Kumbesan Sandrasegaran

Pantha Ghosal

Ramprasad Subramanian

Shouman Barua

Sinh Cong Lam

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Performance of well-known frequency reuse 
algorithms in LTE downlink 3GPP LTE systems 
 
Sinh Cong Lam, Ramprasad Subramanian, Kumbesan Sandrasegaran, Pantha Ghosal, Shouman Barua 
 
Centre for Real-Time Information Networks, Faculty of Engineering and Information Technology, 
University of Technology, Sydney, Australia 
 
Abstract: Intercell interference (ICI) is one of the 
major factors that limit the performance of wireless 
cellular network systems. Soft frequency reuse (SFR) 
as well as its modified algorithms such as Soft 
fractional frequency reuse (Soft FFR) and Distributed 
fractional frequency reuse (Distributed FFR) have 
been introduced as an effective way to optimize 
spectrum and control the ICI.  However, the 
comparison between these algorithms has not fully 
been presented by the researchers proposing the 
models. This paper presents a comparison of the 
performance of well-known frequency reuse algorithms 
in term of system throughput, average packet loss ratio 
and average packet delay. The simulation results 
indicate that the simplest scheme, i.e. Soft FR, archives 
the highest system performance comparing to Soft FFR 
and Distributed FFR. Hence, it is noticed that one of 
the effective methods which optimize frequency reuse is 
to reduce the algorithm complexity. 
Key words: Distributed frequency reuse, Soft 
frequency reuse, Soft fractional frequency reuse, 
network simulation v3 NS3.  
1. INTRODUCTION 
 In Orthogonal Frequency Division Multiple 
(OFDM) multi-cell networks, the main factor, directly 
impacting on the system performance, is intercell 
interference which causes by the use of the same 
frequency band in adjacent cells. Intercell interference 
coordination (ICIC)[1] has been promised as a 
technique that can significantly mitigate the intercell 
interference then improve network performance, 
especially for users suffering low Signal-to-
Interference-Noise ratio (SINR). Generally, ICI 
techniques can be partitioned into two schemes, called 
interference mitigation and avoidance.  
 Interference mitigation aims to reduce or 
suppress the intercell interference during transmission 
or at the receivers. The interference mitigation 
techniques include interference averaging, interference 
cancelation and adaptive beamforming [2, 3]. 
Interference averaging was introduced in an effort to 
randomize the interfering signals among all users by 
scrambling the codeword at the transmitters and the 
processing again at the receivers. This technique is 
quite simple and does not require additional 
measurements and signaling. However, this technique 
does not meet EUTRA requirements because it does 
not improve the signal strength of cell-edge users 
(CEUs). ICI cancellation attempts to suppress the 
interference by using the processing gain at the 
receivers. This technique estimates the interfering 
signal based on the previous receptions and then 
subtracts the estimated interfering signal from the 
received signal to receive more reliable signal. ICI 
cancellation does not improve the strength of desired 
signal, but it can improve the quality of signal. Hence, 
this technique can improve the performance. However, 
it requires the knowledge of interference statistics and 
additional complexity at the receivers. Adaptive 
beamforming utilizes smart antennas which can 
automatically change their radiation patterns to 
improve the signal strength. This technique is quite 
complex and requires additional hardware elements. 
 Interference Avoidance refers frequency reuse 
algorithms which are used to deploy some restrictions 
on the transmission power and resource allocation. The 
aims of ICI avoidance are to reduce the intercell 
interference as well as to improve the desired signal 
strength.  Specially, this scheme does not impose any 
additional computation as well as extra hardware 
element on the user’s device. Hence, it is promised as 
the most effective technique for LTE network to 
provide high quality of services to cell-edge user 
without sacrificing cell-center performance.  
 In most previous works on Interference 
avoidance, the authors proposed the new frequency 
reuse algorithms to optimize a specific parameter such 
as transmission power, intercell interference [4] or cell 
throughput[1, 5]. However, other parameters of cellular 
network performance such as average packet loss and 
average packet delay were not compared together. Due 
to this omission, it might be unfair to compare the 
frequency reuse algorithms because the more complex 
algorithms need more additional processing latency. 
This means if a delay threshold is assigned for each 
packet, the complex algorithms can produce higher 
packet loss ratio than the simple ones. To give an 
overall comparison, this paper compares the three well-
known frequency reuse algorithms, called Soft FR, Soft 
FFR, and Distributed FFR by both mathematical logic 
and simulation. 
2. FREQUENCY REUSE ALGORITHMS 
  In this session, Strict FR, Soft FR, Soft FFR, 
and Distributed FFR are discussed and compared in the 
context of a system with M+1 hexagonal cells and 
 
