS) is asolution whereby all UEs are served in every TTI. The nodewith the lowest CQI level limits the data rate for all nodes,as all available resources (RBs) are all allocated to the singleactivated CQI level.

In this paper we propose a D2D-supported Conventional

Fig. 1. D2D-supported Conventional Multicast Scheme.

Multicast Scheme (D2-CMS) where all UEs are served in everyTTI by considering also the possibility to activate D2D linksto improve the system performance. In fact, users that are incoverage for a D2D communication can activate such a directlink according to the instructions received by the eNodeB. Inthese cases one of the UEs will act as a relay node receiving thecontent from eNodeB and then forwarding it to the connecteddevice. In the reference scenario, as depicted in Fig. 1, D2Dcommunications can be either unicast or multicast; thus, a relaynode can serve one or more connected UEs.

Concerning the resource allocation, we assume that down-link resources are allocated for communications from eNodeBto the relay nodes, while uplink resources are allocated forD2D communications. This choice is motivated by the factthat reusing downlink resources is more challenging thanreusing uplink resources in the worst case of a fully loadedcellular network, as demonstrated in [15]. Moreover, the use ofuplink resources for D2D links gives the possibility of freeingresources in the downlink that could be used for other serviceswithin the cell. We assume that either Multiple DescriptionCoding or Scalable Video Coding techniques are used for thereal time service delivery, so that also low data rates willguarantee some quality of service.

As a term of comparison, we also consider the Oppor-tunistic Multicast Solution (OMS), whereby only the subsetof nodes maximizing the data rate is served in every TTI.For a fair comparison, the same amount of radio resources isdedicated to the multicast service by the three solutions, eitherin downlink only (CMS and OMS) or in downlink and uplink(D2-CMS).

III. The Proposed D2-CMS Solution

A. Assumptions and Spectrum Efficiency Matrix computation

Let N be the number of UEs in the multicast group. LetNdl

j ⊆ N be the set of UEs that can be served by activatingthe MCS corresponding to CQI level j ∈ J in downlink. Ifj is the CQI level considered for the downlink, then the totalattainable data rate on this link depends on the cardinality ofNdl

j , on the number of assigned resources Rdl (the number ofRBs in the frequency domain), and on the achieved data rateper assigned RB. This latter parameter, named bdl

j , depends onthe CQI level j, as reported in the minimum data rate field inTable I. The number of assigned RBs Rdl can vary from 1 toR, where R is the total number of resources dedicated to themulticast service. For a fair comparison of the solutions, in theremaining part of the paper R is considered as a fixed value,while the percentage of R RBs used in downlink (i.e., Rdl) and

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uplink (i.e., Rul) can vary. Thus, in general, the total data rateobtained by activating the MCS corresponding to CQI level jin downlink is given by:

Ddlj = |Ndl

j | · Rdl · bdl

j . (1)

Being Ndlj in general only a subset of N , the remaining

set of nodes N rj = N −Ndl

j is potentially served through D2Dlinks. When D2D links are activated, some or all elements inNdl

j can act as relay nodes toward the elements in N rj . Let

us define RN j ⊆ Ndlj as the subset of nodes that receive the

data when considering CQI level j in downlink and act as relaynodes. The eNodeB will determine the set of nodes Nd2d

j ⊆ N rj

that can be served through D2D links by the nodes in RN j. Inparticular, we define DNm

j ⊆ Nd2dj as the set of nodes (one or

more) served by relay node m ∈ RN j through D2D links, RBmj

as the RBs allocated to relay node m, and bd2dj,m as the data rate

achieved with 1 RB assigned to a D2D link activated betweenthe relay node m and the served nodes in DNm

j . This lattervalue strictly depends on the CQI level on the D2D link. Sincethe objective of the proposed solution is to serve all UEs inthe system, in case of multicast communications from a relaynode, the activated CQI level is the one associated to the worstchannel conditions among the served nodes on D2D links.

