On the Joint Scheduling and Intra-cell InterferenceCoordination in Multi-relay LTE Uplink

Irfan AhmedCollege of Computers and Information Technology

Wireless Communications and Networking Research Center,

Taif University, P.O.Box 888, Taif, Saudi Arabia

Email: i.ahmed@tu.edu.sa

Amr MohamedComputer Science and Engineering Department

Qatar University, P.O.Box 2713,

Doha, Qatar

Email: amrm@qu.edu.qa

Abstract—The relay-assisted Long Term Evaluation (LTE)cellular system is one of the most promising technologies toenhance the throughput and coverage of the access network.Injection of relays in a cell opens many issues of frequency/timedomain radio resource allocation and frequency planning. In thispaper, we consider the uplink of LTE relay-assisted networkand formulate the joint scheduling and intra-cell interferencemitigation problem as a non-linear optimization taking intoaccount the projection of the gradient of user’s utility functionover the user’s rate vector based on Lagrangian method, thenwe proposed a novel transformation of scheduling problem intocombinatorial Knapsack optimization to convert the optimizationproblem into binary integer non-linear program. Finally, weillustrate the solution through a low complexity algorithm toallocate resources based on group of subchannels (GoS), whileconsidering the contiguity constraint, which is a crucial featureof LTE uplink. Simulation results show that our proposed schemeis near optimal with a much lower complexity of the order ofO(

∑M+1m KmNc,m).

I. INTRODUCTION

The growing demand of broadband access network over

last decade has motivated the telecommunication regularity

bodies to launch an air interface that would be capable of

supporting 1 Gbps in downlink and 500 Mbps in uplink [1],

i.e., The Long Term Evolution - Advanced (LTE-A). Time,

frequency, space, multiuser diversity, opportunistic scheduling

and spectrum reuse algorithms/schemes for conventional cellu-

lar systems are now well developed and matured. It is still not

clear how to extend these concepts to relay-assisted multiuser

communications, especially in relay-based cellular orthogonal

frequency division multiple access (OFDMA) networks where

relays can provide extra frequency reuse and expand network

resources, which can help enhance user experience. [2].

In 3GPP Release 10, relay has been agreed as a working

item (WI). After that, the relay research community diverted

its attention towards the relay-assisted transmission in LTE,

especially the LTE uplink, which, first time introduced the

frequency-domain radio resource contiguity constraint. In [3],

the uplink performance evaluation of the type 1 relays [4]

(i.e., relay with same carrier frequencies for backhaul and

access links) with different backhaul sub-frames in FDD

(frequency division duplex) and TDD (time division duplex)

LTE-A networks is carried out by doing the system-level

simulation. In another work [5] with similar scenario of uplink

of LTE-A with relays, authors proposed a statistic-based over-

provisioned backhaul subframe allocation to be utilized for

flexible co-scheduling of relays and users at the donor eNB.

These works incorporate inband type 1 relay, which uses

same carrier frequency for backhaul and access link and thus

needed time division multiplexing for backhaul and access link

transmissions, whereas we use outband type 1a relaying with

microwave backhaul. This will make the problem formulation

entirely different .

The proposed resource allocation in [6] is close to our work but

in this work author did not cater the contiguous RB constraint

also they used frequency reuse factor of 1 which does not allow

the reuse of same RB in any other relay’s coverage area.

II. SYSTEM MODEL

We consider the localized single carrier frequency division

multiple access (L-SC-FDMA) [7] based LTE uplink system

where a user from set K = {1, ...,K} may or may not transmit

to one of the outbound type 1a associated transparent relay

stations (TRS) m ∈ M = {0, 1, ...,M} (where m = 0 is

for DeNB, Donor eNodeB), depending upon the link quality

with eNodeB and TRS. Typically, the TRSs have high speed

microwave link (Un) with eNodeB. UEs (User Equipment)

are divided in two groups K1 and K2 based on reported

measurement of their received signal reference power (RSRP)

[8]. The total frequency band available to communicate with

UEs is divided into a set N = {1, ..., N} of subchannels. Let

xi,j be the binary decision variables of subchannel allocation.

