Thesis

UNIVERSITE LIBANAISE
Faculté de Génie
UNIVERSITE SAINT-JOSEPH
Faculté d’Ingénierie ESIB
Rapport de stage Master 2 Recherche
Pour l’obtention du diplôme Master Recherche en
Réseaux de télécommunications
Gestion des interférences dans les HetNets
Par
Bachir LAHAD
Encadré par : M. Marc IBRAHIM
Soutenance le 23 Juillet 2015 devant le jury composé de
Dr. Marc IBRAHIM
Dr. Abed Ellatif SAMHAT
Dr. Nicolas ROUHANA

Abstract
Long term evolution (LTE) is the standard that the Third-generation Partner-
ship Project (3GPP) developed to be an evolution of UMTS. LTE offers higher
throughput and lower latency than UMTS and this is mainly due to the larger
spectrum used in LTE but in terms of spectrum efficiency LTE does not offer a
lot of improvements compared to UMTS. The reasonsAfor that is that current
technologies such as UMTS and LTE are approaching the theoretical boundaries
in terms of spectral efficiency. Since spectrum has become a scarce resource nowa-
days, new ways have to be found to improve the network performance and one of
the studied approaches to do that is to enhance the network topology.
The concept of heterogeneous networks has attracted a lot of interest recently
as a way to improve the performance of the network. The heterogeneous net-
works approach consists of complementing the Macro layer with low power nodes
such as Micro or Pico base stations. This approach has been considered a way
to improve the capacity and data rate in the areas covered by these low power
nodes; they are mostly distributed depending on the areas that generate higher
traffic.
Since cell selection for the users is based on the downlink power level and due
to the transmitting power differences between Macro and Pico nodes, Pico nodes
might be under-utilized, meaning that a low number of users are attached to the
Pico nodes. Moreover, this difference in transmission power causes a sever uplink
interference between macro users and pico users.As a solution to this problem an
offset to the received power measurements used in cell selection is applied allow-
ing more users to be attached to the Pico nodes, this solution is called ”Range
Extension” which refers to the extended coverage area of the Pico nodes. The
problem with Range Extension is that it drastically increases the interference
that the Macro nodes impose on the Pico nodes users in the Range Extension
area in terms of data and control channels (mainly on downlink path).
Enhanced Inter-Cell Interference Coordination (eICIC) schemes have been pro-
posed to combat the heavy interference in the Range Extension case ranging from
frequency domain schemes like carrier aggregation to time domain schemes like
Almost Blank Sub frames (ABS).
The main idea behind ABS is to periodically mute the transmission of entire sub
frames from macro nodes. In this way, pico nodes affected by high interference
can serve their subscribers in these sub frames. However, users attached to macro
iii

iv Abstract
cells will be facing a degradation in downlink performance.
The main objective of this thesis was to introduce a new method to combat
uplink interference at pico level and in addition, to improve the downlink per-
formance for macro users. The focus of this new method was to tackle directly
the uplink problem without passing by CRE and ABS. The solution consists of
reserving a group of uplink sub frames during which the users attached to macro
nodes are partially muted allowing the micro nodes users to be served with lower
interference.
In order to validate the proposed technique and compare it with the available
methods, LTE-Sim [1] simulator was deployed, customized and enhanced to in-
clude all scenarios proposed in this thesis. Furthermore, a major contribution
to the system level simulation source code and mainly to the uplink part, was
a necessity in order to enhance the uplink algorithm available within LTE-Sim
and to have it aligned with the real and normal behavior of uplink in the LTE
standard.
Keywords: LTE, uplink interference, hetnets, fairness, throughput improve-
ment, performance evaluation.

Contents
Abstract iii
1 Introduction and Problem Statement 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Traﬃc Explosion . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.2 Approaches to meet increasing demand . . . . . . . . . . . 2
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Thesis Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 LTE - Long Term Evolution 5
2.1 LTE Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 LTE Cell Architecture . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 LTE Frame Structure . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3.1 Downlink Slot Structure . . . . . . . . . . . . . . . . . . . 7
2.3.2 Uplink Slot Structure . . . . . . . . . . . . . . . . . . . . . 8
3 HetNets Interference Management : Existing and New Proposed
Techniques 11
3.1 HetNets Description . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 HetNets Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3 HetNets Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 HetNets Interference Management Available Techniques . . . . . . 14
3.4.1 Cell Range Extension . . . . . . . . . . . . . . . . . . . . 14
3.4.2 Frequency Domain Multiplexing (Carrier Aggregation) . . 17
3.4.3 Time Domain Multiplexing (Almost Blank Sub Frames) . 18
3.4.4 First Commercialized eICIC . . . . . . . . . . . . . . . . . 19
3.5 Interference Management - Proposed Technique . . . . . . . . . . 19
4 Customizing System Level Simulator for both uplink and down-
link 21
4.1 Basic Simulation Tool . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1.1 Choice of LTE-Sim . . . . . . . . . . . . . . . . . . . . . . 21
4.2 LTE-Sim Optimization and Add-Ons . . . . . . . . . . . . . . . . 22
4.2.1 Implementation of a Basic Scenario . . . . . . . . . . . . . 22
4.2.2 Implementation of CRE Technique . . . . . . . . . . . . . 23
v

vi CONTENTS
4.2.3 Implementation of ABS Technique . . . . . . . . . . . . . 23
4.3 LTE-Sim - Major Contribution at Uplink Level . . . . . . . . . . 26
4.3.1 Signiﬁcant Change of SLS Uplink Algorithm . . . . . . . . 27
4.3.2 Implementation of the Proposed Technique . . . . . . . . 30
5 Simulations and Results 33
5.1 Simulation Input Parameters . . . . . . . . . . . . . . . . . . . . . 33
5.1.1 General Input Parameters . . . . . . . . . . . . . . . . . . 33
5.1.2 Selected Scheduling Algorithms . . . . . . . . . . . . . . . 34
5.1.3 Selected Propagation Loss Model . . . . . . . . . . . . . . 35
5.2 Simulation Output Performance Metrics . . . . . . . . . . . . . . 36
5.3 Simulation Scenarios and Results . . . . . . . . . . . . . . . . . . 37
6 Conclusions and Future Work 43
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Appendix 44
A LTE-Sim 45
A.1 About LTE-Sim . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
A.2 Running LTE-Sim simulator . . . . . . . . . . . . . . . . . . . . . 45
B Proportional Fair Scheduling 47
Bibliography 49

List of Figures
1.1 Key facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Key options to expand network capacity . . . . . . . . . . . . . . 3
2.1 Access via LTE macrocell nodes, picocell nodes, relay nodes, and
femtocell and HeNB nodes . . . . . . . . . . . . . . . . . . . . . . 6
2.2 LTE frame structures . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Physical downlink slot channel structure . . . . . . . . . . . . . . 8
2.4 LTE uplink subframe . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 Hetnet architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Heterogeneous network using pico-eNBs . . . . . . . . . . . . . . . 12
3.3 Cell selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Range extension area illustration . . . . . . . . . . . . . . . . . . 15
3.5 Range extension interference . . . . . . . . . . . . . . . . . . . . . 16
3.6 Illustration of eIIC based on carrier aggregation . . . . . . . . . . 17
3.7 ABS subframes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.8 Proposed solution-uplink subframes . . . . . . . . . . . . . . . . . 20
4.1 LTE-Sim release 5 add-ons . . . . . . . . . . . . . . . . . . . . . . 22
4.2 Uplink - Resource block level . . . . . . . . . . . . . . . . . . . . 30
5.1 Fast fading realization . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2 LTE-Sim traces example . . . . . . . . . . . . . . . . . . . . . . . 36
5.3 Scenario 1 - Downlink . . . . . . . . . . . . . . . . . . . . . . . . 37
5.4 Scenario 1 - Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.5 Scenario 2 - Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.6 Scenario 3 - Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . 40
vii

List of Tables
5.1 SLS, micro cell assumptions . . . . . . . . . . . . . . . . . . . . . 33
5.2 SLS, macro cell assumptions . . . . . . . . . . . . . . . . . . . . . 34
5.3 Implemented path loss models . . . . . . . . . . . . . . . . . . . . 35
ix