To the aim of determining which of the N rj nodes can be

reached with D2D links from the relay nodes, the spectrumefficiency of a D2D link is considered, as defined in [16].Specifically, let Rmn (bit/s) be the highest achievable data rateon a D2D link connecting the m-th and n-th devices with abandwidth of BWmn (Hz). Then, the spectrum efficiency emn ofthis D2D link can be defined as emn = Rmn/BWmn (bit/s/Hz),which is theoretically equivalent to the capacity of the D2Dlink between devices m and n for unit bandwidth. For eachactivated CQI level j, a Spectrum Efficiency Matrix (SEM)can be defined that includes the emn values for all the linksbetween the Ndl

j nodes served in downlink (the matrix rows)and the remaining Nr

j nodes (the matrix columns). A SEMexample is reported in Table II. A zero in the mn cell of theSEM indicates that a D2D link cannot be activated betweennode m ∈ Ndl

j and node n ∈ N rj .

A node n ∈ N rj will be associated to the relay node m ∈ Ndl

jthat has the highest spectral efficiency emn in the SEM. In fact,especially with dense node distribution in the cell, it mighthappen that more than one node can act as a relay. If multiplepotential relays have the same efficiency values, the eNodeBchooses the one that minimizes the total number of relay nodesin the cell. Indeed, activating more relay nodes means that theavailable resources (the fixed amount of RBs R) are to beshared among more direct links, with a negative impact on theoverall system data rate. Therefore, a node n ∈ N r

j is associatedto the relay node m ∈ Ndl

j with the highest spectral efficiencyin the SEM and serving the highest number of nodes over D2Dlinks.

Once the eNodeB knows how many D2D links are goingto be activated in a given TTI, it can define how the totalavailable RBs R are shared between the downlink and uplinkD2D links. In general, a D2D link is expected to need a fewerresources compared to those needed for an eNodeB-to-relaycommunication, thanks to shorter distances and better channel

TABLE II. Spectrum EfficiencyMatrix.hhhhhhhhhhhhDL-served node

Other nodesnode 1 node 2 ... node n

node 4 e41 e42 ... e4nnode 5 e51 e52 ... e5n... ... ... ... ...node m em1 em2 ... emn

quality conditions. However, this is not necessarily true and itactually depends on the node distribution in the cell and thedownlink choices of eNodeB.

B. The D2-CMS Algorithm Step by Step

The D2-CMS scheme is described in details in Algorithm1. The main idea is to test all CQI levels to be activatedin downlink (maximum 15 for the LTE-A system). A givenCQI level j can be activated only if all nodes in the cell canbe served, either with downlink or uplink (D2D) resources.Among the candidate solutions meeting this requirement, theone maximizing the overall data rate will be chosen. Note-worthy, in the worst case when none of the tested CQI levelsmeets the requirement of serving all UEs by activating alsoD2D links, the classic CMS solution will be adopted (casej = 1 in the Algorithm).

Going into the details, the first eleven lines in Algorithm1 list how the information is collected by the eNodeB aboutthe UEs served in downlink Ndl

j , the UEs serving as relaynodes RN j, and the nodes not being served in downlink butassociated to the selected relay nodes DNm

j . In particular, therequired configuration for the implementation of the conser-vative policy on the D2D links is reported in lines 7-9. Foreach tested CQI level j ∈ J in downlink, the eNodeB verifieswhether all users can be served (possibly by activating alsoD2D links) and whether there are enough RBs to assign atleast one RB in downlink and the corresponding RBs neededto relay the data on the D2D links (line 14). In particular, thecorrespondence of one RB in downlink and the RBs allocatedto a relay node is determined by the dbdl

j /bd2dj,m e fraction. In fact,

the data per RB received by a relay node depends on bdlj , and

the number of RBs needed to relay this amount of data overthe D2D link is obtained by rounding up the bdl

j /bd2dj,m fraction.

If the two cited conditions cannot be met, then the solutionis discarded by assigning zero to the aggregate data rate (line29).

For the other cases when the conditions can be met, theeNodeB needs to determine how the total RBs R are distributedbetween the downlink and the D2D uplinks. The availableresources are allocated by following a Round Robin policy,where, initially, one RB is allocated in downlink, and then,for each of the D2D links, the needed RBs are allocated suchthat all received data can be relayed. In particular, the eNodeBchecks if each relay node m ∈ RN j needs additional RBsto relay the data received in downlink and if these RBs areavailable before allocating them, see lines 19-231.

