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Contention-based Fractional Frequency Reuse
Scheme in LTE/LTE-A Network
Hao-Yue Zhan, Bin-Jie Hu, Zong-Heng Wei, Wen-Ji
Liu and Bing Li
School of Electronic and Information Engineering South
China University of Technology Guangzhou, China
eehyzhan@mail.scut.edu.cn, eebjiehu@scut.edu.cn,
wei.zongheng@mail.scut.edu.cn,
Xing Liu
ZTE Corporation
Xi‘an, China
Abstract—In order to handle the low efficiency problem
caused by an extremely uneven distribution of users for LTE/LTE-
A network, we propose a contention based Fractional Frequency
reuse scheme. Simulation results show that the proposed scheme
not only greatly improves the performance of the edge users but
also improve the throughput of the whole network. This method
demonstrates potential for applicability to some specific scenarios.
Keywords—Contention-based Fractional Frequency Reuse
(CFFR), LTE/LTE-A, uneven distribution.
I. INTRODUCTION
Modern cellular network plays an important role in the
development of communication. With the increasing demand
for communications, interference caused by other users within
the same cell badly limits the communication quality of cellular
system. The next generation cellular systems, Long Term
Evolution [1] (LTE) and LTE-Advanced [2], seek significant
improvements in higher cell throughput and cell-edge spectral
efficiency by using Orthogonal Frequency Division Multiple
Access (OFMDA) Technology. It uses orthogonal subcarriers
to effectively improves cell throughput and prevent Intra-cell
interference [3]. But the inter-cell interference (ICI) still
presents a great difficulty that significantly limits the system
performance, particularly those users located at the cell edge [4].
ICI problem refer to the collision between adjacent cells
which are using the same resource blocks [5]. So the overall
system performance will decreased severely with the low
Signal to Interference and Noise Ratio (SINR) which
depends largely on interference strength. Cell edge users’ who
have relatively remote locations need greater transmit power to
communicate so that they will suffer more disturbances and
have lower SINR. Inter-cell Interference Coordination (ICIC)
is one of approaches to solve this problem [6]. ICIC aims in
reducing the collision probabilities to improve the network
performance [7]. Fractional frequency reuse (FFR) has been
widely recognized as an ICIC technique in OFDMA based
wireless networks [8]. FFR scheme divides cellular into center
zone and edge zone. There are some limitations for each cell to
use wireless resources, including restrictions on the use of time-
frequency resources or limit in transmit power. Moreover, users
located at different cell edge will be assigned different resource
Corresponding Author: Bin-Jie Hu, Email:eebjiehu@scut.edu.cn
Center band Edge band
Fig.1 Contention-based Fractional Frequency Reuse network
blocks (RBs) to avoid inter-cell interference. However, FFR
will result in waste of spectrum resources when cellular
network have an extremely uneven distribution of users, e.g.
when the number of the edge users of a cell is smaller than the
number of the assigned RBs of that cell. In this paper we present
a Contention-based Fractional Frequency Reuse (CFFR)
scheme to solve the spectrum wastage problem and improve the
spectrum efficiency of the cellular network. The cellular
network have three cell sites as Fig.1. According to the
simulation results, the proposed scheme can greatly improve
system performance in edge region compared with the FFR
scheme in some uneven distribution case.
The organization of this paper is as follows: In Section II, we
introduce the system model of our method and present the
proposed the Contention-based Fractional Frequency Reuse
(CFFR) scheme. The simulation results are shown in Section
III. At last, conclusions are drawn in Section IV.
II. METHODOLOGY
We consider a three-cell LTE network to test our model and
use A, B, C to stand for the three cells. Our scheme divide the
users within the cell area into center region and edge region, the
same as the FFR method [8]. However, in edge region, we use
a contention-based algorithm to distribute the edge resource
blocks. Our workflow chart shows in Fig.2.
Each sub-frame will monitor the current resource blocks list
and find for idle resource blocks. Afterwards, each base station
____________________________________
978-1-4--/1/$31.00 © IEEE
will contend for these RBs and their probability ௦ for using
each RB is closely linked to the ratio of edge users in cellular
network. The larger the number of edge users the greater the
success rate௦. Then we define the success rate for each cell as
following:
ୱ ൌ௨ಲష
௨ಲషା௨ಳషା௨ష
(1)
௦ ൌ௨ಳష
௨ಲషା௨ಳషା௨ష
(2)
௦ ൌ௨ష
௨ಲషା௨ಳషା௨ష
(3)
Where ݊ݑ݉ିௗǡ ݊ݑ݉ିௗܽ݊݀݊ݑ݉ିௗ stand for
the number of the edge users of the cell A, cell B and cell C,
respectively.
Monito r Usage of R B
Have free RB˛
contention-based dynamic
spectrum resource allocat ion
SINR<T ˛
Tran smissi on o f
info rmatio n
N
Y
transmission is
completed˛
Allocates RB to users
Release RB
N
Y
Y
N
Fig.2 Flow chart of CFFR algorithm
All three base station will generate a series of random
sequenceݎܽ݊݀, which the size of each random element is
between 0 and 1 and the length of sequence is the same as the
numbers of idle RBs. If these random elements are smaller
than ௦, it means that the base station win for the right to use
corresponding RB.
Obviously, the more users in a cell will win for more edge
RBs so that it can transmit more data, and there are three kind
of using status for each RB: (i) Idle (ii) Occupied by one base
station (iii) Occupied by more than one base station. The idle
RB will be contended in the next competition. On the other
hand, the rest RBs which are occupied by users will be released
or not according to their SINR. And different SINR ranges
correspond to different encoding. The details of the relationship
between coding and SINR can be found in [9].
