On the Impact of Frequency Selectivity on

Multicast Subgroup Formation in 4G Networks

M. Condoluci∗, G. Araniti∗, A. Molinaro∗, A. Iera∗, J. Cosmas†

∗ARTS Lab., DIIES Dep., University Mediterranea of Reggio Calabria, Italy

e-mail: {massimo.condoluci, araniti, antonella.molinaro, antonio.iera}@unirc.it.†WNCC, Brunel University, London, UK

e-mail: john.cosmas@brunel.ac.uk.

Abstract—This paper deals with the transmission of group-oriented services, considered as the main value-added for thefuture 4G broadband wireless systems. In this scenario, the designof proper resource allocation strategies is a key issue in orderto offer high quality services to involved users and to efficientlyexploit the available radio resources. In this paper we extendthe works existing in literature by evaluating the impact offrequency selectivity on the performance in terms of spectralefficiency achieved when group-oriented services are providedin Long Term Evolution (LTE) and LTE-Advanced (LTE-A)systems. We conducted exhaustive simulation campaigns in orderto define the scenarios that could benefit of the exploitationof frequency selectivity in multicast resource allocation. Weinvestigated single- and multi-group scenarios with differentapplication profiles, such as real time video flows, and weconsidered several deployment cases addressing different channelbandwidth and user configurations.

Index Terms—Networking and QoS, Traffic and performancemonitoring, Multicast, LTE, LTE-A, Frequency Selectivity, IPTV.

I. INTRODUCTION

LONG Term Evolution (LTE) and LTE-Advanced (LTE-A)

[1] represent the emerging wireless technologies leading

the growth of mobile broadband services in the next years [2]

[3]. They offer several benefits in terms of high data rates,

low latency and low cost per bit and exploit the Multimedia

Broadcast Multicast Service (MBMS) [4] in order to support

the transmission of group-oriented services (such as broadcast

or multicast), expected as the value-added int the 4G systems.

Such services (e.g., IPTV, audio and/or video streaming, news

forecast) are characterized by strict constraints in terms of

Quality of Service (QoS) [5] and the design of a proper

strategy for the management of MBMS services is still an

open issue investigated in several research works. Indeed,

the performance of a conventional multicast approach [6],

that foresees to serve the whole set of MBMS destinations

according to the user with the poorest channel conditions, is

strongly influenced by the presence of cell-edge users which

limit the quality experienced by all multicast receivers. With

this aim, in [7] we proposed a subgrouping approach for the

This full text paper was peer reviewed at the direction of IEEE Commu-nications Society subject matter experts for publication in the proceedings of2013 IEEE International Symposium on Broadband Multimedia Systems andBroadcasting (BMSB 2013)

DOI: 10.1109/BMSB.2013.6621752

management of multicast services in cellular systems. It ex-

ploits the multi-user diversity by splitting the MBMS members

into different subgroups according to the experienced channel

conditions. Obtained results demonstrated the effectiveness

of the proposed strategy in outperforming the conventional

multicast scheme and in guaranteeing improved session quality

in High Speed Packet Access (HSPA) [7] [8], Worldwide

Interoperability for Microwave Access (WiMAX) [9] and LTE

[10] networks.

In this paper we extend the work in [10] by considering

the impact of frequency selectivity on multicast subgroup

formation in LTE and LTE-A networks. Indeed, thanks to

the use of Orthogonal Frequency Division Multiple Access

(OFDMA) which introduces the scheduling in both time and

frequency domains, the Radio Resource Management (RRM)

of 4G systems could take advantage of the multi-user diversity

by assigning a frequency resource to the user with the highest

channel gain over such resource. Frequency selectivity is

considered as a key feature in order to achieve high data

rates, high capacity, and a better exploitation of the available

spectrum, but it introduces several issues in terms of control

traffic overhead and complexity of RRM strategies in multicast

environments. In this scenario, we aim at contributing to the

advance of the state-of-the-art on this topic by addressing when

and how much the frequency selectivity exploitation improves

the system performance, in order to define the most interesting

scenarios to be investigated in future research works.

The remainder of this paper is organized as follows. Section

II provides an overview on the multicast service provisioning

in LTE and LTE-A systems. In Section III, the addressed

resource allocation problem is analyzed, whereas Section IV

provides the results achieved by simulation campaigns. Finally,

conclusion and future works are summarized in Section V.