1If the RBs are not enough to equally serve all D2D links, the relay nodesto serve first are selected based on the capability to convert an assigned RBinto a data rate; this depends on the number of UEs associated to relay nodem and the data rate per RB obtained with the CQI activated on the D2D link(determined by bd2d

j,m ).

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When all RBs are allocated, the total data rate D obtainedwith the tested solution is computed. The total data rate is givenby the sum of the data rate in downlink and the data rates overthe activated D2D links in a given TTI, see line 27. Finally, forboth the proposed schemes, the eNodeB will select the CQIlevel to be activated in downlink and the corresponding datarate D that maximizes the system data rate, see line 31.

Algorithm 1: The D2-CMS implementation.

Data: J the set of potential CQI levels to be activated, R availableRBs

Result: Total data rate D; MCS level j to activate in downlink; MCSlevels for D2D links

1 for j := min(J)→ max(J) do2 bdl

j ← data rate per RB in downlink with CQI level j3 Ndl

j ; N rj = N −Ndl

j ← UEs served/not served in downlink4 for all n ∈ N r

j do5 Select m ∈ Ndl

j as relay node if emn is the highest ∀m ∈ Ndlj

and serves the highest number of UEs6 Add node m to set RN j and node n to set DNm

j7 if emn is lowest ∀n ∈ DNm

j then8 Set bd2d

j,m according to CQI level between m and n9 end

10 end11 end12 . Start resource allocation if CQI level j ∈ J meets constraints on

serving all UEs in N with at least 1 RB13 if ((Ndl

j +∑

m∈RN j

|DNmj |) == |N|) ∧ (R ≥ 1 +

∑m∈RN j

dbdlj /b

d2dj,m e then

14 Rdl = Rul = 0; RBmj = 0, ∀m ∈ RN j

15 while (Rdl + Rul < R) do16 Rdl = Rdl + 117 for all m ∈ RN j do18 . Check if more RBs are needed for m to relay the data

and allocate if available19 if ((Rdlbdl

j > RBmj bd2d

j,m ) then

20 r =Rdlbdl

j −RBmj bd2d

j,m

bd2dj,m

21 if (R − (Rdl + Rul + r) ≥ 0) then22 RBm

j = RBmj + r; Rul = Rul + r

23 end24 end25 end26 end27 D( j) = |Ndl

j | · Rdlbdl

j +∑

m∈RN j

min[(RBmj bd2d

j,m ), (Rdlbdlj )] · |DNm

j |

28 else29 D( j) = 0 . CQI level not of interest30 end31 Activate configuration j ∈ J for which system data rate D is

maximum

IV. Performance Evaluation

A thorough simulative analysis has been conducted inMatlab where a distribution of UEs over a concentrated area(100x100 m) at the cell-edge has been considered to reflect atypical on-campus scenario. Two different scenarios have beenconsidered:

• Scenario A: the multicast group size N is set to 200while a variable number of RBs R is considered in therange [10 − 100] RBs;

• Scenario B: the number of available RBs R is set to100 RBs and the number of UEs N is in the range[20 − 200].

Besides these two scenarios an analysis is performed fora wide set of UEs distributions within the cell in sampleconfigurations of number of RBs R and number of UEs N.

The network is assumed to adopt Time Division Duplexing(TDD). Channel conditions for each UE are evaluated interms of Signal to Interference and Noise Ratio (SINR) whenpath-loss, shadowing, and multipath fading affect the signalreception [17]. The effective SINR, calculated through theExponential Effective SIR Mapping (EESM), is eventuallymapped onto the CQI level ensuring a block error rate (BLER)smaller than 10% [17]. Main simulation parameters and chan-nel information are listed in Table III. The maximum range fora D2D link connection is set to 50 m [18]. Outputs are achievedby averaging a sufficient number of simulation results to obtain95% confidence intervals.