If subchannel j is allocated to user i, then xi,j = 1 otherwise

xi,j = 0. We have two types of ranges, communication

ranges and interference ranges, when a UE communicates with

its associated uplink entity it generates interference within

the interference range. In general, we are encountered with

2M+1− 1 frequency domain interference sets. Let Km be the

set of UEs, associated with the TRS/eNodeB m and Ni be the

set of subchannels assigned to user (or UE) i, then following

constraint suffices the orthogonality of subchannels within one

TRS/eNodeB m: ∑i∈Km

xi,j ≤ 1 ∀m, j ∈ Ni (1)

Let pi,j be the power allocated to subchannel j by the user

GC'12 Workshop: The 8th Broadband Wireless Access Workshop

978-1-4673-4941-3/12/$31.00 ©2012 IEEE 111

i and Pi be the total power of user i. Then the total power

transmitted by the user i is given by∑j∈Ni

pi,j ≤ Pi, ∀i ∈ K (2)

Pi := min{Pmax, 10log10P0NRB,iβLΔMCSF}. Here Pmax

is the maximum allowed transmit power of the UE, P0is a cell-specific parameter used for controlling the signal-

to-interference-and-noise ratio (SINR) target, NRB,i is the

number of physical resource blocks allocated to UE i, β is

the cell specific path loss compensation factor, L is the uplink

path loss estimate calculated in the eNB, and ΔMCS and Fare UE specific MCS (modulation and coding scheme) offset

and closed-loop correction parameters, respectively [9]. We

put an additional minimum rate Rmin constraint that ensures

the minimum rate requirement for each user promised by the

operator.

The scheduler takes the decision at the beginning of every

transmission time interval (TTI). It tries to find a maximum

weighted sum of the users’ rates over a feasible rate region.

We assume that the eNodeB has the knowledge of SINR per

unit of transmit power, γ(j)m,i through the reference signal (RS)

for each UE i and subchannel j associated with eNodeB/TRS

m. 3GPP has recommended localized single carrier frequency

division multiples access (L-SC-FDMA) which has a con-

tiguous resource block (RB) constraint for each user. Hence,

we perform frequency domain resource allocation in terms of

subchannel (or RBs), and γ(j)m,i represents the average SNR

over subchannel j. The channel conditions are time varying

and can be modeled by a stochastic channel state variable

γ(t) ∈ S at each scheduling time t, where S is the channel

state space. We define a rate region

R(γ) = {r : ri =∑j∈N

ξ(pi,j , γ(j)m,i)} (3)

for each state γ(t) ∈ S such that when the channel is in

the state γ, the users may transmit at any vector of rates

r = (r1, ..., rK) ∈ R(γ). Here, ξ(a, b) = log2(1 +abΓ ). Γ

is the gap approximation which compensate the SNR gap

between AWGN and practical wireless channel. For M-QAM

modulation and target bit error rate of Pe, Γ = −2/3ln(5Pe)[10]. All of the UEs are assumed fixed and associated with

one TRS or eNodeB. We assume that the TRS or eNodeB

association is done before the resource allocation scheme

according to the long term channel state information.

III. PROBLEM FORMULATION

We formulate the scheduling and resource allocation prob-

lem as gradient-based scheduling framework. This framework

has been studied for the downlink of a Gaussian broadcast

channel with orthogonal CDMA transmissions [11]. In our

model, the centralized schedular at eNodeB has the channel

information of all directly connected UEs as well as the

channel knowledge of TRS associated UEs. During each

scheduling epoch, within each group K1,K2 (K = K1+K2),a rate vector rt ∈ R(γt) is selected that has the maximum

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Fig. 1. System Model

projection onto the gradient of user’s utility function U. We

define the utility function Ui assigned to user i as a function of

exponentially weighted moving average throughput upto time

t over last transmission frame T i.e., Ri,t and the queue length

at time t.

U(Rt,Qt) =K∑i=1

Ui(Ri,t, Qi,t) (4)

The average rate Ri,t for user i at time t can be computed

through infinite impulse response (IIR) filter [12]

Ri,t = αri,t + (1− α)Ri,t−1, 0 < α < 1 (5)

where ri,t is the rate allocated to user i at time t within a

time window controlled by α. Therefore, the objective function

becomes

maxrt∈R(γt)

∇U(Rt,Qt) · rt (6)

where U(Rt,Qt) =∑K

i=1 Ui(Ri,t, Qi,t), i.e., from the system

point of view the utility function is the weighted sum of a

function ui(·) across all users in a cell. In order to capture

the fairness and QoS in our utility function ui(·) we use the

following logarithmic function,

ui(Ri,t, Qi,t) = log(Ri,t)Qi,t (7)

