Cooperative Radio Resources Allocation in LTE_A Networks within

MIH Framework: A Scheme and Simulation Analysis

Mzoughi Houda

, Faouzi Zarai

, Mohammad S. Obaidat

, Balqies Sadoun

and Lotfi Kamoun

LETI Laboratory, University of Sfax, Sfax, Tunisia

Department of Computer and Information Science, Fordham University, New York 10458, U.S.A.

College of Engineering, Al-Balqa Applied University, As-Salt, Jordan

Keywords: LTE-Advanced, Radio Resource Allocation, MIH, LTE-a System Level Simulator.

Abstract: Heterogeneity and convergence are two distinctive features for new generation networks like the Long Term

Evolution-Advanced (LTE-A) system. LTE-A is now being deployed and is the way forward for high speed

cellular services. LTE-A enhancements the four areas of capacity, coverage, inter-cells coordination, and

cost. Improvements in these areas are based on using several technologies. Multiple-Input Multiple Output

along with Orthogonal Frequency Division Multiple Access (MIMO/OFDMA) are two of the base

technologies that are enablers. In addition, self-organizing and optimization (SON) technologies have been

also developed to enable automatic configuration, optimization of network operations, including the 802.21

Media Independent Handover protocol (MIH), which is designed to optimize the vertical handover process.

In this paper, we show the importance of inter-technologies and inter-entities cooperation, which can exploit

heterogeneity as an enabler to improve the system capacity as well as the quality of service (QoS) for users.

We present a new cooperative radio resource allocation scheme for LTE-A network to coordinate better the

utilization of network’s available radio resources. We adopted the MIH framework, in order to facilitate the

exchange between heterogeneous network entities to insure self-configuration of radio resource

management parameters. We worked on allocating the right PRB to the right user at the right time. We also

analyze some existing solutions and evaluate our proposed scheme using simulation analysis. Simulation

results illustrate the performance gains brought by the proposed optimization, especially for average

throughput of macro-cell users comparing to their initial performance within two-tier LTE-A network.

1 INTRODUCTION

Next generation of wireless mobile communication

systems, provides to end users several amount of

multimedia services. Many of these services are too

expensive in terms of resources. In order to deal

with this explosive of resources’ demands, it is

essential to have a huge network capacity. In order

to fulfill such requirement, some technologies are

chosen to be deployed for the next generation of

wireless network, like the LTE-Advanced (ETSI TS

36.300 v10.11.0; Akyildiz et al., 2010) system, such

as MIMO, OFDMA, Beamforming, small cells

enhancements, macro cells enhancements, HetNets,

among others. Coming along with their benefits,

these new technologies introduce new challenges in

radio resource management (RRM) process, which

is the key issue to insure efficient exploitation of the

available radio resources including especially

interference management and resource allocation

(Dehghani et al., 2015). In this paper, we deal with

radio resource allocation in downlink in case of the

LTE_A systems.

Regarding the literature, we find out several

interesting works investigating the problem of radio

resource allocation in OFDMA networks, some of

which are reviewed below. Some works investigated

resource allocation based on an optimization theory

approach (Dehghani et al., 2015; Papathanasiou,

2013; Alavi et al., 2013; Tang et al., 2015) and

others formulate the problem based on game theory

approach about which a survey is offered in

Akkarajitsakul, 2011. In Dehghani et al., 2015, a

radio resources allocation scheme for

MIMO/OFDMA system with the employment of

beamforming technique is suggested. In this work,

users are classified into two groups: interior and

exterior users in the cell. Interior users are those

where the interference term satisfies that the sum of

received signal in all beams is considerably

smaller than the Gaussian noise; all others are

138

Houda, M., Zarai, F., Obaidat, M., Sadoun, B. and Kamoun, L.

Cooperative Radio Resources Allocation in LTE_A Networks within MIH Framework: A Scheme and Simulation Analysis.