Chapter 1
Introduction and Problem
Statement
1.1 Introduction
1.1.1 Traffic Explosion
In recent years, mobile broadband traffic has grown exponentially, exceeding
voice, thanks to the new generation of mobile terminals, such as smart phones,
tablets and laptops, and to the new services and capabilities they offer. Mobile
users have also increased and, with them, the number of connections. Further-
more, cellular operators have in general reported non-uniform traffic distributions
in their networks, stating that for instance 50% of the total traffic volume is car-
ried on only 30% of the macro sites. Exact percentages of course vary from net-
work to network [2]. The required capacity has augmented faster than progress in
spectral efficiency. In addition, the service is migrating from a voice-centralized
model to a data centralized model. Subscribers use connected devices not only to
access the Internet, but also to access applications and cloud-based services, in-
cluding video and other bandwidth-intensive content. As a result of these trends,
overall mobile data traffic is expected to grow tenfold by 2016 [3].
Figure 1.1: Key facts
1

2 1.1 Introduction
Users´ expectations for mobile broadband are growing parallel to traffic and,
increasingly, users expect a robust, high-quality and seamless service. Further,
more and more, customers are operating inside offices and buildings, where about
70 percent of today´ s data traffic is generted and where coverage represents a
major problem for mobile operators. Meet the demand for mobile broadband is
specially challenging in certain scenarios; such as:
- Large outdoor hotspots with high traffic demand and a dense macro network,
implying high interference. E.g., town squares and commercial streets.
- Large, isolated indoor hotspots, which may be difficult to reach from an outdoor
macro network. E.g., businesses and hotels.
- Large indoor hotspots, where mobility demands and interference are high. E.g.,
shopping centers, airports and subway stations.
- Localized, indoor hotspots or minor coverage holes, which represent a challenge
of implementation and cost to conventional cellular networks. E.g., small offices
and restaurants.
In order to meet the growing demand for mobile broadband and users´ expec-
tations, it is necessary improve data performance overall and at cell edges, and,
to achieve this, more resources are needed and also new ways of acquiring, de-
ploying, managing and optimizing these resources. Broadband services providers
use a variety of technologies in order to meet customers´ expectations; namely,
improve the existing network, densify current macro cells and, the most impor-
tant one, add small cells to improve coverage, capacity and power signal when
necessary.
1.1.2 Approaches to meet increasing demand
Traditionally, the design adopted for the implementation of wireless cellular net-
works is a homogenous approach. A homogeneous cellular system is a network of
base stations in a planned layout and a collection of user terminals, in which all
the base stations have similar transmit power levels, antenna patterns, receiver
noise floors, and similar backhaul connectivity to the (packet) data network [4].
In these kind of networks, the locations of the macro stations has to be carefully
planned and the settings of each station should be properly configured in order
to maximize the coverage and control the interference between adjacent stations.
As the traffic demand grows dramatically, spectral efficiency in point-to-point
wireless networks is reaching its theoretical limit and the capacity of the current
network is not enough. Therefore, it is necessary to find solutions; that is to say,
improve network capacity, to maintain uniform user experience and, thereby, cus-
tomers satisfied. There are several approaches that can be taken to meet traffic
and data rate demands. On a high level, the key options to expand network ca-
pacity include improving and densifying the macro layer and complementing the
macro layer with low power nodes, thereby creating a heterogeneous network[5].

Chap. 1 Introduction and Problem Statement 3
Figure 1.2: Key options to expand network capacity
Improving the existing network consists in enhancing current macro cells. So
that it is necessary provide them with more spectrum, advanced antennas with
increased order of diversity, and advanced baseband processing capacity within
and between nodes. Although the improvement achieved could not be enough
at some point, increasing capacity and data rates in this way is an attractive
solution as it avoids installing new sites. Due to large attenuation between ter-
minals and the base station caused by the distance and the radio propagation,
the received power is relatively low. That is known as power limitation and is the
cause of limited data rates. Densifying the macro network consists in reducing
the coverage area of each cell and increasing the total number of macro-cell sites;
that is, adding cells strategically located. As a result, the traffic per square meter
can be increased and the distance between the base station and the terminal will
be shorter, implying an improvement in achievable data rates.
The densification, if the number of macro stations is low, keeps the number
of sites relatively low while network performance becomes less sensitive to traf-
fic location; all this, without severely increasing the interference between cells.
However, in deployments already presenting high density of macro stations, the
densification of the network may be limited by a high inter-cell interference and, in
dense urban areas, site acquisition for macro base stations with towers is quite dif-
ficult. Although these two strategies, improve and densify, may slightly enhance
the capacity of the network, nowadays wireless cellular systems have evolved to
the point where an isolated system, with just one macro base station, achieves
near optimal performance. Consequently, future gains of wireless networks will
be obtained from a more flexible and advanced deployment model, which will
allow improving broadband user experience everywhere and in a cost effective
way. This new deployment model is based on densifiyng the current network
with complementary low power nodes; that is to say, adding small cells to create
a heterogeneous network (HETNET).

4 1.2 Problem Statement
1.2 Problem Statement
The sudden increase in demand for mobile broadband services all around the
world (videos, audio, online TV and other streaming services, real time multi-
media services, etc.) that takes place nowadays, puts mobile network operators
and researchers in a continued case of working, researching and developing to
meet users requirements, improve services quality and coverage, increase data
rates and capacity, increase overall cell-site performance and cell-edge data. To
deal with this issue, denser topologies (HetNets), which was included in 3GPP
LTE Release 10 as one of the new features to meet these advanced requirements.
However, the main issue was to manage the interference between macro and pico
layers , to improve users throughput on both downlink and uplink and to fairly
distribute these resources between existing users. The main problem that results
from the cell selection procedure is the heavy uplink interference at pico node
level caused by edge users attached to macro nodes. As a solution to this issue,
CRE (cell range expansion) and ABS (almost blank subframes) techniques were
introduced together as a part of LTE advanced enhancements. CRE and ABS
techniques will be discussed in details in the next chapters.
This master thesis investigates the LTE Release 10 enhancements (including
inter-cell interference coordination (ICIC)) and how the CRE and ABS can be
replaced by a single solution in a way to solve the uplink interference issue at
pico level without deploying any of these techniques. Detailed description about
the thesis´ proposed solution will be available in the next chapters
1.3 Thesis Scope
This thesis is organized in 6 chapters. The rest of the chapters are organized as
follows:
Chapter 2 gives an overview of LTE evolution, cell architecture and frame struc-
ture. Chapter 3 discusses HetNets comprising concepts, challenges, interference
management available and new proposed techniques. Chapter 4 describes the
customizations applied on top of the system level simulator to implement exist-
ing and proposed solutions, in addition to a major contribution at uplink level.
Chapter 5 analyses macro-micro scenarios with CRE/ABS and compares the re-
sults with the proposed scenario. Finally chapter 6 draws the conclusion and
gives recommendations for future works.

Chapter 2
LTE - Long Term Evolution
2.1 LTE Evolution
Through the past few years the mobile broadband technology was released making
it possible for applications such as live streaming, online gaming and mobile TV
to be used on mobile handsets. However, the data rate requirements for these
applications have grown exponentially. The Third-generation Partnership Project
(3GPP)started working on solutions to fulfill the need for high data rates and
came up with HSPA which is currently used in 3G phones for the before mentioned
applications.
In order to ensure the competitiveness of its standards in the future, 3GPP
developed the Long Term Evolution (LTE) to be the 4thgeneration of mobile
telephony. LTE as defined by the 3GPP [6] is the evolution of the 3rdgeneration
of mobile communications (UMTS). The main goal of LTE is to introduce a new
radio access technology with a focus on high data rates, low latency and packet
optimized radio access technology, LTE is also referred to as E-UTRAN (Evolved
UMTS Terrestrial Radio Access Networks).
In December 2008, the LTE specification was published as part of Release 8
and the first implementation of the standard was deployed in 2009. The first
release of LTE, namely release 8, supports radio network delay less than 5ms
and multiple input multiple output (MIMO) antenna techniques which allow
achieving very high data rates. Later on in December 2009 release 9 has been
introduced with extensions to various features that existed in release 8 such as
Closed Subscriber Group (CSG) and Self Organizing Network (SON). It added
also new features such as Location Services (LCS) and Multimedia Broadcast
Multicast Services (MBMS). Finally release 10 has been introduced in March
2011 which is also called LTE-Advanced and it added new features such as carrier
aggregation, relaying and heterogeneous deployments which will be all discussed
in details later.
5