II. MULTICAST SERVICES OVER LTE

The LTE system [1] represents the most promising cellular

system able to support the growing demand of high-quality

services over mobile terminals [11] [12]. LTE is characterized

by a flat all-IP network architecture which allows to reduce

the latency in both control and data planes. The main entity in

the radio access network is the eNodeB, the LTE base station,

which forwards control and data traffic towards the mobile

users and performs link adaptation procedures. The most im-

TABLE ILTE AND LTE-A PERFORMANCE TARGETS

LTE LTE-A

Peak data rate DL 300 1000[Mbps] UL 75 500

Peak spectrum efficiency DL 15 30[bps/Hz] UL 3.75 15

Cell-edge user throughput DL 1.87 2.6[bps/Hz/cell/user] UL 0.024 0.04

portant entity in the control plane is the Mobility Management

Entity (MME), located in the core network, which is in charge

for authentication, security, and mobility management. The ex-

ploitation of OFDMA and Single Carrier Frequency Division

Multiple Access (SC-FDMA), with the joint use of Multiple

Input Multiple Output (MIMO) techniques, guarantees higher

data rates and spectral efficiency compared to previous cellular

systems. The 3rd Generation Partnership Project (3GPP) is

also working on the LTE-Advanced (LTE-A) system, i.e., the

extension of the LTE towards the real 4G wireless systems.

By exploiting Carrier Aggregation (CA) techniques [1], LTE-

A can guarantee a channel bandwidth up to 100 MHz while

assuring and backward compatibility for LTE devices. The

main performance targets of LTE and LTE-A systems are listed

in Table I.

Fig. 1. General Architecture for E-UTRAN MBMS

The network architecture of LTE is designed in order to

couple with the MBMS standard [4], which defines both radio

access [1] and core network functional entities (Fig. 1). The

Broadcast-Multicast Service Centre (BM-SC) represents the

entry point for content provider transmissions and is in charge

for service provisioning (e.g., service announcement, member-

ship function), security operations and header compression.

The MBMS-GW is a logical entity (i.e., it may be a stand

alone device or co-located with other network nodes such as

the BM-SC) which forwards data packets to the eNodeBs

involved in the MBMS session by exploiting IP multicast

transmissions via the M1 interface.1 The Multi-cell/multicast

Coordination Entity (MCE) is a logical entity2 which selects

1The allocation of IP multicast address for the eNodeBs joined the MBMSdata session is provided via the MME.

2[1] also allows MCE to be deployed within the eNodeB.

the transmission mode (i.e., single- or multi-cell) in the setup

(or reconfiguration) phase of the MBMS Service. The MCE

also performs MBMS session suspension/resumption. The

MCE exploits two interfaces: (i) the M2 interface is used for

MBMS session management and radio configuration signaling;

(ii) the M3 interface connects the MME and the MCE for

conveying MBMS session management signaling.

In the radio access network, the MBMS sessions are con-

veyed to involved users through point-to-multipoint transmis-

sions, managed by the packet scheduler which is implemented

at the Medium Access Control (MAC) of the eNodeB. It

performs the RRM by efficiently handling the resource allo-

cation in the time and frequency domains. In details, the Time

Domain Packet Scheduler (TDPS) selects the flows which

must be served during every TTI (lasting 1 ms), according

to the their QoS constraints. The Frequency Domain Packet

Scheduler (FDPS) is in charge of performing the link adapta-

tion by assigning a modulation and coding scheme (MCS) at

each assigned resource. The available resources are managed

in terms of Resource Blocks (RBs). Each RB consists of 12

adjacent sub-carries and lasts 0.5 ms. The link adaptation

procedure is based on the Channel Quality Indicator (CQI)

feedback transmitted by the users at the eNodeB. The CQI

indicates the maximum supported MCS value according to

channel conditions experienced by the mobile terminal. Table

II lists the CQI-MCS mapping defined in the LTE system.