DOI: 10.5220/0006041801380145

In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2016), pages 138-145

ISBN: 978-989-758-199-1

considered as exterior users. Then the total number

of PRB is divided onto Q groups (GPRB) with an

equal size. For exterior users, all GPRB are sorted in

decreasing order of power. Next, the set of GPRBs

that will be allocated to exterior users, which is

calculated according to their number in the cell, are

those with the minimum power for all eNodeBs. The

rest of the GPRBs will be automatically allocated to

interior users. In Papathanasiou, 2013, authors

considered imperfect channel state information

(CSI) in the transmitter, which means that CSI is

estimated at the receiver then it is fed back to the

transmitter, and they take into account bit error rate,

rate requirement and delay requirement as QoS

constraints. They proposed a heuristic approach

including three steps. In the first one, the resource

allocation unit decides the number of subcarriers

needed by each user according to the QoS

requirements. Secondly, subcarriers are assigned to

users according to the corresponding power

allocation based on the outcomes of the first step and

power constraint. In the third step, they suggested

the reallocation of some subcarriers according to

user’s satisfaction.

The remainder of this paper is organized as

follows. We give an overview of the present

contribution in section 2. Then in section 3, we

describe the system model used in our study. The

proposed resources allocation scheme is discussed in

section 6 as well as the problem formulation. In

section 5 the simulation results are presented and

analyzed. Finally, section 6 concludes the paper.

2 MAIN CONTRIBUTION

In this paper, we attempt to present a dynamic and

cooperative solution to the problem in order to

satisfy the LTE-A network features. We considered

both network and mobile users constraints. As

presented in Figure 1, in the first step we classify

users to edge and central users, in both cases users

will be collected to M-UE teams according to their

localization, velocity and QoS requirements. On the

other side, PRBs are assigned by user’s M-UE team.

We estimate the required number of PRBs by each

M-UE team. Then the total available band will be

divided into sub-bands of the required estimated

value and one of them will be assigned to the

corresponding M-UE team of users. The sub-band

selection is based on the resolution of optimization

problem to maximize the capacity provided by the

sub-band for

the

M-UE team of users. We also study

the case when the M-UE team requirement risks

overloading the cell, in such case the cell will

collaborate with neighbors one to serve the group of

users.

Figure 1: Contribution overview.

When developing the proposed approach, we

focus on the selection of the best action by the radio

resources allocation scheme to assign the right sub-

band to the right M-UE team of users at the right

time. We also integrate the congestion control aspect

in order to maintain system stability and users

required QoS. In fact, when the cell becomes close

to overload state, the eNodeB selects a neighbor cell

or one of the existing RAT to collaborate with based

on information collected about different existed

technologies; thanks to MIH deployment (IEEE Std

for Local and metropolitan area networks Part 21,

2008).

3 SYSTEM MODEL

In this work, we investigate radio resource allocation

for multi-cell downlink OFDMA communication

scenario in LTE-A systems. With the OFDM

scheme, the total band is equally divided into P

physical resource blocks (PRBs) and each user can

allocate an integer number of PRBs according to its

requirements. Consider MIMO as key technology of

LTE-A, where each eNodeB is equipped with Me

transmit antennas and users devices are equipped

with M

receive antennas. Each eNodeB transmits a

single data stream to each user with zero forcing

beamforming. We mean by M

t ,

the total number of

transmit antennas which is equal to

∑

where E denotes the total number of eNodeB. Figure

2 illustrates the downlink coordinated beamforming

Cooperative Radio Resources Allocation in LTE_A Networks within MIH Framework: A Scheme and Simulation Analysis

139

Figure 2: Illustration of downlink coordinated

beamforming.

transmission process. Thus, the received signal

at user k in the set of K user scheduled in PRB p is

modeled in equation (1) below.

11,j

,,, ,

()

pp p pp p

ijk

ji ji

ki ki ki ki k

YZ HWS HWSN

==≠

=+ +

∑∑

∑

(1)

Where,

∈

is the channel matrix between

user k and eNodeB i at PRB p.

∈

is the data

vector transmitted by the eNodeB i for user j

employing the beamforming vector

∈

with

()

SS =Ε

⎡⎤

⎣⎦

is the additive white Gaussian

noise with zero mean and covariance matrix σ

Furthermore

denotes the received combining

vector employed at user k. So, the SINR for user k

connected to eNodeB e at PRB p is modeled as

shown below:

,, ,,i

111

kke

EKE

pp pp

ki ki ki j

iji

ie jk

ZH W

SINR

ZH W ZH W Z

===

≠≠

++σ

∑∑∑

(2)

Moreover, the interference expression in (2)

includes the interference introduced from other

eNodeBs as well as inter-user interference.

However, with Joint Transmission (Bjornson et al.,

2011), interference between macro and small base

station can be neglected; thanks to the coordination

between all of them when serving all covered users.