6 2.2 LTE Cell Architecture
2.2 LTE Cell Architecture
The below figure 2.1 shows a generic LTE architecture. The access network of
LTE, called E-UTRAN, consists of a network of eNodeBs connected via different
interfaces. In LTE, eNodeBs are normally inter-connected with each other by
means of an interface called X2 and to the core network through an interface called
S1. Although 3GPP-LTE employs a flat architecture, for purposes of exposition,
cells can be classified in terms of their transmission powers, antenna heights, the
type of access mechanism provided to users, and the backhaul connection to other
cells.
Figure 2.1: Access via LTE macrocell nodes, picocell nodes, relay nodes, and
femtocell and HeNB nodes
Macrocells cover a large cell area (typical cell radius being of the order of
500 meters to a Kilometer), with transmit antennas above the clutter and trans-
mission power of the order of 46 dBm (20 watts). They provide service to all
users.
Femtocells, also called Home eNodeBs (HeNBs) are lower power cells installed
(typically indoors) by the end-consumer. HeNB access is classified either as a
closed, hybrid or open access type. A closed access HeNB maintains a Closed
Subscriber Group (CSG) white-list where access is limited only to subscribed
users (i.e. to UEs that are members of the CSG). Hybrid access HeNBs allow
limited access to non-subscribed UEs, but provide differentiated higher quality of
service to CSG users. Open access HeNBs provide undifferentiated access to all
UEs. In Rel-10, X2 interface is used between open access HeNBs and between
closed/hybrid access HeNBs with identical CSG IDs and between closed/hybrid
HeNBs and open access HeNBs.
Picocells are operator deployed cells, with lower transmission powers - typi-
cally an order of magnitude smaller - relative to macrocell eNodeBs. They are
installed typically in wireless hotspot areas (for example, malls) and provide ac-
cess to all users. Relay Nodes are operator deployed and are primarily used to
improve coverage in new areas (e.g. events, exhibitions etc.). Unlike HeNBs and

Chap. 2 LTE - Long Term Evolution 7
picocells which connect to the macrocell over X2 backhaul, relay nodes backhaul
their traffic through a wireless link to a Donor eNodeB. In band relays use the
same frequency of operation over their backhaul link as the access (relay-UE)
links. Out band relays, on the other hand, use different spectrum over backhaul
and access link.
2.3 LTE Frame Structure
2.3.1 Downlink Slot Structure
Downlink transmissions are organized in (radio) frames of length 10 ms which,
in turn, are divided into 10 equally sized sub frames of 1ms duration each. As
illustrated in Figure 2.2, each sub frame consists of 2 time slots of length Tslot=0.5
ms, where each time slot consists of a number of OFDM symbols including cyclic
prefix.
Figure 2.2: LTE frame structures
In 3GPP-LTE, downlink transmissions employ orthogonal frequency division
multiple access (OFDMA) transmission. The basic unit of transmission is a down-
link sub frame, defined by two slots, each having duration of 0.5 milli-seconds.
The transmitted signal (Figure 2.3) over each antenna in each slot is described by
N (lying between 6 and 110) physical resource blocks (PRBs) which defines the
mapping of certain physical channels to resource elements. Each physical resource
block (PRB) occupies one slot (0.5 ms) in time and 180 KHz (12 subcarriers) in
frequency. A downlink sub frame therefore comprises of N concatenated PRB
pairs. Cell-Specific Reference Signals (CRS) are pilot signals, essential for down-
link demodulation, mobility measurements and to acquire channel-state informa-
tion (CSI). The control payload for all users is carried via the Physical Downlink
Control Channel (PDCCH) whose duration is conveyed via the Physical Control
Format Indicator Channel (PCFICH). The control payload conveys the RB as-
signment for the uplink information data (Physical Uplink Shared Channel or

8 2.3 LTE Frame Structure
Figure 2.3: Physical downlink slot channel structure
PUSCH) and downlink information data (Physical Downlink Shared Channel or
PDSCH), while hybrid automatic repeat-request acknowledgments for PUSCH
are carried by the PHICH signal. While the PDCCH is transmitted across the
entire bandwidth in the ﬁrst slot of each sub frame, the PDSCH signal is trans-
mitted - on speciﬁc PRB locations signaled via PDCCH - to scheduled user(s).
2.3.2 Uplink Slot Structure
Uplink Physical Channels
Physical Uplink Control Channel (PUCCH) The PUCCH carries uplink
control information and is never transmitted simultaneously with PUSCH data.
PUCCH conveys control information including channel quality indication (CQI),
ACK/NACK responses of the UE to the HARQ mechanism, and uplink schedul-
ing requests.
Physical Uplink Shared Channel (PUSCH) Resources for the PUSCH
are allocated on a sub-frame basis by the UL scheduler. Subcarriers are allocated
in multiples of 12 because, as stated earlier, a Resource Block has 12 subcarriers.
Subcarriers may be hopped from sub-frame to sub-frame. The PUSCH may
employ QPSK, 16-QAM, or 64-QAM modulation.
Physical Random Access Channel (PRACH) The PRACH carries the
random access preamble and coordinates and transports random requests for ser-
vice from mobile devices. The PRACH channel transmits access requests (bursts)

Chap. 2 LTE - Long Term Evolution 9
when a mobile device desires to access the mobile system (call origination or pag-
ing response).
Figure 2.4: LTE uplink subframe

Chapter 3
HetNets Interference
Management : Existing and New
Proposed Techniques
3.1 HetNets Description
HetNets involve the use of different types of radio technology and employ low
power nodes working together with the current macro cells; that is to say, they
may coexist in the same geographical area sharing the same spectrum, so it is
not necessary that they provide full area coverage. For this reason, while the
location of the macro stations is generally carefully planned, the low power nodes
are typically deployed in a relatively unplanned manner. Usually, the main aim
of low power nodes is to eliminate coverage holes in the macro network, improve
capacity in hot-spots and improve cell edge throughput; that is why, the location
chosen for their deployment is based on the knowledge of coverage issues and
traffic density in the network
Figure 3.1: Hetnet architecture
Deploying low power nodes can be challenging, as performance depends on
close proximity to where traffic is generated and, due to their reduced coverage
range, a lot of them may be needed. Nevertheless, owing to their lower transmit
11

12 3.2 HetNets Motivation
power and smaller physical size, low power stations can offer flexible site acquisi-
tions. Furthermore, HETNETS allow improving spectral efficiency per unit area
and offer very high capacity and data rates in areas covered by the low power
nodes. Therefore, it is an attractive solution in scenarios where users are highly
clustered.
3.2 HetNets Motivation
The concept of Heterogeneous networks has attracted a lot of interest recently to
optimize the performance of the network. Spectral efficiency of current systems
like WCDMA and LTE is approaching theoretical boundaries [7], we can see that
from the fact that LTE release 8 does not offer a lot of improvements in terms
of spectral efficiency compared to UMTS, instead LTE improves system perfor-
mance by using more spectrum and since spectrum has been a scarce resource in
the past few years a different approach must be considered to improve network
performance. The main approach to enhance the performance is to improve the
network topology. This is done in the scenario of Heterogeneous networks by
overlaying the planned network of high power Macro base stations with smaller
low power Pico base stations that are distributed in an unplanned manner or
simply in hotspots where a lot of traffic is generated. These deployments can
improve the overall capacity and the cell edge users´ performance. [8]
Figure 3.2: Heterogeneous network using pico-eNBs
Properties of Pico base stations:
1. They have a transmission power of 1W.
2. They can be deployed to eliminate coverage holes.
3. Offer high data rate and capacity where they are deployed.
4. Offloading the Macro-eNBs by serving some users that used to belong to
the Macro-eNBs, which allows the Macro-eNB to serve better its users.