TABLE IICQI-MCS MAPPING

CQI Modulation Code rate Spectral Efficiencyindex Scheme [bit/s/Hz]

1 QPSK 0.076 0.15232 QPSK 0.120 0.23443 QPSK 0.190 0.37704 QPSK 0.300 0.60165 QPSK 0.440 0.87706 QPSK 0.590 1.17587 16-QAM 0.370 1.47668 16-QAM 0.480 1.91419 16-QAM 0.600 2.4063

10 64-QAM 0.450 2.730511 64-QAM 0.550 3.322312 64-QAM 0.650 3.902313 64-QAM 0.750 4.523414 64-QAM 0.850 5.115215 64-QAM 0.930 5.5547

III. RESOURCE ALLOCATION IN MULTICAST

ENVIRONMENTS

Several approaches have been developed for multicast data

delivery in OFDMA-based systems. The conventional ap-

proach [6], aimed at achieving the highest fairness among

multicast destinations, adapts the transmission parameters (i.e.,

MCS) according to the user which experiences the poorest

channel condition. As a consequence, this approach is not

efficient in terms of spectral efficiency (i.e., the number of

transmitted bits per Hertz).

Another strategy proposed in literature, based on the multi-

rate approach, is the subgrouping [7]. In order to reduce

the bottleneck effects of cell-edge users, it foresees to split

the multicast members into different subgroups, each one

including users with similar channel conditions. The main

goals of the subgrouping technique are: (i) high robustness to

user mobility [8]; (ii) exploitation of Scalable Video Coding

(SVC) [13] techniques which foresee to split the original

video stream into a base layer and multiple enhancement

layers [9]; (iii) multicast gain maximization, i.e., all the users

are served within every scheduling frame and no additional

data coding (such as rateless code) is needed and short-term

fairness can be guaranteed [10]. The results achieved in [7]-

[10] demonstrated that subgrouping can meaningfully improve

the quality experienced by the multicast users compared to a

conservative approach and other approaches in literature.

Another key feature for achieving high spectral efficiency

performance is the exploitation of the frequency selectivity

[14] in resource allocation procedures. Although this technique

is expected to guarantee meaningful improvements, frequency

selectivity in multicast scenarios must be exploited on a per-

group basis. This involves two issues: (i) a larger amount

of uplink control traffic is required for the transmission of

eNodeB-configured subband CQI feedbacks compared to the

Full Bandwidth CQI case; (ii) the scheduling policy complex-

ity, which depends on the number of multicast group members

and on the available resources, is further increased by a factor

equal to the number of channel quality levels in the system. In

details, the computational cost is equal to (K+1)N (M+1)N

for a single-group scenario [14], where K is the multicast

group size, M is the overall number of MCS levels defined

in the system and N is the number of available resources.3

The above mentioned issues cannot be considered negligible

in multicast environments, where the number of involved users

is typically high.

The goal of this paper is to define the scenarios where the

exploitation of frequency selectivity has the highest impact on

the system performance. With this aim, the subgrouping algo-

rithm proposed in [10] has been extended according to [14] in

order to exploit frequency selectivity in subgroup formation.

This novel policy works as follows: among all the admissible

subgroups to be potentially enabled, the RRM selects the one

which guarantees the highest increase in the system spectral

efficiency by selecting, for a given subgroup, the best RBs

according to the channel quality of the users belonging the

subgroup. We conducted an exhaustive simulation campaign

where we considered both LTE and LTE-A systems. We

investigated several scenarios present in literature (e.g., single-

and multi-group scenarios, scalable video services) under

different deployment cases (number of resources available for

multicast services, multicast group size). Section IV focuses

on the system setting and the achieved results of the addressed

performance analyses.

3Authors in [14] propose an effective near-optimal policy characterized bya complexity equal to O(MN).

IV. PERFORMANCE ANALYSIS

A. System setting

The performance analysis has been conducted in accordance

with the guidelines defined in [15]. The channel quality of

each multicast member is evaluated in terms of Signal to

Interference and Noise Ratio (SINR) experienced over each

sub-carrier [16]:

SINRi =P0 × PL0 × h0

∑NBS

j=1(Pj × PLj × hj) +No

(1)

where Pj , PLj and hj are the transmission power, the path

loss, and the small scale fast fading of the link between the

user and the j-th base station (j = 0 indicates the serving base

station, j > 0 the interfering ones); No is the noise power.

