International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 3- July 2015
Fuzzy Based Inter Cell Interference Cancellation in LTE
System
Priyanka
#
M.tech Student in Electronics and communication, VTU Belgaum
Karnataka, India
Abstract— LTE System is emerging wireless technology
which offers reallocation and reutilization of the unused
subcarriers. Not all the network runs with highest load at all
the time. Therefore part of subcarrier always remains
unused. In a multi radio environment LTE System allows the
user to request for subcarrier while roaming from another
service provider. In case the service provider has free
subcarriers, it can reallocate the subcarrier to the
demanding user. Also power allocation is a major concern
in such a network. The allocated power depends upon inter
cell interference among user transmission.
In this work we provide a Fuzzy based mechanism for
coordinating inter cell interference and reduction using
appropriate channel, modulation and subcarrier selection.
Keywords —LTE, Fuzzy system, Channel allocation,
Power allocation
I. INTRODUCTION
LTE is Long Term evolution network. Generally it is
referred in conjunction of 4G network. A LTE system
is used to provide seamless internet connectivity to the
mobile devices (4G) by increasing the data rate with
the collaboration of other underneath radio networks.
LTE basically, a mechanism of combining various
networks seamlessly.
Other technologies are also presented [1-10] in order
to improve the capacity of system.
The main problem with LTE system is how to manage
different technologies and how to increase channel
capacity because the objective of it is to increase the
available channel capacity by multifold.
In this work we design a MAC/Phy standard of LTE
system based on OFDM. The cellular cell is divided
into micro cells. A micro cell is part of the large
cellular cell which supports data communication
through any of the wireless techniques. As LTE
supports multiple technologies to do exist, multiple
channels and data rates are to be managed efficiently.
Currently the access points or the base stations
manage the data rate through appropriate sub carrier
allocation. Such allocations are based on user demand
as well as available resources. However inter cell
interference is a major concern in such networks.
Before any resource allocation, coordination between
the access points is desired. Current state of art
techniques mainly focus on Carrier to Interference
ratio (CIR) based measurement for resource
ISSN: 2231-5381
allocations. But such interferences affect data in
different ways. For instance, for voice transmission
the quality of received voice should be considered
where as image reception quality is measured by
PSNR. Hence single parameter based technique does
not offer efficient solution for resource allocation. In
this work we develop a novel technique for resource
allocation with the help of multiple parameters such as
BER, PSNR and Edge Similarity percentage for
images and resource allocation is based on a fuzzy
based system that can convert various quantitative
values into qualitative value set of {VERY LOW,
LOW, MEDIUM, HIGH , VERY HIGH} interference.
Based on the interference the resources are allocated
and it is shown that interference is reduced
significantly by proposed approach.
II. PROBLEM STATEMENT
The problem statement of the work can be
summarized as to design and simulate a LTE system
and demonstrate efficient resource allocation based on
observed transmission quality among the access point
and coordination based exchange of the observed
quality. The transmission quality set defined by
multiple parameters must be resolved using a fuzzy
based system in order to provide better transmission
quality to the nodes. The system must justify the
design with the help of appropriate graphs and should
improve the transmission quality achieved in present
system.
III. FUZZY BASED SYSTEM
Fuzzy logic based system is designed Fuzzy logic, [2]
distributes the decision on a number of fuzzy sets .
The decision is made based on combination of fuzzy
sets and which depends on degree of closeness to
required performance. For the performance to be
better a feedback loop is connected in within the
system in order to monitor the penalties action taken
by the fuzzy system. The resource is nothing but the
frequency bandwidth and power which can be
compensated using antenna adjustment by down tilt or
by allocating required power. This can be achieved
using fuzzy logic. For self healing purpose it needs to
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 3- July 2015
be work with cell outage detection (COD) and cell
outage compensation (COC) under the project of
SOCRATES [2]. The near far effect is also addressed
by controlling the SINR. From the above discussion
we can deduce the concept of fuzzy reinforcement.
IV. LTE SYSTEM
In simple terminology a LTE system allows multiple
technologies and channels to co exist and user can
select the most appropriate channel and band based on
the demand. The heart of the system is Evolved Node
Base Stations which are commonly referred to as eNB.
Such nodes can switch between different technologies
and channels. The core objective is to increase the
data speed with the assistance of various core radio
First data is converted from serial to parallel and then
each stream is processed as independent data block.
They are added with parity codes using convolution
encoder and an interleaved sequence is added between
the frames. Orthogonal modulator modulates the
sequence with carrier signal that are phase shifted by
each other with multiples of fraction of pi. For
example a six sub carrier sequence carrier signals will
have phase 0, 60,120,180,240,300, and 360 degrees.
Rake receiver is adopted which can sum up the signals
coming from different directions and then decode the
signa
.
networks.
AWGN channel adds random noise with message
sequence s{t}. The noise is bi-polar in nature with the
distribution as shown in figure
Fig. 1: LTE System Architecture
Different data rates at (1.4, 3 , 5 , 10 , 15, 20) MHz are
supported. LTE system divides the region into micro
cells ( 100 km range), followed by Pico cells and
Femoto cells [4] which ranges within meters. 200
active nodes can transmit simultaneously in 5Mhz
transmission cell. The network is mainly monitored by
eNBs. The eNBs also supports legacy systems like 3G
and 2G cellular network systems.
V. RELATED WORK
Fig.2 Noise distribution for +1 and -1 Signal
In this work we mainly focus on OFDM based
System for data transmission. We simulate the
network with image transmission from eNBs to
mobile nodes in the context of downlink transmission
and measure the transmission quality at UEs. Further
the measurement in transmitted back to the eNBs.