Chap. 3 HetNets Interference Management : Existing and New Proposed Techniques 13
5. Due to their low transmission power and small physical size they can offer
flexible site acquisitions.
3.3 HetNets Techniques
As stated in the previous section, HETNETS, that is to say, the mix of different
types of radio technology and the use of macro cells with low power nodes working
together, provide important improvements in terms of increased data rates and
cell coverage. It should be emphasized that HETNETS can be deployed with the
existing technologies (e.g. HSPA, LTE Release 8, 9, 10) and without standards
changes, although they will enable further range expansion, concept that will be
further explained below. In this section, the main technical challenges for HET-
NETS deployment will be discussed as well as server selection and coordination
between cells strategies when the radio access technology used is LTE, which is
the most used in HETNETS.
In conventional single layer networks, in other words, homogeneous networks,
each mobile terminal connects to the node from which the downlink (DL) signal
strength is the strongest, while the undesired signals from the other base stations
are treated as interference. Although this gives the optimum server selection
methodology for these networks, it is not the best strategy for HETNETS since
this principle can lead to sub-optimal performance. Therefore, resources coordi-
nation between base stations and server selection strategies, as well as advanced
techniques for efficient interference management, are very important issues in
HETNETS, needed in order to achieve gains in throughput and user-experience.‘
Cell Selection A terminal, before being able to receive or transmit data, must
perform certain steps, such as searching the possible cells to which it can be
connected, selecting the suitable oneand deriving some system information.
Figure 3.3: Cell selection
The cell search procedure that a User Equipment (UE) wishing to access the
LTE system follows includes a series of synchronization stages by which the UE
determines time and frequency parameters that are necessary to demodulate DL

14 3.4 HetNets Interference Management Available Techniques
signals, to transmit with correct timing and to acquire some critical system pa-
rameters. There are three synchronization requirements in LTE: symbol timing
acquisition, carrier frequency synchronization, and sampling clock synchroniza-
tion. There are two cell search procedures in LTE, one for initial synchronization
and another for detecting neighbor cells in preparation for handover. In both
cases, the UE uses two special signals broadcast on each cell: Primary Synchro-
nization Sequence (PSS) and Secondary Synchronization Sequence (SSS). The
detection of these signals allows the UE to complete time and frequency syn-
chronization and to acquire useful system parameters such as cell identity, cyclic
prefix length, and access mode (FDD/TDD). Once UE knows the physical cell id
(PCI) for a given cell, it also knows the location of cell reference signals that are
used in channel estimation, cell selection / reselection and handover procedures.
In homogeneous deployments, where all the cells transmit on similar power
levels, the cell selection process is based on the comparison of the Reference Signal
Received Power1
(RSRP) and the Reference Signal Received Quality (RSRQ) of
downlink signaling transmitted from neighboring cells. That is to say, each UE
selects its serving cell ID according to the cell from which the largest RSRP is
provided [9]:
CellIDserving = argmaxi(RSRPi) (3.1)
3.4 HetNets Interference Management Available
Techniques
3.4.1 Cell Range Extension
Range Extension Introduction
Cell selection in LTE is based on terminal measurements of the received power of
the downlink signal or more specifically the cell specific reference (CRS) down-
link signaling. However; in a heterogeneous network we have different types of
base stations that have different transmission powers including different powers
of CRS. This approach for cell selection would be unfair to the low power nodes
(Pico-eNBs) as most probably the terminal will choose the higher power base
stations (Macro-eNBs) even if the path loss to the Pico-eNB is smaller and this
will not be optimal in terms of:
-Uplink coverage: as the terminal has a lower path loss to the Pico-eNB but
instead it will select the Macro-eNB.
-Downlink capacity: Pico-eNBs will be under-utilized as fewer users are connected
to them while the Macro-eNBs could be overloaded even if Macro-eNBs and Pico-
1
RSRP is defined as the linear average over the power contributions (in [W]) of the resource
elements that carry cell-specific reference signals within the considered measurement frequency
bandwidth.

eNBs are using the same resources in terms of spectrum, so the cell-splitting gain
is not large and the resources are not well utilized.
-Interference: due to the high transmission power of the Macro-eNBs, then the
Macro-eNB transmission is associated with a high interference to the Pico-eNB
users which denies them to use the same physical resources.
As a solution for the first 2 points cell selection could be dependent on estimates
of the uplink path loss, which in practice can be done by applying a cell-specific
offset to the received power measurements used in typical cell selection. This
offset would somehow compensate for the transmitting power differences between
the Macro-eNBs and Pico-eNBs; it would also extend the coverage area of the
Pico-eNB, or in other words extend the area where the Pico-eNB is selected. This
area is called ”Range Extension” and is illustrated in Figure 3.4.
Figure 3.4: Range extension area illustration
Range Extension Advantages
1. Applying range extension would maximize the achievable uplink SINR which
in turn maximizes the uplink data rate.
2. The terminal transmit power would be reduced as the path loss to the Pico-
eNB is lower than the one to the Macro-eNB so the interference to other
cells would be reduced and the uplink system efficiency would be improved.
3. It also allows more users to be connected to the Pico-eNB, thus increasing
the cell splitting gain.
4. Since the Macro-eNB transmits to fewer users then the interference it ap-
plies on the Pico-eNB is reduced and the Pico-eNBs can reuse the resources
more efficiently so the downlink system efficiency is maximized as well.
Interference Effects Associated to Range Extension
Due to the difference in transmission powers of the Macro-eNBs and the Pico-
eNBs, in the range extension area, illustrated in Figure 3.4, where the Pico-eNB

is selected by the terminal while the downlink power received by that terminal
from the Macro-eNB is much higher than the power it receives from the Pico-
eNB, this makes the users in the range extension area more prone to interference
from the MacroeNB.
So along with the benefits of range extension comes the disadvantage of the
high inter-cell interference that the Macro layer imposes on the users in the range
extension area of the Pico layer. Figure 3.5 illustrates the comparison of 2 users
connected to the Pico-eNB where:
-User 1 is placed close to the Pico-eNB so we will call it ”center Pico user”, this is
not affected very much by the Macro-eNB interference as the downlink received
power from the Pico-eNB is higher than the one received from the Macro-eNB.
-User 2 is placed farther from the Pico-eNB, in the range extension area, and as
discussed before this user endures a severe interference from the Macro-eNB.
Solutions for the high interference levels in the range extension area will be dis-
cussed in the next section.
Figure 3.5: Range extension interference
Inter-cell Interference Available Solutions
The enhanced Inter-Cell Interference Coordination (eICIC) in heterogeneous net-
works introduced in LTE-Advanced has been a hot topic lately as without an
efficient inter-cell interference scheme the range extension concept loses its ad-
vantage and efficiency. The problem with ICIC schemes in releases 8 and 9 was
that they were only considering data channels and did not focus on the interfer-
ence between control channels, so LTE release 10 solves this problem with the
solutions in the following subsections.
The solutions are mainly divided into frequency domain solutions such as carrier
aggregation and time domain solutions such as almost blank sub frames (ABS),
and they will be discussed in details in the following.

3.4.2 Frequency Domain Multiplexing (Carrier Aggrega-
tion)
The main FDM interference cancellation method used in LTE-Advanced is ”car-
rier aggregation”.This feature is one of the most important features of LTE Ad-
vanced and it basically enables an LTE-Advanced user equipment (UE) to be
connected to several carriers simultaneously. Carrier aggregation not only allows
resource allocation across carriers but also allows scheduler based fast switching
between carriers without time consuming handovers, which means that a node
can schedule its control information on a carrier and its data information on an-
other carrier. An example of that concept in a HetNet scenario is to partition
the available spectrum into, for example, 2 separate component carriers, and as-
sign the primary component carrier (f1) and the second component carrier (f2)
to diﬀerent network layers at a time as shown in Figure 3.6.
Figure 3.6: Illustration of eIIC based on carrier aggregation
In the example we have 2 component carriers f1 and f2 where 5 sub frames
are shown in each carrier. There are 2 cases, the case of Macro layer usage and
the case of Pico layer usage; the sub frames are distributed in control part, the
blue part, and data part. The control part in the example only illustrates the
PDCCH, PCFICH and PHICH at the beginning of the sub frames.
As shown Figure 3.6, the Macro layer can schedule its control information on
f1 but can still schedule its users on both f1 and f2 so by scheduling control and
data information for both Macro and Pico layers on diﬀerent component carriers,
interference on control and data can be avoided. It is also possible to schedule
center Pico-eNB users data information on the same carrier that the Macro layer
schedules its users as shown in the third subframe in Figure 3.6, as the inter-
ference from the Macro layer on center Pico-eNB users can be tolerated, while
Pico-eNB users in the range extension areas are still scheduled in the other carrier
where the Macro-eNB users are not scheduled.
The disadvantage of carrier aggregation with cross carrier scheduling is that
it is only supported by release 10 terminals and onwards so this feature cannot
be used by release 8 and 9 terminals.