TABLE III. Main Simulation Parameters

Parameter ValueCell radius 500 mFrame Structure Type 2 (TDD)UL/DL configuration 0Carrier Frequency 2.5 GHzeNodeB Tx power 46 dBmD2D node Tx power 20 dBmAntenna gains and patterns(Tx and Rx)

BS: 14 dBi; Device: Omni directional0 dBi

Noise power -174 dBm/HzPath loss (cell link) 128.1 + 37.6 log(d), d[km]Path loss (D2D link, NLOS) 40 log(d) + 30 log(f) + 49, d[km], f[Hz]Path loss (D2D link, LOS) 16.9 log(d) + 20 log (f/5) + 46.8, d[m],

f[GHz]Shadowing std. 10 dB (cell mode); 12 dB (D2D mode)RB size 12 sub-carriers, 0.5 msSub-carrier spacing 15 kHzBLER target 10%TTI 1 ms

In the first analysis, the focus is on Scenario A and both themean data rate achieved by multicast users and the AggregateData Rate (ADR), i.e., the sum of the individual data rates, aremeasured. The results are plotted in Fig. 2. As expected, bothmean data rate and ADR increase with the number of availableRBs for all solutions. The D2-CMS always outperforms theCMS, with a gain that increases with the number of availableRBs. In fact, the gain is equal to 44% for 10 RBs, but itreaches 69% for 100 RBs. In particular, the mean data ratefor the CMS varies between 0.25 and 2.5 Mbps, whereas forthe D2-CMS it is between 0.36 and 4.24 Mbps, as shown inFig. 2(a). When considering the OMS performance, the datarate varies between 0.75 and 7.5 Mbps, showing always thehighest values among the three solutions. The price to pay isa corresponding reduction in terms of number of served usersand fairness, as shown in the following.

A similar trend can be observed for the ADR in Fig. 2(b).In particular, the minimum ADR values are 50, 72, and 150Mbps, for CMS, D2-CMS and OMS respectively, whereas themaximum values are equal to 502, 850, and 1500 Mbps.

Focusing now on Scenario B, the impact of the multicastgroup size is highlighted in plots in Fig. 3. Also for these cases,the D2-CMS outperforms the CMS. For all the solutions themean data rate slightly decreases when the number of users inthe cell increases, Fig. 3(a). In particular, the performance ofCMS varies from 3.7 with 20 multicast users to 2.5 Mbps with

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(a) Mean data rate (b) Aggregate Data Rate

Fig. 2. Performance in Scenario A.

200 UEs, with a reduction of about 32%. When considering theD2-CMS, the mean data rate decreases from 5.7 Mbps with thesmallest group size to 4.24 Mbps with the largest group size,with a consequent 25% reduction. This is an expected resultas the more users are in the group the higher is the chance ofhaving users with very bad channel conditions. While also theOMS has a reduction in the mean data rate with more UEs,this phenomenon has less impact by decreasing from 7.9 Mbpswith 20 UEs to 7.5 Mbps with 200 UEs.

When looking at the ADR, as expected, it grows when thenumber of multicast members in the cell increases. Moreover,the gain introduced by the D2-CMS w.r.t the CMS solution islarger when the number of multicast users increases, varyingbetween 56% and 67% in the considered range. In details, theADR for the CMS varies from 73 Mbps to 502 Mbps, for theD2-CMS it varies between 114 Mbps and 842 Mbps, while forOMS the ADR varies between 157 Mbps and 1500 Mbps, forthe 20 UEs and the 200 UEs cases respectively.

(a) Mean data rate (b) Aggregate Data Rate

Fig. 3. Performance in Scenario B.

A further analysis is presented in Table IV, where someinteresting performance figures are summarized for a samplestudy case with R = 100 RBs and N = 200 UEs (similar resultsand analysis can be obtained under different sample cases). Themeasured parameters are (i) the percentage of served UEs inthe multicast group, (ii) the relationship between the numberof UEs served in downlink and on D2D links, (iii) the numberof relay nodes, (iv) the relationship between the RBs usedin downlink and in D2D links, and (v) the short-run fairnessin the data rate assignment, computed according to the Jain’sFairness Index (FI) [19]:

FI =(∑|N|

i=1 di)2

|N|(∑|N|

i=1 di2)

(2)

where di is the total data rate for UE i; FI = 1 is the maximumfairness value that is achieved when all UEs are served with

Fig. 4. Sample configuration for D2-CMS (200 UEs, 100 RBs).

the same data rate.