In practice, the uncertainty of CSI makes the

cancellation of inter-users interference an impossible

task. The zero forcing beamforming strategy can

give inter-users interference relaxation by being

limited to some threshold value γ>0 instead of being

cancelled (Lee et al., 2011), which means that:

,,i

kkij

ZH W

≠

≤

∑∑

(3)

According to the description above, the SINR at user

k can be formulated as:

,,i

kke

kkk

ki j

kke

ZH W

SINR

ZH W Z

ZH W

≠

+σ

≥

+σ

∑∑

(4)

The achieved data rate by user k in its served cell e

for one PRB p is given by:

2,,

log (1 )

ke ke

RSINR

∑

(5)

Where N

denotes the number of allocated PRB to

user k in by eNodeB e. And the total system capacity

is modeled as:

Tke

∑

(6)

4 RADIO RESOURCES

ALLOCATION

To meet expectations of 5G telecommunication

systems, an efficient resource allocation scheme is

needed, that should be able to provide a capable real

time solution. In this paper, we look to find a

solution of the problem, while maximizing system

capacity and maintaining end users required QoS.

As mentioned above we focus on LTE-A RAT, but

it could be extended to other RATs. Our approach

includes four steps. In the first step, we focus on

active users classified into central and edge users.

For both cases we collect users into M-UE teams to

form what we called M-UE teams, such idea can

replace the deployment of small cells in order to

cancel inter small cell interference. In addition, it

aims to resolve problems with edge users like

interference and Ping-Pong effect. The result of step

2 gives the selected group of PRBs that will be

allocated to each M-UE team of user, based on CQI

SIMULTECH 2016 - 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

140

indicator. In this step, we also propose a linear

problem that maximizes the system capacity under

QoS constraints. In next step, we aim to prevent a

congestion state in the cell. Hence, we propose to

collaborate overloaded cells with neighbor ones that

are under-loaded and can serve some M-UE teams

of users. Finally, in the last step we track the

allocation to check whether the M-UE teams

requirements are satisfied or not, or if there is an

over served M-UE teams and both cases need a

reallocation to improve the efficiency; we consider

also new coming call. Next, we describe details of

each of these steps.

Step 1:

In the macro cell, users are classified to edge and

central one. Then, they are collected into groups to

form M-UE teams according to three metric:

localization, user’s application and user’s velocity.

The role of M-UE team leader can be assigned to

one UE at the same time according to an agreement

with the operator. However, this role is not limited

to a single user; another one can fill it when

principal leader is down, in order to maintain the M-

UE team. The number of mobile users by each M-

UE team is fixed taking into consideration the

capacities of the leader equipment.

Algorithm 1: M-UE Teams Conception.

Initialization

L: List of active users in the sector/cell

B: Number of M-UE teams, B=0

: Velocity of user k

: Available data rate

: Required rate by user k

while Stop=false

if length (L) >1

a. Select a leader l, L= L/{l}, B = B +1

b. Define the max number of user in the M-UE

team N

max

while i< N

max

and k≤ length (L)

if | V

–V

|< threshold

Add the mobile terminal to the M-

UE team.

L= L/{k}

i=i+1

end

k=k+1

end

if i< N

max

Stop=true

end

else

Stop=true

end

for b=1 to B

for k=1to N

#N is the number of user in the team

if | V

–V

|> threshold

So this user will be removed from the

M-UE team

end

while i< N

max

and k≤ length (L)

if | V

–V

|< threshold

if RR

< R

Add the mobile terminal to the M-

UE team.

= R

- RR

L= L/{k}

i=i+1

end

k=k+1

end

Liberate unused resources

end

The radio resources allocation for each M-UE team

of users will be communicated with the eNodeB by

the team leader only, which will decrease

signalization traffic in the cell.

Step 2:

In the network side, we work on the selection of the

best radio resources to be allocated to each M-UE

team of users. Therefore, the first issue is the

estimation of the number of required PRBs by all

users in the M-UE team, according to the required

QoS for each user in the team, which depends on the

type of trafﬁc. In our work, we considered both real-

time and non-real-time trafﬁc (Rysavy). For users

with real-time applications, ﬁxed rate are required

and for those with non-real-time services only

minimum rate requirements are demanded.