Each eNB collaborates to change the modulation
index, signal power and data rate for the nodes.
ISSN: 2231-5381
It can be seen that Gaussian distribution has two series:
+1 and -1. Thus several sequences of the message
signal will become negative. This affects the
performance of the system greatly. Therefore in order
to overcome this problem, we use NRZ( non return to
zero) coding . Rather than transmitting logical 1 and 0,
CDMA system transmits -1 for logic zero.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 3- July 2015
VI. SYSTEM DESIGN
Each Femoto cell is an independent
subnetwork monitored by a access point.
Each Femoto cell operates at different sub
carriers and have different allocated power.
A mobile device instead of directly accessing
the services through base station access the
service through Femoto cells.
Based on the user’s demand user can be
allocated to different sub carriers. Based on
quality of transmission, user may be serviced
by appropriate cells.
Each of the cell will divide the allocated
spectrum through subcarriers.
When base station finds out that in SNR is
low in a mobile’s transmission specific to a
particular cell, it assigns the mobile to
another cell. It means that when one
particular type of LAN has more
congestion/delay/packet loss/BER, a node
can be made to access the base station
through other network.
Finally the objective is to show that by
adopting Femoto cell structure, effective
throughput of the system can be significantly
improved.
-10--0.0021867
-8--0.0019464
-6--0.0013216
-4--0.00076894
-2--0.00076894
0--0.00050461
2--0.00031238
4--4.8058e-05
6--4.8058e-05
8--4.8058e-05
10--0
12--0
14--0
16--0
18--0
20--0
M=16 Channels=4
-2
10
-3
10
-4
10
-5
10
-10
-8
-6
-4
-2
0
2
4
6
8
-10--0.0031959
-8--0.0025231
-6--0.0017541
-4--0.0014898
-2--0.0013216
0--0.00088908
2--0.00045656
4--0.00026432
6--0.00033641
8--0.00014418
10--9.6117e-05
12--0
14--0
16--0
18--0
20--0
System Requirement
1. Hardware Requirement:
* Intel i3 Core and Above Processor
* Minimum 4 GB RAM
* Accelerated Graphics adapter for better simulation
display
2. Software Requirement
* Matlab 2012 and Above
* Windows 7 or 8.1
* Microsoft Excell
M=16 Channels=8
-2
10
RESULTS
-3
10
1. Transmission
Modulation Index
of
PNG
Icon
with
varying
-4
10
-5
10
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-10
-8
-6
-4
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0
2
4
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8
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International Journal of Engineering Trends and Technology (IJETT) – Volume 25 Number 3- July 2015
-2
10
M=4
M=8
M=16
-3
10
-4
10
-5
10
0
2
4
6
8
10
12
14
It is clear from the above graph that as the modulation
index increases, BER also increases. This is because
higher constellation more level of noise creeps into the
transmission. Therefore by controlling the modulation
index depending upon the error helps in reducing BER.
VII. CONCLUSION
We have proposed a unique system here that
overcomes this limitation of the LTE system by
leveraging Fuzzy based resource allocation. We have
also shown that Fuzzy logic can efficiently resolve
network quality assessment by quantitative to
qualitative transform. Simulation results show
significant improvement over non fuzzy based
systems.
VIII.REFERENCES
LTE”(IJARAI) International Journal of Advanced Research in
Artificial Intelligence, Vol. 1, No.1,2012
[2] Saeed, Osianoh Glenn Aliu, Muhammad Ali Imran
“Controlling Self Healing Cellular Networks using Fuzzy
Logic”2012 Wireless communication and network conference
[3] Koudjo M. Komadi, Kester Quist-Aphetsi, Robert A. Sowah,
Amevi Acakpovi” An Interference Reduction Strategy for TDDOFDMA Cellular Systems” Internatio€nal Journal of Computer,
Control, Quantum and Information Engineering Vol:8, No:1, 2014
[4] Jayasankar .S “A Comprehensive Study on System Capacity
Improvement in LTE Femto cells by On-Request Channel
Allocation “International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
[5] Aamod Khandekar Naga Bhushan Ji Tingfang Vieri Vanghi
“LTE-Advanced: Heterogeneous Networks” 2010 European
Wireless Conference
[6]Panagiotis Vlacheas, Evangelos Thomatos, Kostas Tsagkaris and
Panagiotis Demestichas “Autonomic Downlink Inter-Cell
Interference Coordination in LTE Self-Organizing Networks”
[7] A. Z. Yonis, M. F. L. Abdullah and M. F. Ghanim “ Effective
Carrier Aggregation on the LTE-Advanced Systems”
International Journal of Advanced Science and Technology Vol. 41,
April, 2012
[8] P. Munoz, R. Barco, I. de la Bandera, M. Toril and S. LunaRamırez” Optimization of a Fuzzy Logic Controller for
Handover-based Load Balancing”
[9] P. Muñoz ⇑, R. Barco, I. de la Bandera “Load balancing and
handover joint optimization in LTE networks using Fuzzy Logic
and Reinforcement Learning” Computer Networks 76 (2015)
112–125
[10] Nabeel Khan, Maria G Martini and Dirk Staehle ”QoS-aware
composite scheduling using fuzzy proactive and reactive
controllers” EURASIP Journal on Wireless Communications and
Networking 2014, 2014:138
[1] Aderemi A. Atayero and Matthew K. Luka “ Adaptive NeuroFuzzy Inference System for Dynamic Load Balancing in 3GPP
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