We consider the optimal solution to (6) when R(γt) is given

by (3), as

maxx,p

∑i∈K

∂Ui(Ri,t, Qi,t)

∂Ri,t

∑m∈M

∑j∈N

log(1 +pi,jγ

(j)m,i

Γ) (8)

subject to the constraints in (1), and (2), and

Ri,t ≥ Rmin ∀i ∈ K (9)

with

x = {xk = 1|Nk| | i = k, j ∈ Nk,Nk ∩ Nk′ = ∅, ∀k = k′}(10)

112

p = {pi,j ≥ 0 | pi,j ≤ xi,jςi,j,m

γ(j)m,i

∀i, j,m} (11)

where ςi,j,m is a maximum SINR constraint defined as ςi,j,m.=

SINR64QAM , i.e., SINR that can provide 64QAM average

across subcarriers in subchannel j allocated to user i. In the

objective function (6) the time varying weight assigned to user

i at time t is given by the gradient of a utility function.

We define I � {Ii,j}K,Ni,j=1. Ii,j represents the UE i’s

interfering set of relays and eNodeB on subchannel j. Ii,jdenotes any subset of M = {0, 1, ...,M}, including empty set

which refers to the case that no other UE from neighboring

set M−m is currently using the same subchannel j. This will

ensure that a frequency domain resource would be utilized by

one and only one serving entity (eNodeB or TRS) within an

interference range of an UE. The SINR γ(j)m,i, for UE i on

subchannel j can be expressed as:

γ(j)m,i =

xi,j |H(j)m,i|2

σ2i,j +∑

m∈Ii,j∑

i∈Kmxi,j |H(j)

m,i|2(12)

where m ∈ M − {m} and σ2i,j is the noise power density.

H(j)m,i is the channel gain when subchannel j is allocated

to user i associated with eNodeB/TRS m. Practically, the

channel gain depends upon various factors, including thermal

noise at the receiver, receiver noise figure, antenna gains,

distance between transmitter and receiver, path loss exponent,

log normal shadowing and fading, hence we can write [13]

H(j)m,i = −− p10log10dm,i − ζ

(j)m,i + 10log10F

(j)m,i (13)

In above equation, (83.46dB) is a constant depending upon

thermal noise at receiver, receiver noise figure, and antenna

gains, p (3.5) is the path loss exponent, dm,i is the distance

in Km from UE i to eNB/TRS m, ζ(j)m,i (10.5dB) is shad-

owing parameter modeled by a normally distributed random

variable with standard deviation 8 dB, and F(j)m,i corresponds

to Rayleigh fading.

In the presence of interference from other UEs is in subchannel

j, subchannel j assigns to UE i (i = i) according to the

following condition:

xi,j =

{1, γj,i > γthres;

0, γj,i < γthres. ∀m (14)

IV. OPTIMAL EQUAL POWER CONTIGUOUS SUBCHANNEL

ALLOCATION

The optimization problem in (6) is a combinatorial prob-

lem of prohibitively large complexity, especially due to the

subchannel adjacency requirement. We would have exactly Kgroups of subchannels (GoS), where K is the number of active

users at scheduling epoch. Each GoS contains ni, i = 1, ...,Knumber of subchannels such that

∑i ni = N . We transform

this problem into a pure binary integer program that matches

the famous Knapsack problem also known as capital budgeting

or cargo loading problem [14]. This problem has the following

generic form:

max∑j

vjxj (15)

s.t∑j

djxj ≤ W

xj = 0, 1, ∀j

where the decision variable xj indicate whether the jth

alternative item is chosen (xj = 1) or not (xj = 0). Each

item worth is vj and the objective function gives the total

value of all items chosen. The capacity used by each xj is

dj . The total capacity used should be less than or equal to the

capacity limit W . In our case, each element of decision vector

xj corresponds to a particular GoS, and each element in the

value vector vj is simply an element of resultant vector of rate

vector projection onto the gradient of user’s utility function. dj

is the constraint matrix and W corresponds to the minimum

rate requirement Rmin. The constraint matrix d is used to

enforce the subchannel contiguity and exclusivity constraints.

For N number of subchannels, there are L = N2 (N + 1)

number of possible combinations of contiguous subchannels.