3.4.3 Time Domain Multiplexing (Almost Blank Sub Frames)
When the sub-frames of the macro station and low power nodes are aligned,
their control and data channels overlap with each other. The basic idea with
time domain eICIC in order to coordinate inter-cell interference and protect con-
trol channels is that an aggressor layer creates protected sub-frames for a victim
layer by reducing its transmission activity in certain sub-frames. Whereby, trans-
missions of the victim users are scheduled in time domain resources where the
interference from other nodes is mitigated. The concept relies on accurate time
and phase synchronization on sub-frame resolution between all base station nodes
within the same geographical area. This approach involves periodically muting
the transmissions of entire sub-frames from nodes that cause harmful interference
onto others; in this way, nodes affected by high interferences can serve their sub-
scribers in these sub-frames.
In the case of a macro-pico scenario, users served by the low power node who
are located in the range expansion zone observe large DL interference from the
macro cell. In order to protect the DL control signaling from the low power node,
macro transmission activity is reduced in certain sub-frames, which are called
almost blank sub-frame (ABS) and range expanded pico cell users are scheduled
within sub-frames that overlap with the ABSs of the macro cell. The low power
node is provided with data about the set of protected sub-frames over the X2
interface, which connects the macro cell with low power nodes [10]. Therefore
low power users are categorized into 2 groups: Users in the range extension area,
who suffer from a high level of interference and are only served during the ABS,
and users that are located closer to the low power node, so they are not heavily
affected by the interference from the macro, and can be served by any sub-frame
whether ABS or non-ABS.
In a macro-femto scenario, the situation is very similar, but, in this case, ABS
are used at low power nodes in order to mitigate the interference in control chan-
nels of users served by the macro stations; in other words, on this occasion, the
transmission activity that is muted is of the femto nodes. Macro cells users in the
vicinity of a small cell can be scheduled within the sub-frames overlapping with
the ABSs of the femto nodes, which significantly mitigate cross- tier interference
[11].
For backward compatibility, certain signals must be transmitted in all DL
sub-frames even if they should be muted. Therefore, an ABS is characterized by
minimum transmission, but not completely null; that is why these sub-frames are
called almost blank. No control or data signals are transmitted in ABSs, only
reference signals are sent, namely:
- Common reference signals (CRS)
- Primary and secondary synchronization signals (PSS and SSS)
- Physical broadcast channel (PBCH)

Figure 3.7: ABS subframes
- System Information Block-1 (SIB-1) and paging with their associated physical-
downlink control channel (PDCCH)
3.4.4 First Commercialized eICIC
28 Jan 2015: SK Telecom, Nokia claim world’s first commercialized
eICIC
SK Telecom and Nokia Networks have announced what they called the world’s
first commercialized Enhanced Inter-Cell Interference Coordination (eICIC) tech-
nology, which is designed to enhance LTE-A network quality.
South Korea’s largest wireless operator by subscribers, SK Telecom (SKT) has
launched commercially what it claims is the world’s first LTE-Advanced (LTE-A)
service available through smartphones. Noting that the technology allows it to
offer downlink speeds of up to 150Mbps, SKT said that in order to commercial-
ize LTE-A it had successfully developed and applied the most-advanced mobile
network technologies, specifically noting that it had applied Carrier Aggregation
(CA) and Coordinated Multi Point (CoMP) software; it also has plans to apply
Enhanced Inter-Cell Interference Coordination (eICIC) in 2015.
3.5 Interference Management - Proposed Tech-
nique
The main challenge in this thesis was to reduce uplink interference at pico node
level caused by edge users attached to macro nodes, without applying CRE and
thus the ABS techniques. Hence, we have proposed to mute a group of uplink
sub frames during which the users attached to macro nodes are muted.(figure

20 3.5 Interference Management - Proposed Technique
3.8) This will allow the micro nodes’ users, using the uplink channel, to be served
with lower interference.
Figure 3.8: Proposed solution-uplink subframes
Motivation
In this proposal, we have tackled directly the uplink problem however in the
existing solutions , two techniques (CRE and ABS) have been used to solve the
same problem. Moreover, muting the uplink for macro users is not as much
critical as muting the downlink for these users and this is mainly because, in
uplink, we had fewer constraints, less traﬃc and low priority applications will be
aﬀected.

Chapter 4
Customizing System Level
Simulator for both uplink and
downlink
4.1 Basic Simulation Tool
In this thesis, System level simulator was needed to evaluate the impact of Het-
Nets deployment and the performance of both micro and macro users. It also
reflects the effects of issues that are unavailable to study in link level simula-
tion, such as cell planning, scheduling and interference. In addition, this type of
simulator was essential to study and analyze effects of applying CRE, ABS and
compare the results with the proposed solution.
The LTE SLS that has been used in this research is LTE-Sim simulator. LTE-
Sim is an open source framework to simulate LTE networks including downlink
and uplink scenarios at system level. More details about LTE-Sim have been
included in appendix A.1 and A.2.
4.1.1 Choice of LTE-Sim
LTE-Sim has been selected as a main simulator in this thesis for the below reasons:
1. LTE-Sim has been written in C++, using the object-oriented paradigm,
as an event-driven simulator in order to ensure modularity, polymorphism,
flexibility, and high performance.
2. LTE-Sim is an open source framework so it is easy to apply changes at the
core or simulation level.
3. It is a system level simulator where we can manage Hetnets’ interference
which is the main purpose of this thesis.
4. It includes basic downlink and uplink scenarios.
21

22 4.2 LTE-Sim Optimization and Add-Ons
5. It supports heterogeneous environments (macro with pico nodes), handover
procedures and includes different types of DL/UL schedulers.
4.2 LTE-Sim Optimization and Add-Ons
Figure 4.1 illustrates all changes applied on top of LTE-Sim release 5, in a way
to fit our proposal’s requirements. All details and changes performed at source
code level will be described in the next sections.
Figure 4.1: LTE-Sim release 5 add-ons
4.2.1 Implementation of a Basic Scenario
In order to start with a basic macro-micro scenario, a micro cell and a new type
of eNodeB must be defined in NetworkNode::NodeType as follow:
enum NodeType
{
TYPE_ENODEB,
//TYPE_MICROENODEB,
}
Moreover, a new channel realization class must be created to define and cal-
culate the channel propagation loss within the micro area. This class is named
MicroCellUrbanAreaChannelRealization and inherited from ChannelRe-
alization basic class.
The below code will be executed once creating a micro channel within Chan-
nelRealization* CreateChannelRealization static function under channel-
realization-helper.h

Chap. 4 Customizing System Level Simulator for both uplink and downlink 23
else if (src->GetNodeType()==NetworkNode::TYPE_MICROENODEB ||
dst->GetNodeType()==NetworkNode::TYPE_MICROENODEB)
{
MicroCellUrbanAreaChannelRealization* c =
new MicroCellUrbanAreaChannelRealization (src, dst);
return c;
}
4.2.2 Implementation of CRE Technique
In PowerBasedHoManager::CheckHandoverNeed under src/protocolStack/rrc/ho,
add a bias to the received power and apply the below change:
#define BIAS 12; // dB
if((*it)->GetNodeType() == NetworkNode::TYPE_MICROENODEB) RXpower+= BIAS;
if (RXpower > targetRXpower)
{
if (NetworkManager::Init()-
>CheckHandoverPermissions(probableNewTargetNode,ue))
{
targetRXpower = RXpower;
targetNode = probableNewTargetNode;
}
}
4.2.3 Implementation of ABS Technique
In TDDConﬁguration.h deﬁne a static int vector for the kind of the TTI
(1=ABF, 0 = not-ABF).
static int ABSConfig[1][10] = {
{
0, 1, 0, 1, 0, 1, 0, 1, 0, 1
},
}; // 1=ABS, 0=NOT ABS
Add FrameManager::GetSubFrameABSType function to get ABS sched-
uler:

24 4.2 LTE-Sim Optimization and Add-Ons
int FrameManager::GetSubFrameABSType(int nbSubFrame) {
return ABSConfig[0][nbSubFrame - 1];
}
In FrameManager::ResourceAllocation() apply these changes:
if (GetFrameStructure () == FrameManager::FRAME_STRUCTURE_FDD)
{
if((record->GetNodeType() == NetworkNode::TYPE_MICROENODEB) &&
GetSubFrameABSType(GetNbSubframes()) == 1)
{
Simulator::Init()->Schedule(0.0, &ENodeB::DownlinkResourceBlokAllocation,record);
}
{
}
if((record->GetNodeType() == NetworkNode::TYPE_ENODEB) &&
{
}
{
}
CQI Feedback and Interference Optimization
In ENodeB.h you have to extend the UserEquipmentRecord structure by
duplicating the std::vector¡int¿ mcqiFeedback vector and its reading/writing
methods.
class ENodeB : public NetworkNode {
public:
struct UserEquipmentRecord
{
std::vector<int> m_cqiABSFeedback;
void SetCQIabs (std::vector<int> cqiABS);
std::vector<int> GetCQIabs (void) const;

Initialize the vector and deﬁne the methods in EnodeB.cpp
ENodeB::UserEquipmentRecord::UserEquipmentRecord ()
{
m_UE = NULL;
//Create initial CQI values:
m_cqiFeedback.clear ();
m_cqiABSFeedback.clear ();
ENodeB::UserEquipmentRecord::UserEquipmentRecord (UserEquipment *UE)
{
m_UE = UE;
m_cqiFeedback.clear ();
m_cqiABSFeedback.clear ();
BandwidthManager *s = m_UE->GetPhy ()->GetBandwidthManager ();
int nbRbs = s->GetDlSubChannels ().size ();
for (int i = 0; i < nbRbs; i++ )
{
m_cqiFeedback.push_back (10);
m_cqiABSFeedback.push_back (10);
}
void
ENodeB::UserEquipmentRecord::SetCQIabs (std::vector<int> cqiABS)
{
m_cqiABSFeedback = cqiABS;
}
std::vector<int>
ENodeB::UserEquipmentRecord::GetCQIabs (void) const
{
return m_cqiABSFeedback;
}
Modify the EnbMacEntity::ReceiveCqiIdealControlMessage method for
the feedback CQI manager. You can set by using: record.SetCQI (cqiFeed-
back) Insert if statement on the node (enb.GetTypeNode()). If the node is
a micro cell, you have to save the CQI vector:
if (enb->GetNodeType() == NetworkNode::TYPE_ENODEB)
{
record->SetCQI (cqiFeedback);
}
else if (enb->GetNodeType() == NetworkNode::TYPE_MICROENODEB)
{
if (FrameManager::Init()->GetSubFrameABSType(FrameManager::Init()-
>GetNbSubframes()) == 1) record->SetCQIabs (cqiFeedback);

26 4.3 LTE-Sim - Major Contribution at Uplink Level
else if (FrameManager::Init()->GetSubFrameABSType(FrameManager::Init()-
>GetNbSubframes()) == 0) record->SetCQI (cqiFeedback);
}
In void DownlinkPacketScheduler::SelectFlowsToSchedule () get the CQI
vector by adding a if statement in order to select the correct CQI vector:
{
cqiFeedbacks = ueRecord->GetCQI ();
} else if (enb->GetNodeType() == NetworkNode::TYPE_MICROENODEB)
{
if (FrameManager::Init()->GetSubFrameABSType(FrameManager::Init()-
>GetNbSubframes()) == 1) cqiFeedbacks = ueRecord->GetCQIabs ();
else if (FrameManager::Init()->GetSubFrameABSType(FrameManager::Init()-
>GetNbSubframes()) == 0) cqiFeedbacks = ueRecord->GetCQI ();
}
In Interference::ComputeInterference, macro users in muted sub frames
should not be included in the interference calculation :
for (it = eNBs->begin (); it != eNBs->end (); it++)
{
node = (*it);
if (((node->GetNodeType() == NetworkNode::TYPE_MICROENODEB) &&
FrameManager::Init()->GetSubFrameABSType(FrameManager::Init()->GetNbSubframes())
== 1) || ((node->GetNodeType() == NetworkNode::TYPE_MICROENODEB) &&
== 0) || ((node->GetNodeType() == NetworkNode::TYPE_ENODEB) &&
== 0))
{
4.3 LTE-Sim - Major Contribution at Uplink
Level
Since the uplink in LTE-sim was very basic (relying on downlink CQI feedback)
and not aligned with the real and the correct uplink behavior in lte environment,
the most important part in this thesis was to enhance the uplink scheduler in
LTE-Sim to be based on uplink channel quality and also to include uplink inter-
ferences at each resource block.
Hence, it was very critical to add this part since the purpose of all this pro-
posal is to improve uplink channel quality and mitigate uplink interference at
pico level. Details about the uplink optimization process will be included in the
next section.

4.3.1 Signiﬁcant Change of SLS Uplink Algorithm
The uplink process has been optimized by applying changes in LTE-Sim source
code at diﬀerent levels on each transmission time interval (TTI).
First, add uplink interference object to EnbLtePhy::EnbLtePhy() function :
Interference *interference = new Interference ();
SetInterference (interference);
Initialize a new variable under the structure EnodeB:UserEquipmentRecord
called std::vector int mcqFeedback to store the uplink channel quality:
std::vector<int> m_cqFeedback;
void SetCQ (std::vector<int> cq);
std::vector<int> GetCQ (void) const;
Add NetworkNode* src to EnbLtePhy::StartRx function to pass the
UE(user equipment) object and apply the following change to include the uplink
interference per RB and thus calculate the uplink channel quality per RB for each
pair of (enb,Ue) :
if (GetInterference () != NULL)
{
interference = GetInterference ()->
ComputeENodebInterference ((ENodeB*) GetDevice (),chId);
}
else
{
interference = 0;
}
noise_interference = 10. * log10 (pow(10., NOISE/10) + interference);
// dB
measuredSinr.push_back (power - noise_interference);
chId++;
}
AMCModule *amc = src->GetProtocolStack ()->GetMacEntity ()->GetAmcModule ();
std::vector<int> cqi = amc->CreateCqiFeedbacks (measuredSinr);
UserEquipment* ue = (UserEquipment*) src;
ENodeB* enb = (ENodeB*) GetDevice ();
ENodeB::UserEquipmentRecord* record = enb->
GetUserEquipmentRecord (ue->GetIDNetworkNode ());
if (record != NULL)

{
record->SetCQ (cqi);
}
Including uplink interference was done by creating a new function called Inter-
ference:ComputeENodebInterference:
double Interference::ComputeENodebInterference (ENodeB *enb,int rb)
{
UserEquipment *ue;
double interference = 0;
std::vector<UserEquipment*> *ues = NetworkManager::Init ()
->GetUserEquipmentContainer ();
std::vector<UserEquipment*>::iterator it;
for (it = ues->begin (); it != ues->end (); it++)
{
ue = (*it);
if (enb->GetIDNetworkNode () != ue->GetTargetNode ()->GetIDNetworkNode () &&
enb->GetPhy ()->GetBandwidthManager ()->GetUlOffsetBw () ==
ue->GetTargetNode ()->GetPhy ()->GetBandwidthManager ()->GetUlOffsetBw ())
if (ue->GetChannelsForTx().size () > 0 && ue->IsRBinChannelTx(rb))
{
double powerTXForSubBandwidth = 10 * log10 (
pow (10., (ue->GetPhy()->GetTxPower()-30)/10)
/
ue->GetChannelsForTx().size ());
double nodeInterference_db = powerTXForSubBandwidth - 10 -
ComputePathLossForInterference (enb, ue); // in dB
double nodeInterference = pow(10, nodeInterference_db/10);
interference += nodeInterference;
}
}
return interference;
}
Getting the channel quality value within the UplinkPacketScheduler::SelectUsersToSchedule
function:

user->m_transmittedData = 0;
user->m_channelContition = record->GetCQ ();
As stated before, calculating the interference per RB required having the al-
located RBs for each user, therefore:
Add the following variable std::vector int mchannelsForTx under UserEquip-
ment class to store the allocated RBs for each user that were calculated by the
uplink scheduler.
Add the below appropriate functions as members of the same class:
bool UserEquipment::IsRBinChannelTx (int rb)
{ for (std::vector<int>::iterator it = m_channelsForTx.begin ();
it != m_channelsForTx.end (); it++)
{
int channel = (*it);
if (channel ==rb ) return true;
}
return false;
}
void UserEquipment::SetChannelsForTx (std::vector<int> channelsForTx)
{ m_channelsForTx.clear ();
m_channelsForTx=channelsForTx;
}
std::vector<int> UserEquipment::GetChannelsForTx (void)
{
return m_channelsForTx;
}
Modify UeLtePhy::ReceiveIdealControlMessage to set the allocated sub
channels for each user:
NetworkManager::Init ()-> GetUserEquipmentByID(node)
->SetChannelsForTx(m_channelsForTx);
Add a new function called ResetChannelsForTx to reset user RBs alloca-
tion on each sub frame:
void UserEquipment::ReSetChannelsForTx ()
{
m_channelsForTx.clear ();
}