 
 
every cell is allocated the same N sub-bands. It is 
assumed that frequency reuse factor Δ is deployed and 
N available sub-bands are partitioned into two groups 
having NC and NE sub-bands (NC + NE = N). We 
define that the maximum numbers of interfering base 
stations is the set of base stations that can cause 
intercell interference on a typical user. The interfering 
probability is defined as the probability in which a base 
station cause interference on a typical user.  
2.1 Strict frequency reuse  
  In strict FR with reuse factor Δ = 3 as in 
Figure 1, the whole bandwidth is dived into one 
common sub-band and Δ private sub-band group. The 
common sub-bands are allocated to cell-center area 
with low power level in every cell while each private 
sub-band group is allocated to the cell-edge area at high 
power level. In this case, the cell-center users (CCUs) 
are allowed to use NC sub-bands, while the cell-edge 
users in each cell are only allowed to use NE
Δ
. Hence, 
the interfering probabilities for a cell-edge and cell-
center user are 𝟏 
Nc
 and 𝛥
𝑁𝐸
, respectively. 
 
Figure 1: Strict frequency reuse 
 It is noticed that cell-center users do not share 
their own sub-bands to the cell-edge users, and then a 
cell-center (cell-edge) user is only affected by 
interfering that is cause by base station transmitting on 
the same cell-center (cell-edge) sub-band. Hence, there 
are M interfering cells that can effect on interior users, 
but there are only M
Δ
 for the case of exterior users. The 
interferences that cause both cell-edge and cell-center 
users is, hence, minimized. 
 
 2.2 Soft frequency reuse 
 Soft FR scheme (SFR) [6] is a modification of 
Strict FR in which the whole bandwidth is divided into 
two sub-bands, called the cell-edge and cell-center sub-
bands. The main difference between Strict and Soft FR 
is that the cell-center users share the allocated sub-
bands with cell-edge users in adjacent cells in an effort 
to improve spectrum efficiency and system 
performance. 
Figure 2: Soft frequency reuse with reuse factor Δ = 3 
 Because of sharing resource between cell-edge 
and cell-center users, cell-edge users can use the whole 
cell-edge sub-bands. Hence, the interfering probabilities 
for a cell-center and cell-edge user are, respectively, 1
NC
 
and 1
NE
. The maximum interfering cells for both cases of 
users are the same and equal M in which (Δ−1)
Δ
𝑀 base 
stations transmitting on cell-center sub-bands at low 
power level and 1
Δ
M base stations transmitting on cell-
edge sub-bands at high power level. Therefore, it is said 
that Soft FR can produce more interference on a user 
than Strict FR. 
2.3 Soft fractional frequency reuse 
 Soft fractional frequency reuse scheme (SFFR) 
was introduced in [5] as a combination of Strict and 
Soft to increase the cell-center user throughput without 
increasing the power consumption [7]. In this scheme, 
the allocated edge bandwidth is divided into two non-
overlapping sub-bands called 𝑁′𝐶  center, 𝑁𝑀 medium 
and 𝑁′𝐸  edge sub-bands (𝑁′𝐶 + 𝑁𝑀 + 𝑁′𝐸 = 𝑁). The 
medium sub-bands (yellow color) work as cell-center 
sub-bands in Strict FR and two others such as medium 
and edge sub-bands work as cell-center and cell-edge 
sub-bands in Soft FR [8]. The maximum interferes for a 
typical user in each cell area is similar in Strict and Soft 
FR. 
Figure 3: Soft fractional frequency reuse with factor 
Δ = 3 
 
 
 