The adjacency constraint for user k leads to following matrix:

dk = [C1, ...,C(N)L ] (16)

For N = 3, Cl =∑3

j=1Rj,l, where Rj,l is the moving

average throughput with the allocation of subchannel j.

Weighted average throughput (WAT) matrix with contiguous

subchannels looks like

⎡⎣ R1,1 0 0 R1,4 0 R1,6

0 R2,2 0 R2,4 R2,5 R2,6

0 0 R3,3 0 R3,5 R3,6

⎤⎦

We associate each possible WAT column l for a user k with

a binary decision variable xk,l, which indicates whether a

particular GoS is chosen or not. This decision vector is given

as xk = [xk,1, ..., xk,L]T .

Next, we determine the constraint matrix on x. From the

original optimization problem in (8) we can see that we

have minimum throughput constraint in addition to subchan-

nel exclusivity and adjacency constraints. First we work for

minimum rate and subchannel adjacency constraints and form

the following matrix inequality:

⎡⎢⎢⎢⎢⎣

−d1,L 0L · · · 0L

0L −d2,L. . .

......

. . .. . . 0L

0L · · · 0L −dK,L

⎤⎥⎥⎥⎥⎦

⎡⎢⎢⎢⎣

x1x2...

xK

⎤⎥⎥⎥⎦ ≤ −

⎡⎢⎢⎢⎣

R1,mim

R2,min

...

RK,min

⎤⎥⎥⎥⎦

(17)

Secondly, in order to enforce the exclusive subchannel alloca-

tion constraint, we have the following condition:

[u1,L u2,L . . . uK,L

] [x1 x2 . . . xK

]T ≤ 1N(18)

113

where uk,L is a possible contiguous RB pattern (RBP) matrix

of user k, e.g., we have following RBP corresponding to above

WAT matrix: ⎡⎣ 1 0 0 1 0 10 1 0 1 1 10 0 1 0 1 1

⎤⎦ (19)

Finally, we build the constraint in (15) by combining (17)and (18) as

⎡⎢⎢⎢⎢⎢⎢⎣

−d1,L 0L · · · 0L

0L −d2,L

. . ....

.

.

.. . .

. . . 0L0L · · · 0L −dK,L

u1,L · · · uK−1,L uK,L

⎤⎥⎥⎥⎥⎥⎥⎦

⎡⎢⎢⎢⎣

x1x2...

xK

⎤⎥⎥⎥⎦ ≤

⎡⎢⎢⎢⎢⎢⎣

−R1,min

−R2,min

.

.

.−RK,min

1N

⎤⎥⎥⎥⎥⎥⎦

(20)

Now the above inequality corresponds to the inequality con-

straint of generic Knapsack problem. In this way we have

transformed our SC-FDMA resource allocation problem into

a well known type of binary integer programming problem

whose solution methods are widely investigated and estab-

lished. MATLAB has bintprog function to solve this problem,

bintprog uses a linear programming (LP)-based branch-and-

bound algorithm to solve binary integer programming prob-

lems.

V. SUBOPTIMAL APPROACH FOR RESOURCE ALLOCATION

Although the optimal solution gives the best compromise

of the resource usage, the price to pay is the computational

complexity in solving the knapsack problem. In cases where

our computational resources are limited, suboptimal heuristic

algorithms that are much less complex yet still perform almost

as well are of significant value. This section presents a low

complexity scheduling algorithm implemented in eNB. We

define the Group of Subchannels (GoS) which consists of a set

of consecutive subchannels. Number of subchannels per GoS

can be computed by dividing total available subchannels by

the number of users to transmit, so, the minimum size of an

GoS is equal to one subchannel when the number of users is

equal to the number of subchannels (N = K), which results in

maximum number of GoSs (equal to number of subchannels).

If the number of users exceeds the number of subchannels

(K > N ), the scheduler randomly selects the users for each

epoch until all subchannels are consumed. When subchannels

are greater than the number of active users (N > K), i.e.,

[q, r] = N/K results in quotient q (number of subchannels per

GoS) and reminder r. The remaining r subchannels are evenly

assigned to first r GoS. The proposed algorithm consists of

allocating GoS nc to UE km associated with eNB/TRS m in

such a way to maximize the objective function

δ(nc)km

= ΔUkm(log(Rkm,t)Qkm,t)rk∗

m(Pkm ,Nkm ∪ {nc})

(21)

where rk∗m(Pkm ,Nkm ∪ {nc}) gives the user information

rate when GoS nc is allocated to UE km. The scheduling

process is outlined in algorithm 1. The complexity order of

the proposed algorithm is O(∑M+1m KmNc,m) compared to

the high complexity O(((M+1)K)N ) [15] of BILP problem.