The SINR for a single RB of a typical center user is
ܵܫܴܰ௧ ൌ್ೞାீ ିೞೞିೞೌೢ
ூೝାே (4)
Where ܲ௦ the transmission power of local base station, G is
the channel gain, ܲ௦௦ is path loss, ܲ௦ௗ௪ is shadow effect
loss, ܫ௧ is the interference from other base station and N is
white-noise. Respectively, the SINR for a single RB of a typical
edge user is
ܵܫܴܰௗ ൌ್ೞାீିೞೞିೞೌೢ
ூೞାே (5)
Other parameters are the same except forܫ௦, it is an
interference generated by the base stations which are using the
same RB with local base station. If the user who is using this
kind of RB, we will release this RB if user’s SINR is lower than
T (T=-9.478) that it is too low to support the right encoding, so
these user does not contribute to the throughput of cellular
network. On the other hand, we will random release some RBs
termly in three cells to simulate the completion of data
transmission.
Therefore, the throughput of each user is:
݄ܶݎݑ݄݃ݑݐ ൌ ௨ೌೝೝೝ ൈଶൈൈ
்ೝೌ
(6)
Where ݊ݑ݉ is the number of subcarriers of each RB,
r is the rate, k is the number of OFDM symbols of per slot,
ܶ
is sub-frame length, which is 1ms in LTE-FDD, the unit
of ݄ܶݎݑ݄݃ݑݐ is kbps.
We use the utilization of edge resource blocks to show the
edge efficiency of frequency spectrum. It is different with the
fixed efficiency compared with the edge reuse factor of FFR. It
will dynamic change in the role of CFFR algorithms. It is:
ܧ݂݂݅ܿ݅݁݊ܿݕௗ ൌோೠೞ
ோ
ൈ ͳͲͲΨ (7)
Where ܴܤ௨௦ௗstands for the number of occupied edge resource
blocks of three cells, ܴܤௗ is the total number of edge RBs.
III. SIMULATION
A. Simulation Setting
We use matlab to do a system-level simulation of LTE. In
the simulation, we consider a heterogeneous network with
three cell and 500 users for cell A, 75 users for cell B and cell
C. Ten percent of users will have communication needs and
the network use Round-Robin Scheduling. The total system
band is 10 MHz, which can be divided into 50 RBs. The users
are randomly deployed within the cell coverage. In the
meantime, we use Monte Carlo method to reduce the random
error. The other simulation parameters are listed in Table I.
B. Simulation Result
The proportion of edge users in figures is 20:3:3
corresponding to the Cell A, Cell B and Cell C.
In Fig.3 and Fig.4, we can see the total throughput of the
whole system as the sub frame increase. It shows that: through
the CFFR algorithm, the total throughput of the whole system
TABLE I. SIMULATION PARAMETERS[10]
Parameter
Value
LTE standard
FDD
System bandwidth
10MHz
Cellular Layout
Hexagonal grid, 3 cell sites
Inter-site distance
1000m
Distance-dependent path
loss
L=128.1 + 37.6log
10
(R), R in
kilometers
Lognormal Shadowing
Log Normal Fading with 0
mean, 8dB standard deviation
BS transmit power
33.2dBm
Noise Figure
9dB
Tx antennas (BS)
1
Rx antennas (UE)
1
Traffic model
Full buffer
Link Adaptation
Perfect
Modulation
QPSK and 16-QAM, 64-QAM
White noise power density
-174 dBm/Hz
Subcarriers of each RB
12
numbers of OFDM
symbol of each slot
7
Proportion of center
region
0.7
Proportion of center users
0.6
Proportion of center RBs
0.6
may not grow fast because it takes time to compete for their
appropriate resource blocks. One RB may generate a poor SINR
due to the collision with other base station which is using the
same RB.
Fig.3 System total throughput
However, the throughput has become gradually stabilized
as the sub frame increase. And the total throughput using CFFR
algorithm in three cell is more than 10% than the total
throughput using FFR algorithm. What’s more, the total edge
throughput using CFFR scheme in three cell is more than 25%
than the other one.
The total throughput and edge throughput of each cell as the
sub frame increase are shown in Fig.5 and Fig.6.
Fig.5 shows that the total throughput of Cell A using CFFR
Fig.4 System total edge throughput
Fig.5 Each cell total throughput
Fig.6 Each cell edge throughput
scheme performs 20% higher than the FFR scheme. The other
two cells’ total throughput suffer a slight decrease.
As can be seen in Fig.6, the edge throughput of Cell A has
been greatly improved for more than eighty percent with the
decrease of the other two cells’ edge throughput. But it may not
generate severe degradation of communication quality because
these two cell have slight communication needs.
Fig.7 Each cell edge efficiency
In Fig.7, we can see the edge efficiency of the whole system
as the sub frame increase. Fig.7 shows that the edge efficiency
of frequency spectrum has been greatly improved compared
with the FFR scheme. The proposed scheme can dynamically
adapt to the cell number of edge users to allocate the RBs.
IV. CONCLUSION
In this paper, we proposed a CFFR scheme for LTE-FDD
network to handle the spectrum wastage problem under an
extremely uneven user’s distribution. According to the
simulation results, the proposed scheme not only improve the
edge efficiency of frequency spectrum but also improve the
performance of throughput of the network compared with the
FFR scheme. Therefore, the algorithm solves the spectrum
wastage problem and improve the edge throughput with an
acceptable cost in some specific scenarios.
ACKNOWLEDGMENT
This work was supported by the IOT Key Project of the
Ministry of Industry and Information Technology ([2014]351),
Guangzhou key science and technology project of Industry-
Academia-Research collaborative innovation (2014Y2-00218),
University-Industry Key Project of Department of Education of
Guangdong Province (CGZHZD1102), and the research fund
of ZTE, 2014.
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