 In the Soft FFR and Soft FR algorithms, the 
usage of resource blocks in the adjacent cells is not 
considered. This leads to an increase in intercell 
interference which occurs when one resource block 
(RB) is used by two neighboring cells at the same time.  
2.4 Distributed fractional frequency reuse 
 Distributed fractional frequency reuse 
(Distributed FFR) algorithm [4] was proposed to 
minimize the effect of ICI by exchanging ICI 
information between the adjacent cells through X2 
interface. The eNodeB estimates the quality of channel 
𝑗 based on the cell-edge-band matrix 𝐶𝑖𝑖  [4] 
𝐶𝑖𝑖 = �𝛿𝑖𝑖
𝑖∈𝐼
𝑤𝑖 
In which: 𝛿𝑖𝑖 takes a value of 1 if RB 𝑗 is occupied 
at cell edge and 0 otherwise and 𝑤𝑖  denotes the 
estimated number of UEs at adjacent cell 𝑖. If the 
difference between the power of interfering signal and 
desired signal is larger than the threshold, 𝑤𝑖  will be 
increased by one.  
Sub-channel for which matrix 𝐶𝑖𝑖 is the largest is 
considered to be strongest effected by ICI then assigned 
the lowest priority for transmitting. Hence, Distributed 
FFR can reduce the probability in which two adjacent 
cells transmit on the same resource block at the same 
time. As a result, the interfering probabilities and the 
maximum number of interfering base stations are 
smaller than in Soft FR. 
The differences between these algorithms can be 
summarized in Table 1. It is clear that Distributed FR 
can provide the highest SINR to associated users and 
then it can provide the highest system throughput. 
However, it is reminded that this throughput is analyzed 
without considering the impact of transmission and 
processing delay. The overall performance of these 
algorithms will be discussed in session 3 and 4.  
3. SIMULATION MODEL 
 The network model is shown in Figure 4 in 
which users exchange information with the remote host 
[9] through radio channels, point to point links and 
internet connections. The wireless channels between 
users and eNodeBs are modeled as in [10] in which the 
transmitted signals have to experience propagation path 
loss, fast fading, slow fading as well as Doppler effect. 
The connections from eNodeB to remote host are 
assumed to have unlimited bandwidth and then the 
transmission delay and packets loss on these links can 
be neglected. Hence, this network model can be used to 
evaluate the performance of radio links between users 
and eNodeBs.  
 
Figure 4: Network model [9] 
 To examine the well-known frequency reuse 
algorithms, a 19 hexagonal cell model with frequency 
reuse factor of 3 at the cell-edge and 1 at the cell-center 
is considered. UEs are normally distributed within each 
cell moving randomly at a speed of 3 km/h and have 
internet connections to exchange 72 byte packets (e.g. 
VoIP) with the remote host [9, 11]. The simulation 
parameters are summarized in Table 2 
 
Parameters Value 
Carrier frequency 2GHz 
Bandwidth 5 MHz 
Number of RBs 25 
Number of cells 19 
Cell radius 1000m 
Packet interval 20 ms 
Packet size 72 bytes   
Fading model Extended Pedestrian A [10] 
Scheduling Proportional Fair 
UE distribution Normal distribution, 
Moving at 3km/h 
SINR threshold 20 dB for SFR and distributed FR 
25dB and 15 dB for Soft FFR 
Simulation time 30s 
Table 2: Simulation parameters 
 
We assume that SFR, distributed FFR and the two 
outer areas of SFFR use the same power profiles as 
3GPP recommendation [12] . The center region of cells 
enabled Soft FFR is assumed to work with lower 
power to save energy consumption.  
4. SIMULATION RESULTS 
The system performance parameters including 
average values of packet delay, packet loss ratio/flow 
(PLR) and throughput/UE are calculated for the whole 
system (i.e. 19 cells) and for the worst cell, i.e. center 
cell which is located at the center of 18 cells. From 
figures, it is observed that the average performance 
over the whole system is better than over the worst cell 
which experiences the higher ICI level. Furthermore, 
Soft FFR achieves the lowest system performance due 
to decrease in the transmission power at the center area 
in Soft FFR which may be not strong enough to combat 
the fading and inter-cell interference. It is also noticed 
that in Soft FFR, the entire bandwidth is separated into 
three small sub-bands and allocated to specific areas 
which may reduce the flexible of system resource, e.g. 
 
 
 
users are mainly distributed at a center or edge area. 
On the other hand, Soft FR as well as Distributed FFR 
allow more RBs to be used at the cell-edge area, and 
then these schemes can obtain the higher the 
performance than Soft FFR.  
 
Figure 5: Average Delay/Packet 
 
When compared with SFR, distributed FFR 
algorithm can reduce the ICI by exchange cell-edge-
band matrix 𝐶𝑖 between each cell with its neighboring 
cells. This, however, leads to more complex 
computation required to configure this matrix and 
broadcast it to all eNodeBs. The simulation indicates 
that Distributed FFR only outperforms Soft FR in 
terms of whole system performance with high number 
of users; otherwise it can be observed that the complex 
computation causes the higher delay, packet loss ratio 
and lower throughput than Soft FR. 
 
 
Figure 6: Packet loss ratio per flow 
 
In a VoIP call service environment, it is 
important to maintain the average delay/packet and 
packet loss ratio below 100ms and 10−2 [13] such that 
the QoS requirements of VoIP call service could be 
met. In Figure 6-7, it can be observed that less than 26 
users can be supported by Soft FR and Distributed FFR 
at the target PLR threshold. On the other hand, Soft 
FFR can only support around four less number of users 
at the same QoS.  
 