Algorithm 1 Low Complexity Heuristic

Step 1: intializationAt the beginning of each TTI, the eNB updates the channel

conditions of all links involved in the K1 and K2 groups

using CSI (channel state information).

Nc = No. of contiguous subchannels (nc = {1, ..., Nc})Step 2:

for m = 0 to M dofor nc = 1 to Nc dok∗(nc)m = argmaxkm

δnc

km{condition for allocation of GoS}if Rk∗

m,t < Rm and γnc

k∗m> γthres then

Nk∗(nc) ∪ {nc}{delete the allocated GoS}Nm = Nm − {nc}

end if{to ensure the GoS level contiguity}if k∗(nc)

m = k∗(nc−1)m then

Km = Km − {k∗(nc−1)m }

end ifend for

end for

VI. SIMULATION RESULTS

The simulation model consists of a single cell with an eNB

equipped with an omnidirectional antenna. Cell radius is 1KM

and relays are placed at 0.75KM from the eNB. We divided

total bandwidth in two part namely, f1 and f2. eNB has total

bandwidth f1 + f2 within the inner radius and the relays use

f1, f2 alternatively to avoid the inter-relay interference. The

throughput is averaged over 100 TTIs, with the duration of a

TTI being 1 msec. The total bandwidth considered is B = 5

MHz, subdivided into 25 RBs of 12 subcarriers each. 7 OFDM

symbols in 1 TTI including the reference signal (RS). The

maximum mobile transmit power is considered to be 24 dBm

in compliance to Class 2 UE defined in 3GPP TS 25.102 . All

UEs are assumed to transmit at the maximum power, and the

power is subdivided equally among all subchannels allocated

to a UE. In the simulation we use eNB and one relay. Relays

can be increased without the loss of generality because they

operate in orthogonal bands.

Fig. 2 shows the individual user throughput for binary integer

optimal solution, round robin (RR), and proposed GoS based

heuristic algorithm with and without relays. The proposed

scheduling with relays gives near optimal solution for every

user with a minor difference in average throughput across all

users. Heuristic GoS allocation without relays suffers from

the large variations in average user’s throughput due to the

dominant path loss propagation which depends upon the user

distance from the eNB. RR solution is shown as baseline

algorithm which allocates radio resource without any objective

and constraint in round robin fashion. Sum throughput has

114

1 2 3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 106 Individual user throughput

Number of users

Indi

vidu

al u

ser t

hrou

ghpu

t in

bps

Optimal RB allocationRR GoS allocationHeuristic GoS allocationHeuristic GoS allocation w/o relays

Fig. 2. Average per user throughput over 100 TTIs

1 2 3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5x 108 Sum Capacity

Number of users

Sum

thro

ughp

ut in

bps

RR GoS allocationHeuristic GoS allocationOptimal RB allocation

Fig. 3. Sum Capacity with increasing number of users

been evaluated as shown in Fig. 3. The sum capacity of

proposed scheme is less than even the RR when there are few

users because of the utility function which not only caters the

throughput but also the fairness among the users and allocate

more resource to the user who has low average throughput in

the last TTI.

The performance of the capacity maximization heuristic

algorithm is close to that of the optimal solution, and signif-

icantly outperforms the RR GoS resource allocation scheme.

In addition, at the price of loss in capacity, the fairness-

constrained heuristic algorithm significantly increases the fair-

ness index FI, based on Jains index [16] compared to the non-

relay GoS allocation scheme, as shown in Fig. 4. RR resource

allocation is known as the fairest scheme because it allocates

resource to each user in sequel without taking into account the

channel conditions and other constraints.

VII. CONCLUSION

We have presented a combinatorial Knapsack optimization

based joint scheduling and intra-cell interference mitigation

scheme. A gradient of utility function based weighted sum of

users throughput has been evaluated analytically. In addition,

the presented problem has been transformed into pure binary

integer Knapsack program. Finally a low complexity near

optimal heuristic solution is presented. Performance results

demonstrate the efficacy of proposed algorithm in terms of

individual throughput, sum throughput, and the fairness.