The values reported in Table IV clearly show that OMScannot guarantee service to all users on a short term basis.Indeed, the percentage of served UEs is equal to 60%, while,as expected, it is equal to 100% for both the CMS and theD2-CMS solutions. Looking at the number of UEs served bythe proposed D2-CMS solution on either the downlink or theD2D links, 89% (178 of the 200 UEs) of multicast users areserved through the cellular link, while the remaining UEs areserved through D2D links, by using on average 4 relays. A niceexample of service configuration when the D2-CMS solutionis implemented is plotted in Fig. 4, where the role of each UEin the group is highlighted in the reference cell-edge scenario.

A further aspect to be underlined is that the exploitationof D2D links through the proposed D2-CMS policy allowsoffloading the downlink resources compared to both CMS andOMS approaches. This is an important result as the downlinkchannel congestion is a well-known issue in cellular systems,and the resources that have not been used in downlink canbe used for additional services in the cell. In particular, theD2-CMS solution guarantees a reduction in terms of occupiedRBs in downlink of about 32%.

Finally, in Table IV it can be observed that the D2-CMS solution significantly enhances the data rate, but withoutnegatively affecting the fairness. On the contrary, although theOMS can offer even higher data rate performance, the price topay is in terms of fairness and number of served UEs.

TABLE IV. Performance Results (200 UEs, 100 RBs).

CMS D2-CMS OMSServed UEs [%] 100 100 60

Ncell/ND2D 200/0 178.33/21.67 119.62/0Nr - 4.2 -

Rdl/Rul 100/0 68.6/31.4 100/0FI 1 1 0.78

To conclude the analysis presented in this paper, we focuson a wide set of UEs distributions within the cell. To dothis, the CMS solution is used as a benchmark of minimumperformance in the tested scenarios. The area where the UEsare uniformly distributed is progressively extended from thecell-edge scenario until the whole cell is covered. In details,the average data rate gains, w.r.t. the CMS, introduced by theD2-CMS scheme are plotted in Fig. 5 for a sample value ofavailable RBs R = 100 and a variable number of UEs. Focusing

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the attention on the number of UEs in the MG (x-axis inthe plots) and the area covered within the cell MG area (they-axis in the plots reports the side length of the consideredsquare area), the gain increases with the number of users anddecreases with the area size. This is an expected behavior asthe D2D coverage range is limited to a maximum of 50 mand larger areas with the same number of UEs reduce thepossibility to exploit D2D links.

Fig. 5. Data rate gain for D2-CMS vs. CMS.

V. Conclusions

This paper introduces significant enhancements to the stan-dard CMS solution in terms of efficient delivery of multicastservices in LTE-A networks. The proposed solution is basedon the exploitation of D2D links between UEs within themulticast group. The proposed D2-CMS scheme is designedto dynamically activate D2D links in order to select the “best”relay nodes and minimizing the number of activated relays.The proposed solution outperforms the CMS solution in termsof data rate in all the considered scenarios, and the gainincreases with the number of multicast users in the cell andthe number of available RBs. Moreover, the improvements areachieved without affecting the service coverage and the fairnessamong the multicast members. The proposed D2-CMS solu-tion reduces the number of resources necessary for multicastservice delivery in the downlink direction, with a consequentdownlink channel offloading. Note that, frequency resourcesreuse on the D2D links have not been considered in this work,but these would actually allow for further improvements in theperformances. The analysis integrating these techniques will bethe focus of future work.

References[1] 3GPP, “TS 36.300, Evolved Universal Terrestrial Radio Access (E-

UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN), Rel. 11,” Tech. Rep., Sept. 2012.