Once the SINR value for all the sub-carriers is collected, the

effective SINR is obtained through the Exponential Effective

SIR Mapping (EESM):

SINReff = −β ln

(

1

Nsub

Nsub∑

i=1

e−SINRi

β

)

(2)

being Nsub the overall number of sub-carriers and β a scaling

factor used to adjust the mismatch between the actual and

the predicted block error rate (BLER). Finally, the effective

SINR is mapped onto the CQI level related to the MCS which

ensures a BLER smaller than 10% [16]. More details on the

LTE system settings are listed in Table III. The parameters

for modeling the real-time SVC streams, set in accordance to

[17], are listed in Table IV.

TABLE IIIMAIN SIMULATION ASSUMPTION

Parameters Value

Cell layout 3GPP Macro-cell case #1Cell radius 1 kmDistance attenuation 128.1+37.6*log(d), d [km]Shadow fading Log-normal,0 mean, σ = 8 [dB]Fast Fading ITU-R PedB (extended for OFDM)Scheduling frame 10 msTTI 1 msCarrier frequency 2 GHzeNodeB transmit power 20 W, 13 dBMaximum antenna gain 11.5 dBThermal Noise -100 dBm

TABLE IVDATA RATE SETTINGS FOR SVC STREAMS

BL E1 E2 E3[kbps] [kbps] [kbps] [kbps]

CREW 306 578 814 1184FOOTBALL 442 827 1114 1621MOBILE 189 322 442 649CITY 448 923 1288 1943FOREMAN 170 407 589 890BUS 185 390 567 857HARDBOUR 577 1025 1379 1929NEWS 121 259 372 564SOCCER 385 795 1095 1651ICE 277 548 767 1123

In our simulations we compare the spectral efficiency gain

of a resource allocation scheme based on eNodeB-configured

sub-band CQI feedbacks scheme with that achieved when

the Full Bandwidth CQI mode is exploited. According to

the latter scheme, a user equipment calculates a single CQI

value for the whole available spectrum, and this feedback is

transmitted to the eNodeB. Hence frequency selectivity cannot

be exploited as the FPDS has not information on which part

the available spectrum is more suitable for the transmission

towards a given user. According to the eNodeB-configured

scheme, a terminal estimates a CQI value for each sub-band:

a sub-band is composed of k consecutive RBs, where k is

function of the channel bandwidth (refer to Table V).

TABLE VSUB-BAND SIZE FOR ENODEB-CONFIGURED MODE

Channel Bandwidth Subband size(RBs) (k RBs)

6-7 (Wideband CQI only)8-10 411-26 427-63 664-110 8

Each simulation run has been repeated several times to get

95% confidence intervals. The members of each multicast

group are randomly distributed in a concentrated area, as

depicted in Fig. 2. In the considered scenarios, we varied both

the number of multicast members and the channel bandwidth

available for MBMS services.

Fig. 2. User distribution within the cell.

B. Simulation results

1) Single-group LTE Scenario: In this simulation campaign

we consider the scenario in [10], where a single multicast

group served by the base station. No constraints in terms of

number of subgroups to enable and subgroup data rate are

considered, and the whole set of available RBs is exploited in

the resource allocation.

Fig. 3 shows the spectral efficiency gain of the frequency

selectivity exploitation as a function of the number of RBs

and MBMS users. In details, the spectral efficiency, measured

in bps/Hz, is the ratio between the number of bits successfully

Fig. 3. Spectral efficiency gain in the single-group LTE Scenario.

received by multicast users and the bandwidth exploited for

data delivery. We varied the number of multicast members

from 10 to 100, and the available bandwidth from 6 RBs (i.e.,

1.4 MHz) to 100 RBs (i.e., 20 MHz).

The achieved results demonstrate that the subgrouping tech-

nique natively improves the spectral efficiency. Indeed, no

meaningful gain is introduced by the frequency selectivity

exploitation. In details, the highest gain is about 5% and is

only obtained when the number of users in the group is very

low, namely 10, and the number of RBs is high, namely 100. In

other cases, the introduced gain is even negligible. Moreover, it

is worth noting that when the number of users is higher than

20, no spectral efficiency gain is achieved also when large

number of resources are available for the MBMS service.

2) Multi-group LTE Scenario: In this simulation campaign

we focus on the impact of frequency selectivity in practical

scenarios. With this aim, we conducted further analysis by

addressing multicast scalable video coding (SVC) sessions

transmitted by the base station. Two main enhancements have

been introduced with respect to the previous analysis: (i) due

to the presence of video application, each enabled subgroup

has to achieve a target data rate, i.e., the data rate of the layer

related to such a subgroup; (ii) several multicast flows are

simultaneously served by the base station.