Figure 7: Average throughput per UE 
 
5. CONCLUSION 
This paper investigates the comparison of 
well-known frequency reuse algorithms in downlink 
3GPP LTE system and identifies the strengths and 
weaknesses of these algorithms to satisfy the VoIP 
QoS service for the users. The differences between 
these algorithms are presented through the paper by 
both mathematical logic and simulation. Soft FFR is 
promised to improve the system performance 
without increasing the power consumption, however, 
splitting the whole bandwidth into 3 small parts 
leads to a decrease in the bandwidth flexible. Hence, 
it obtains the lowest system performance comparing 
with Distributed FFR and Soft FR. Distributed FFR 
archives the lowest intercell interference; however, 
due to the complexity of this algorithm, it only 
slightly outperforms Soft FR in term of whole 
system performance. Then it is important to 
conclude that reducing of complex computation of 
the algorithms is as important as proposing new 
ideas which can improve the system performance.  
 
REFERENCES 
1. R1-050507, G.P.D., Soft Frequency Reuse Scheme 
for UTRAN LTE. 3GPP R1-050507 Huawei, 2005. 
2. Abdelbaset S. Hamza, S.S.K., Haitham S. Hamza, 
and Khaled Elsayed, A Survey on Inter-Cell 
Interference Coordination Techniques in OFDMA-
Based Cellular Networks. IEEE 
COMMUNICATIONS SURVEYS & 
TUTORIALS, 2013. VOL. 15, NO. 4,. 
3. Khan, F., LTE for 4G Mobile Broadband: Air 
Interface Technologies and Performance. 2009: 
Cambridge University Press, . 
4. Kimura, H.S., Inter-Cell Interference Coordination 
(ICIC) Technology. FUJITSU Sci. Tech. J., Vol. 
48, No. 1 2012. 
 
 
 
5. C802.16m-08/782, I., Fractional Frequency Reuse 
in Uplink. LG Electronics, 2008. Available (July 
2012). 
6. Soft Frequency Reuse Scheme for UTRAN LTE, C. 
3rd Generation Partnership Project, France, 3GPP 
Project Document R1-050507, Editor. 
7. Hamza, A.S., et al., A Survey on Inter-Cell 
Interference Coordination Techniques in OFDMA-
Based Cellular Networks. Communications Surveys 
& Tutorials, IEEE, 2013. 15(4): p. 1642-1670. 
8. Kwon, Y., et al., Power Control for Soft Fractional 
Frequency Reuse in OFDMA System, in 
Computational Science and Its Applications – 
ICCSA 2010, D. Taniar, et al., Editors. 2010, 
Springer Berlin Heidelberg. p. 63-71. 
9. Ns3, N.S. https://www.nsnam.org. 
10. 36.104, G.T., E-UTRA Base Station (BS) radio 
transmission and reception. 2010. 
11. 3GPP TS 26.236 version 10.0.0 Release 10. 2011. 
12. 8, G.T.v.R., Physical layer procedures 2009. 
13. V11.6.0, G.T., Policy and charging control 
architecture. 2012. 
 
 
 Strict FR Soft FR Soft FFR Distributed 
FR 
 CEU CCU CEU CCU CEU CC
U 
CMU CCU 
Interfering 
probability 
Δ
𝑁𝐶
 
1
𝑁𝐸 
 
1
𝑁𝐶
 
1
𝑁𝐸 
 
1
𝑁′𝐶
  
1
𝑁′𝐸 
 
1
𝑁𝑀
 <
1
𝑁𝐶
 <
1
𝑁𝐸 
 
Max number of  
interfering BS 
𝑀
Δ  
𝑀 
(Δ − 1)
Δ 𝑀
(1)  
 and 
1
Δ𝑀
(2) 
(Δ − 1)
Δ 𝑀′
(1)  
 and 
1
Δ𝑀′
(2) 
𝑀 
<
(Δ − 1)
Δ 𝑀
(1)  
 and <
1
Δ𝑀
(2) 
Transmission 
power of 
interfering BS 
Low High (1) Low 
(2) High 
(1) Low 
(2) High 
Medium (1) Low (2) High 
Table 1: Comparison between some well-known frequency reuse algorithms 
 


Performance of well-known frequency reuse algorithms in LTE downlink 3GPP LTE systems

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Performance of well-known frequency reuse algorithms in LTE downlink 3GPP LTE systems

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