10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Jain Fairness Index

Resource allocation schemes

Fairn

ess

Inde

x

Optimal RB allocationRR GoS allocationHeuristic GoS allocationHeuristic GoS allocation w/o relays

Fig. 4. Fairness Index

ACKNOWLEDGMENT

This work is supported by the Qatar Telecom (Qtel) Grant

No.QUEX-Qtel-09/10-10.

REFERENCES

[1] Requirements for further advancements for Evolved Universal TerrestrialRadio Access (E-UTRA) (LTE-Advanced) (Release 10), 3GPP Std. 3GPPTS 36.913, Rev. 10.0.0, 2011-13.

[2] O. Oyman, “Opportunistic scheduling and spectrum reuse in relay-basedcellular networks,” IEEE Trans. on Wireless Communications, vol. 9,no. 3, pp. 1074–1085, Mar. 2010.

[3] W. Hong, J. Han, and H. Wang, “Full uplink performance evaluationof FDD/TDD LTE-advanced networks with type-1 relays,” in VehicularTechnology Conference (VTC Fall), 2011 IEEE, sept. 2011, pp. 1 –5.

[4] Evolved Universal Terrestrial Radio Access (EUTRA): Relay architec-tures for E-UTRA (LTE-Advanced) (Release 9), 3GPP Std. 3GPP TR36.806, Rev. 9.0.0, 2010.

[5] O. Bulakci, A. Bou Saleh, S. Redana, B. Raaf, and J. Hamalainen, “Flex-ible backhaul resource sharing and uplink power control optimizationin LTE-advanced relay networks,” in Vehicular Technology Conference(VTC Fall), 2011 IEEE, sept. 2011, pp. 1 –6.

[6] E. Yaacoub and Z. Dawy, “Uplink scheduling in LTE systemsusing distributed base stations,” European Transactions onTelecommunications, vol. 21, no. 6, pp. 532–543, 2010. [Online].Available: http://dx.doi.org/10.1002/ett.1408

[7] J. Lim, H. Myung, K. Oh, and D. Goodman, “Channel dependentscheduling of uplink single carrier fdma systems,” in Proc. IEEE VTC’06Fall, vol. 1, Oct. 2006, pp. 1–5.

[8] “Additional RSRP reporting trigger for ICIC,” Ericsson, Sevilla Spain,3GPP TSG RAN WG1 Meeting 51b, R1-080361, Jan. 2008.

[9] Evolved Universal Terrestrial Radio Access (EUTRA): Physical LayerProcedures (Release 8), 3GPP Std. 3GPP TS 36.213, Rev. 8.5.0, 2008.

[10] A. Goldsmith, Wireless Communications. UK: Oxford UniversityPresss, 2005.

[11] V. G. Subramanian, R. A. Berry, and R. Agrawal, “Joint scheduling andresource allocation in cdma systems,” IEEE Transaction on InformationTheory, vol. 56, no. 5, pp. 2416–2432, 2010.

[12] R. Madan, S. P. Boyd, and S. Lall, “Fast algorithms for resourceallocation in wireless cellular networks,” IEEE/ACM Transactions onNetworking, vol. 18, no. 3, pp. 973–984, 2010.

[13] E. Yaacoub and Z. Dawy, “Joint uplink scheduling and interferencemitigation in multicell LTE networks,” in Proc. IEEE ICC, vol. 1, Kyoto,2011, pp. 1–5.

[14] B. Dimitris and R. Weismantel, Opimization over Integers. Belmont,MA: Dynamic Ideas, 2005.

[15] M. Salem, A. Adinoyi, H. Yanikomeroglu, and D. Falconer, “Oppor-tunities and challenges in OFDMA-based cellular relay networks: Aradio resource management perspective,” Vehicular Technology, IEEETransactions on, vol. 59, no. 5, pp. 2496 –2510, jun 2010.

[16] M. Salem, A. Adinoyi, M. Rahman, H. Yanikomeroglu, D. Falconer,Y.-D. Kim, E. Kim, and Y.-C. Cheong, “An overview of radio resourcemanagement in relay-enhanced ofdma-based networks,” Communica-tions Surveys Tutorials, IEEE, vol. 12, no. 3, pp. 422 –438, quarter2010.

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