The most difficult part in uplink interference calculation at resource block
level was to get the number of allocated RBs per user from the scheduler, use
this value to calculate, during the transmission, the transmitted power per RB
and thus the interference per RB. However in downlink part, it was much more
easier to calculate the power transmitted per RB since the eNodeB is all the time
using and transmitting on all RBs.
Lets take an example where allocated RBs for a micro user are highlighted
in purple and allocated RBs for a macro user are highlighted in green (figure
4.2). Calculating the uplink interference on RB2 for a micro user will take into
consideration the transmitted power of macro user which is transmitting on the
same RB2 . However, calculating the interference on RB1 for a macro user will
not take into consideration the transmitted power of the micro user which is not
transmitting on the same RB1 .
Figure 4.2: Uplink - Resource block level
4.3.2 Implementation of the Proposed Technique
In TDDConfiguration.h define a static int vector for the kind of the TTI
(1=uplink muted subframe, 0 = uplink non muted subframe).
static int ABSUpConfig[1][10] = {
{
0, 1, 0, 1, 0, 1, 0, 1, 0, 1
},
};
Add FrameManager::GetSubFrameABSUpType function to get uplink new
scheduler:

int FrameManager::GetSubFrameABSUpType(int nbSubFrame) {
return ABSUpConfig[0][nbSubFrame - 1];
}
In FrameManager::ResourceAllocation() apply these changes:
GetSubFrameABSUpType(GetNbSubframes()) == 1)
{
Simulator::Init()->Schedule(0.0, &ENodeB::UplinkResourceBlockAllocation
,record);
}
{
,record);
}
{
}
{
,record);
}
CQI Feedback and Interference Optimization
Similar variable, as in the ABS case, was added to ENodeB:UserEquipmentRecord
but as duplication of the uplink channel quality indicator instead of the downlink
CQI. Similar functions were applied to get, set and initialize std::vector int
mcqABSFeedback new variable.
Modify the EnbLtePhy::StartRx method to set the new uplink channel
quality indicator variable:
AMCModule *amc = src->GetProtocolStack ()->GetMacEntity ()->GetAmcModule ();
std::vector<int> cqi = amc->CreateCqiFeedbacks (measuredSinr);
UserEquipment* ue = (UserEquipment*) src;
ENodeB* enb = (ENodeB*) GetDevice ();
ENodeB::UserEquipmentRecord* record = enb->GetUserEquipmentRecord
(ue->GetIDNetworkNode ());

if (record != NULL)
{
{
record->SetCQ (cqi);
}
else if (enb->GetNodeType() == NetworkNode::TYPE_MICROENODEB)
{
if (FrameManager::Init()->GetSubFrameABSUpType(FrameManager::Init()
->GetNbSubframes()) == 1) record->SetCQabs (cqi);
else if (FrameManager::Init()->GetSubFrameABSUpType(FrameManager::Init()
->GetNbSubframes()) == 0) record->SetCQ (cqi);
}
}
In void UplinkPacketScheduler::SelectUsersToSchedule() get the uplink
channel quality indicator vector by adding a if statement in order to select the
correct vector:
if (node->GetNodeType() == NetworkNode::TYPE_ENODEB)
{
user->m_channelContition = record->GetCQ ();
} else if (node->GetNodeType() == NetworkNode::TYPE_MICROENODEB)
{
if (FrameManager::Init()->GetSubFrameABSUpType(FrameManager::Init()
->GetNbSubframes()) == 1) user->m_channelContition = record->GetCQabs ();
else if (FrameManager::Init()->GetSubFrameABSUpType(FrameManager::Init()
->GetNbSubframes()) == 0) user->m_channelContition = record->GetCQ ();
}
In Interference::ComputeENodebInterference, macro users in muted sub
frames should not be included in the interference calculation :
for (it = ues->begin (); it != ues->end (); it++)
{
ue = (*it);
if (((ue->GetTargetNode ()->GetNodeType() == NetworkNode::TYPE_MICROENODEB) &&
FrameManager::Init()->GetSubFrameABSUpType(FrameManager::Init()->GetNbSubframes())
== 1) || ((ue->GetTargetNode ()->GetNodeType() == NetworkNode::TYPE_MICROENODEB) &&
== 0) || ((ue->GetTargetNode ()->GetNodeType() == NetworkNode::TYPE_ENODEB) &&
== 0))

Chapter 5
Simulations and Results
5.1 Simulation Input Parameters
5.1.1 General Input Parameters
The assumptions and parameters that are being used in SLS for macro layer and
micro layer are summarized in the tables 5.1 and 5.2. The parameters have been
considered so as to create the most appropriate simulation environment that is
relative to real scenarios.
To note that on both downlink and uplink scenarios, only one flow per user
was initiated, transmitting an infinite buffer application on top of UDP transport
protocol.
Parameter Assumption
Cell coverage Radius 250m
BS transmit power 30 dBm
Number of pico cells 1 pico cell per macro cell
Min. distance between pico and macro 50 m
Min. distance between pico cells 40 m
Carrier frequency 2.0 GHz
System bandwidth 10 MHz, 50 RB
Min.distance between UE and pico cell 10 m
Nb of UEs Uniformly distributed users and de-
pending on the simulated scenario
Path loss model PL (dB) =140.7+ 37.6log10R, R in
km
User mobility model Constant position, speed =0
Table 5.1: SLS, micro cell assumptions
33

34 5.1 Simulation Input Parameters
Parameter Assumption
Cell coverage Radius 1 km
BS transmit power 46 dBm
Number of macro cells 1 Macro cell
Inter-site distance 500 m
Carrier frequency 2.0 GHz
System bandwidth 10 MHz, 50 RB
Min. distance between UE and macro cell 35 m
Nb of UEs Uniformly distributed users and de-
pending on the simulated scenario
Path loss model PL (dB) =128.1+ 37.6log10R, R in
km
User mobility model Constant position, speed =0
Table 5.2: SLS, macro cell assumptions
5.1.2 Selected Scheduling Algorithms
There are various scheduling methods that have been developed over time to
enhance the process of data distribution. But in this thesis, we shall be con-
centrating on particularly two algorithms which have been implemented in the
software environment provided by LTE-Sim. Among them are:
- Round Robin used as uplink scheduler,
And
- Proportional Fair used as downlink scheduler.
Round Robin Scheduling
This scheduling method is based on the idea of being fair in the long term by
assigning equal no. of Physical Resource Blocks (PRBs) to all active UEs. In
this thesis, the number of PRBs used is 5. It operates by assigning the PRBs to
UEs in turn i.e. one after another without taking into account their CQI. Hence
the users are equally scheduled. The main advantage of this kind of scheduling
is the relative ease in its implementation whereas the major disadvantage is the
fact that it does not take into account user CQI feedback, which may lead to
lower and unequal throughput.
Proportional Fair Scheduling
This algorithm assigns the PRBs to the UE with the best relative channel quality
i.e. a combination of CQI and level of fairness desired. There are various versions
of PF algorithm based on values it takes into account. Main goal of this algorithm
is to achieve a balance between Maximizing the cell throughput and fairness,
by letting all users to achieve a minimum QoS (Quality of Service). Such an
algorithm is designed to be better in terms of average user throughput as well
as being fair to most of the users and meeting the minimum QoS requirements