The required number of PRBs for user i is

calculated as follow:

PRB

⎢

⎥

⎢

⎥

⎣

⎦

(7)

With

⎣⎦

denotes the floor of the fraction, RRi

denotes the required rate of user i and R

PRB

denotes

the peak capacity of one PRB. Assume 64QAM

modulation without coding, over 2 time slot (1ms)

a single PRB has 12 subcarriers and 14 symbols, or

Cooperative Radio Resources Allocation in LTE_A Networks within MIH Framework: A Scheme and Simulation Analysis

141

12 x 14 = 168 resource elements (REs). Some of

those REs are occupied by the PDCCH and the

downlink reference signals, leaving about 120 REs

per PRB to carry data on the downlink. And with

64QAM each RE holds 6 data bits, so the maximum

data rate delivered by one PRB is equal to:

720 /

PRB t

RM Kbs=×

(8)

Then the total number of required PRBs of the M-

UE team k is equal to:

Nn=

∑

(9)

Next, the available band will be divided into sub-

bands each one with a number of successive PRBs

equal to the estimated number of required PRBs.

Our goal is to maximize the cell capacity.

Mathematically, we can present this maximization

problem as:

{} {}

{}

,,i

1.. E 1.. K

1.. K

1..

maximize

C : ,

C :

C: C

kkij

NRT

threshold

ki RT

NRT

ki NRT

ZH W j

subject to

≠

∈

≤∀∈

∀

≥

≤

∑∑

(10)

Algorithm 2: PRB Allocation.

Result: Obtain the group of PRBs G

to be

allocated to each M-UE team b.

Input: Available bandwidth

Input: User’s M-UE teams list with leader

localization and QoS requirements for each M-UE

team.

For each M-UE team b

1. Define Set of users with real time services

Define Set of users with non real time services

2. Initialize G

3. Resolve the problem (10)

End for

Update the available bandwidth to B= B- G

If B <=threshold

Execute the MIH collaborated cell

B= available bandwidth in the new collaborated cell

End

The selected GPRB will be allocated to the

correspondent M-UE team of users. In order to

maximize system throughput, we adopt downlink

beamforming vector for each GPRB (Alavi et al.,

2013) and also for each M-UE team of users.

Step 3:

This step is executed by the MIIS server, which aims

to form a group of cooperative heterogeneous cells

to extend the available capacity. Heterogeneity here

describes not only macro or small cells, but also

different radio access technologies deployed by the

operator in the area, which explain our choice of

using MIH technology (IEEE Std for Local and

metropolitan area networks Part 21, 2008) to ensure

a simple communication between heterogeneous

network equipment. The selection of a collaborative

cell or collaborative RAT is mainly based on the

load of each. This step is executed when one of the

LTE cell risks depleting its available resources,

which can lead to a congested cell. In order to

prevent such scenario, the MIIS is charged to collect

information about all neighbors of the current cell in

a limited area. Then, it communicates the list of

candidates to the eNodeBs. If the eNodeBs find

LTE-A cell among the candidates list, it will be

selected automatically to establish a collaboration

with through direct communication between

eNodeBs via X2 interface. Otherwise, one of the

least loaded RAT takes the place, and the two

stations will exchange direct messages; thanks to the

MIHF sub layer. We called the selected cell/RAT

the “grandmother cell/RAT”, because all new

connections in addition to some M-UE teams, if

needed, will be served by the “grandmother

cell/RAT” via the intermediate of the mother cell

which is considered like a remote node when serving

new calls/or handover. As soon as possible, the

mother eNodeBs interrupt the connection with the

“grandmother cell/RAT” and continue by itself to

serving all connected users.

Following, is the detailed algorithm for the inter-

cell/RAT collaboration establishment executed by

the eNodeBs.

Algorithm 3: MIH-Cooperation management.

: Current eNodeB

: Available capacity in the current LTE-A cell

MIH-RR-Coop: list of cooperative cells/RATs

MIH-RR-Coop

: Total available capacity of the

cooperative

group of cells/RATs

Initialization:

MIH-RR-Coop ={e

};

MIH-RR-Coop

ec;

Stop=false;

SIMULTECH 2016 - 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

142

While C

MIH-RR-Coop

< threshold and Stop=false

♦ Sending a request to MIIS for searching a

cooperative cells

♦ Neighbors’ capacity state request

♦ Select under loaded LTE cells / RATs

♦ Response by the list of candidate L

sorted

in ascending by load

If L

not empty

• Select the first cell/RAT in the list

• Establish collaboration via X2 or MIH.