Fig. 4. Spectral efficiency gain in the multi-group LTE scenario.

Fig. 4 shows the spectral efficiency gain for the LTE

system when the base station transmits four scalable video

streams, each one related to a different multicast group.4 The

considered video streams are CREW, FOOTBALL, MOBILE

and CITY (refer to Table IV).

The gain introduced by the frequency selectivity exploita-

tion in this scenario is higher compared to the previous one,

and varies from 5% to 23%. It is worth underlining that,

in a scenario with scalable video streams, the lower spectral

efficiency gain is equal to the highest gain in the scenario

addressed in Fig. 3. However, a gain higher than 20% is

obtained when the multicast groups are composed of few users

(namely 10) and a high number of RBs (from 50 to 100) are

available in the system for serving the four video sessions.

3) LTE-A Scenario: The last analysis we conducted focus

on the LTE-A system, where we considered the aggregation

of five Component Carriers (CCs) [1].5

We consider two cases: (i) a single-group scenario, as

proposed in [18], depicted in Fig. 5(a); (ii) a multi-group

scenario, with the transmission of four video sessions, shown

in Fig. 5(b).

(a) Single-group

(b) Multi-group

Fig. 5. Spectral efficiency gain in the LTE-A scenario.

4The x-axis in Fig. 4 represents the number of users per multicast group.5In Fig. 5, the y-axis refers to the number of RBs available over each CC.

The larger channel bandwidth (up to 100 MHz) of LTE-

A networks, allows to better exploit the multi-user diversity

in multicast resource management, and, as a consequence, to

further improve the spectral efficiency performance. Indeed,

high spectral efficiency gain, varying from 15% to 20% for

the single-group scenario and from 15% to 25% for the multi-

group scenario, is guaranteed for all the considered RBs values

when the multicast group size is low (10 and 20 users).

Moreover, in this scenario a spectral efficiency gain higher than

5% is guaranteed also when a high number of users (namely

100) join the multicast group. In this case, the gain introduced

increases with the number of RBs, up to a maximum value

equal to 8%, in both the single- and multi-group case.

Fig. 6. Comparison of the average Spectral efficiency gain.

Finally, we further analyzed the average spectral efficiency

gain by varying the number of multicast flows in the cell.

We varied the number of multicast video streams (modelled

according to the parameters in Table IV) from 1 to 10 and we

considered the mean spectral efficiency in both the LTE and

the LTE-A systems. The achieved results are depicted in Fig.

6. As expected, the frequency selectivity exploitation in LTE-

A systems allows to efficiently support up to eight multicast

groups, with a gain equal to 11.6%. In LTE system, instead,

the maximum average gain is equal to 9.2% and it is achieved

when the number of multicast groups is equal to five.

V. CONCLUSIONS

In this paper we focused on the impact of frequency

selectivity on multicast subgroup formation in LTE and LTE-

A networks. We considered the gain in terms of spectral

efficiency achieved when the resource allocation is performed

in order to exploit the frequency selectivity. We addressed

two scenarios investigated in literature: a single-group scenario

with no constraints in terms of number of subgroups to enable

and subgroup data rates; (ii) a multi-group scenario where real-

time video services are transmitted by the base station, hence

with constraints on the data rate values for each subgroup.

The achieved results demonstrate that the frequency selec-

tivity exploitation can introduce effective spectral efficiency

gain in multicast environments when practical applications,

such as SVC, are considered, and the gain is more evident in

LTE-A systems. The obtained results motivate future studies

related to the design of RRM strategies that can efficiently

manage the transmission of multi-layer video services in LTE

and LTE-A systems, in order to further improve the spectral

efficiency in multicast service provisioning or to consider

other aspects, such as computational burden, fairness, etc., not

considered in this paper.

ACKNOWLEDGEMENT

The research of Massimo Condoluci is supported by Eu-

ropean Union, European Social Fund and Calabria Regional

Government. This paper reflects the views only of the authors,

and the EU, and the Calabria Regional Government cannot

be held responsible for any use which may be made of the

information contained therein.

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