[2] A. Richard, A. Dadlani, and K. Kim, “Multicast scheduling and resourceallocation algorithms for OFDMA-based systems: A survey,” IEEECommunications Surveys and Tutorials, vol. 15, no. 1, 2013.

[3] 3GPP, “Specification Group Services and System Aspects; Multime-dia Broadcast/Multicast Service (MBMS); Architecture and functionaldescription, Rel. 11,” Tech. Rep., Sept. 2012.

[4] T. P. Low, M. O. Pun, Y. W. P. Hong, and C. C. J. Kuo, “Optimizedopportunistic multicast scheduling (OMS) over wireless cellular net-works,” IEEE Trans. on Wireless Communications, vol. 9, no. 2, Sept.2009.

[5] G. Araniti, M. Condoluci, and A. Iera, “Adaptive multicast schedulingfor HSDPA networks in mobile scenarios,” IEEE BMSB, pp. 1–5, June2012.

[6] G. Araniti, M. Condoluci, L. Militano, and A. Iera, “Adaptive resourceallocation to multicast services in LTE systems,” IEEE Transactions onBroadcasting, vol. 59, no. 3, 2013.

[7] L. Militano, M. Condoluci, G. Araniti, and A. Iera, “Multicast servicedelivery solutions in LTE-Advanced systems,” IEEE InternationalConference on Communications (ICC), Budapest, Hungary, June 2013.

[8] M. Belleschi, F. Gabor, and A. Abrardo, “Performance analysis of adistributed resource allocation scheme for D2D communications,” IEEEGLOBECOM Workshops, 2011.

[9] A. Pyattaev, K. Johnsson, S. Andreev, and Y. Koucheryavy, “3GPP LTEtraffic offloading onto wifi direct,” IEEE WCNC, 2013.

[10] C.-H. Yu, K. Doppler, C.B. Ribeiro, and O. Tirkkonen, “Resourcesharing optimization for Device-to-Device communication underlayingcellular networks,” IEEE Trans. on Wireless Comm., vol. 10, Aug. 2011.

[11] A. Pyattaev, K. Johnsson, S. Andreev, and Y. Koucheryavy, “Proximity-based data offloading via network assisted device-to-device communi-cations,” IEEE VTC-Spring, 2013.

[12] J Seo, Taesoo Kwon, and V Leung, “Social groupcasting algorithm forwireless cellular multicast services,” IEEE Communications Letter, vol.17, no. 1, pp. 47–50, Jan. 2013.

[13] Y. Wang, K.I. Pedersen, T.B. Sørensen, and P.E. Mogensen, “Carrierload balancing and packet scheduling for multi-carrier systems,” IEEETrans. on Wireless Communications, vol. 9, no. 5, pp. 1780–1789, 2010.

[14] 3GPP, “TS 36.213 Evolved Universal Terrestrial Radio Access (E-UTRA): Physical layer procedures, Rel. 11,” Tech. Rep., Dec. 2012.

[15] K. Doppler, M.P. Rinne, P. Janis, C.B. Ribeiro, and K. Hugl, “Device-to-Device communications; functional prospects for LTE-Advancednetworks.,” IEEE ICC, pp. 1–6, June 2009.

[16] B. Zhou, H. Hu, S. Huang, and H. Chen, “Intra-cluster device-to-devicerelay algorithm with optimal resource utilization,” IEEE Transactionson Vehicular Technology, Jan. 2013.

[17] R. Giuliano and F. Mazzenga, “Exponential Effective SINR Approxi-mations for OFDM/OFDMA-Based Cellular System Planning,” in 17thEuropean Signal Processing Conference (EUSIPCO 2009), 2009.

[18] J. Seppala, T. Koskela, Tao Chen, and S. Hakola, “Network ControlledDevice-to-Device (D2D) and Cluster Multicast Concept for LTE andLTE-A Networks,” IEEE WCNC, 2011.

[19] R. Jain, D. Chiu, and W. Hawe, “A quantitative measure of fairnessand discrimination for resource allocation in shared systems,” tech. rep.,Digital Equipment Corporation, DEC-TR-301, 1984.