• C= C + available capacity in the

collaborative cell

• MIH-RR-Coop = MIH-RR-Coop

∪{selected cell}

Else

Stop=true

end

End

If stop=true

Reject new call

Decrease the rate for some users

End

End.

We define a new MIH primitive of service “MIH-

Cooperate.req” to be exchanged between the MIH

information server and the eNodeBs to ensure the

collaborative resources allocation between LTE-A

macro-cells. This primitive has as a parameter the

minimum required capacity and we use also the

users’ applications to verify that they are supported

or not. The syntax of this primitive is given follow:

MIH-Cooperate.req:

{Available capacity

Users applications }

Step 4:

This step deals with allocation track to see if there is

over served M-UE teams, so as to withdraw unused

resources to be reallocated to underserved M-UE

teams or to incoming calls independently or seeing

the possibility to include new users to one of the

existing M-UE team according to the proposed

scheme described above. We also take into account

resources as soon as they become available after a

terminal leaving.

5 SIMULATION RESULTS

This section illustrates simulation results to evaluate

our proposed algorithms in terms of system

throughput. We worked with the Matlab-based LTE-

A System Level simulator developed by the TU

Wien Telecommunications Institute (Mehlfuhrer et

al., 2011). The simulator allows both link level and

system level simulations. We used also the Matlab-

based convex modeling framework CVX (Becker et

al., 2011) for the resolution of the optimization

problem (10). The LTE-A system parameters used in

simulations are presented in Table 1.

Table 1: Simulation Parameters.

Parameter Description

Duration of simulation 200 TTIs

Number of users per macro cell

100 UEs/MC;

591 in total

User velocity 5m/s

Cell radius 250m

System bandwidth 20 Mhz

Number of PRBs 100 RBs

PRB Bandwidth 180 KHz

Real time requirement [11] 384 kbps

Non real time requirement [11] 32 kbps

In this paper, we compare results of two simulated

scenarios:

• An interfered system and no optimization is

performed with femtocell deployment only;

• An interfered system with joint MUE-team and

femtocell deployment.

First result is shown in Figure 3 that depicts the

empirical Cumulative Distribution Function (CDF)

for all users’ throughput to compare performance

when considering macrocells and femtocells with

and without the integration of our optimization

algorithms. We observe that MUE-teams

deployment besides femotocells offer a higher

average throughput in comparison to the initial

configuration. Indeed, the maximum achieved

throughput when deactivating the proposed

optimization algorithms is about 9.4 Mb/s. While,

when performing our algorithms 20% of users

exceed this value with the possibility of achieving

11.8 Mb/s.

Figure 4 shows performance of scheduling SINR

in form of the empirical CDF when performing the

proposed schemes. By examining the curve we

conclude that, for 90% of users our scheme is able to

find a better choice of resources allocation than with

the initial configuration case.

Cooperative Radio Resources Allocation in LTE_A Networks within MIH Framework: A Scheme and Simulation Analysis

143

Figure 3: Empirical CDF of global throughput.

Figure 4: Wideband SINR.

Figure 5 shows the variation of the average

throughput in the macro cell with the interference

threshold value when using our scheme. This

performance is presented for two different

concentrations of femotocells. For each simulation

run, we fixed three values of threshold interference

when maintaining the same configuration. We can

clearly observe that there is no major difference

between the two cases, even more femotocells

means more interference inside the macro cell.

Figure 5: Average throughput vs. interference threshold.

We can conclude that our optimization solution

maintains the UE throughput level in various

densities of small cells.

6 CONCLUSIONS AND

PERSPECTIVES

We developed in this paper a cooperative RR

allocation approach for LTE-A systems as multi-

RAT environment and highlight the importance of

inter-cell and inter-RAT cooperation. The proposed

solution makes the heterogeneity an enabler to

improve system capacity. We also provide a new

way to manage active mobile users in the macro cell,

in order to minimize inter user’s interference and

improve their QoS. We further propose a novel

PRBs selection and allocation technique that

optimizes resources exploitation. Simulation results

show that the proposed scheme maximizes the

system throughput while guaranteeing QoS for

users. As future work, we attempt to integrate new

features like D2D communication technique inside

MU-teams. We also intend to deploy radio network

virtualization in order to extend system capacity.

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