Chap. 5 Simulations and Results 35
Cell scenario Path loss model
Macro Cell - Urban and Suburban Areas PL (dB) =128.1+ 37.6log10R @ 2GHz
Micro Cell - Urban and Suburban Areas PL (dB) =140.7+ 37.6log10R @ 2GHz
Table 5.3: Implemented path loss models
during the scheduling process. More details about proportional fair algorithm are
available in appendix B.
5.1.3 Selected Propagation Loss Model
In LTE-Sim, the channel propagation of the E-UTRAN interface was modeled
and developed to compute the transmitted signal propagation losses. It supports
various cell scenarios (i.e., urban micro-cell, suburban macro-cell, urban macro-
cell and rural macro-cell), it realizes the channel condition in terms of loss and
interference.
In this thesis, the realization of the channel condition (uplink and downlink
channels) considered four diﬀerent phenomena: (i) the path loss, (ii) the penetra-
tion loss, (iii) the shadowing, and (iv) the eﬀect of fast fading due to the signal
multipath.
During packet transmissions, knowing the source device and the destination
device, we can identify the cell type and thus the propagation loss model that
can be one of:
(i)macro-cell channel realization for urban and suburban areas, (ii) a macro-cell
channel realization for the rural area, and (iii) a micro-cell channel realization.
Taking the downlink scenario as an example [1] , Let PTX,j and PRX;i;j be
the eNB transmission power and the reception power of the i-th UE for the j-th
sub-channel, respectively. PRX;i;j is given by:
PRX;i;j = (PTX,j − Mi,j − Li − Ti − Si,j)indB (5.1)
Where Mi,j ,Li,Ti, and Si,j are the losses due to multipath, path loss, pene-
tration, and shadowing. Note that all these variables are expressed in dB.
In this proposal, the large scale shadowing fading has been modeled trough
a log-normal distribution with 0 mean and 8 dB of standard deviation. The
penetration loss, instead, is set to default value of 10 dB.
Table 5.3 reports path loss models used for each of the cell scenarios imple-
mented in this thesis. Note that d is the distance between the eNB and the UE
in kilometers.
In LTE-Sim [1], the fast fading has been modeled for all the implemented
propagation models with the Jakes model [12] for the Rayleigh fading, taking
into account the user speed, the sub-carrier frequency (i.e., the central frequency
of the j-th sub-channel), and a number of multiple paths uniformly chosen in the
set [6, 8, 10, 12]. Fig. 5.1 shows an example of multipath realizations when users
speeds are equal to 0, 3, 30, and 120 km/h.

36 5.2 Simulation Output Performance Metrics
Figure 5.1: Fast fading realization
5.2 Simulation Output Performance Metrics
In this thesis, several performance metrics have been identified to be used as a
means to evaluate and compare all the studies similarly.
1. CDF of user spectrum efficiency which is a cumulative density function
of the information rate that can be transmitted over a given bandwidth per
user, is measured in bps/Hz/user.
2. Cell spectrum efficiency measured in bps/Hz/cell.
3. Jain’s fairness index which determines whether users or applications are
receiving a fair share of system resources (ex. Bandwidth, throughput).
The result ranges from 1/number of users (worst case) to 1 (best case).
In order to calculate these metrics, a new windows based application has been
developed in order to analyze the LTE-Sim traces (figure 5.2), parse them and
represent the calculated metrics in a graphical way using Gnuplot software.
Figure 5.2: LTE-Sim traces example

5.3 Simulation Scenarios and Results
Once the theoretical concepts of CRE/ABS and proposed technique have been
explained, and part of the system level simulator has been modified and validated,
the next step is to obtain results in order to compare and evaluate both solutions
in different proposed scenarios while tuning specific parameters.
To note that each scenario has been iterated 100 times to apply the average and
get more accurate values.
Next, a new parameter α will be introduced to define, for each scenario, the
number of uplink sub frames that have been muted while simulating the proposed
solution.
In all coming simulations, we are referring to the existing solution by ABS-50%
which involves the CRE technique with a bias of 12 dB and on top of it applying
the ABS technique with a density of 50% (where half of the downlink sub frames
have been muted).
Scenario 1 nbmacroUe=25, nbmicroUe=20 and α=50%
The common input parameters to all scenarios were listed in the previous section,
however the parameter to be varied in this scenario is the number of micro-macro
users.
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1 1.2 1.4
CDF
spectral efficiency (bps/Hz/user)
baseline
existing,ABS-50%
proposed
(a) CDF of user spectrum efficiency
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1
CDF
Jain’s index
baseline
existing,ABS-50%
proposed
(b) CDF of Jain’s index
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
All Micro Macro
cellspectralefficiency(bps/Hz/cell)
basic
existing;ABS-50%
proposed
(c) Cell spectrum efficiency
Figure 5.3: Scenario 1 - Downlink

38 5.3 Simulation Scenarios and Results
Fig 5.3 depicts three metrics to evaluate user throughput at downlink and ra-
dio cell resources utilization as well as downlink throughput fairness distribution
between micro-macro users.
First, it is normal to notice that the proposed solution is aligned with the basic
scenario while simulating the downlink case, since the proposed solution is focus-
ing on uplink and doesn’t apply any change at downlink level.
Analyzing the CDF of user throughput (fig 5.3a) , we can observe that the existing
solution is aligned with the proposed solution for all throughputs less than 1.25
Mbps, however the proposed solution took advantage for all highest throughputs.
For example, 90% of users (macro and micro) users have throughputs less than
1.25 Mbps within the existing solution whereas same number of users acquire
throughputs less than 3.75 Mbps within the proposed solution.
Moving to the CDF fairnesses measurement (fig 5.3a) , we observe that the exist-
ing solution delivers more fairness to all users than the proposed one,. This due
mainly to CRE technique within the existing solution, that offloads the macro
cell and enables small cell to capture more traffic.
Analyzing the cell spectrum efficiency graph (fig 5.3c), we can notice that down-
link throughput shows a performance degradation at the macro cell level within
the existing solution comparing to the proposed scenario. Moreover, a stable
throughput is observed at micro cell and this is because the ABS technique is
serving micro users by muting the macro cell. This degradation at macro cell
level was reflected when applying the average of both cells, where the throughput
per cell decreases from 23 Mbps/cell to 18 Mbps/cell when applying the ABS-50%.
Similar to downlink case, Analyzing the CDF of user uplink throughput (fig
5.4a) , we can observe that the existing solution is aligned with the proposed
solution for all throughputs less than 1.5 Mbps, however the proposed solution
took advantage for all highest throughputs. For example, 90% of users (macro
and micro) users have uplink throughputs less than 1.7 Mbps within the existing
solution whereas same number of users acquire throughputs less than 3.3 Mbps
within the proposed solution.
Moving to the CDF fairnesses measurement (fig 5.4a) , we observe, for the same
reason discussed in the downlink case, that the existing solution comparing to the
proposed one improves uplink throughput fairness distribution among all users.
Moving to the cell spectrum efficiency graph (fig 5.4c), we can conclude that
the main target of this proposal was reached by observing an uplink throughput
improvement per micro cell (26 Mbps/cell) comparing to the baseline scenario
(22.9 Mbps/cell) and to the scenario with existing CRE/ABS solutions (24.8
Mbps/cell). So when applying the new proposed solution, micro cells are able to
benefit from the maximum of their available resources and deliver more through-
put to their served users. However, the proposed solution reduces the uplink per-

0
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5
CDF
baseline
existing,ABS-50%
proposed
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1
CDF
Jain’s index
baseline
existing,ABS-50%
proposed
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
All Micro Macro
basic
existing;ABS-50%
proposed
Figure 5.4: Scenario 1 - Uplink
formance at macro level, when muting 50% of uplink sub frames in this scenario
.The macro cell throughput will then decrease from 12Mbps/cell to 6 Mbps/cell.
This degradation at macro cell level was also reflected when applying the aver-
age of both cells, where the cell throughput decreases from 17.5 Mbps/cell to
16 Mbps/cell, after applying the proposed solution with α=50%. However, the
existing solution improves the total throughput per cell from 17.5 Mbps/cell to
20 Mbps/cell (fig 5.4c).
In this scenario, the number of micro users was reduced from 20 to 10 users.
After plotting the same graphs, the main difference appears when calculating
the cell spectrum efficiency within the uplink scenario (fig 5.5). In this case, the
micro cell is under-utilized and it is normal that the CRE takes advantage over
the proposed solution in improving micro cell total throughput. So the improve-
ment after applying the existing solution is 5 Mbps/cell while reaching only 2.5
Mbps/cell after deploying the proposed solution.
However, when increasing the number of users attached to micro cell (previous
scenario) and since the micro cell is already well utilized at the baseline, the CRE
technique within the existing solution will have a negligible effect and won’t take
advantage over the proposed technique (fig 5.4c).

0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
All Micro Macro
basic
existing;ABS-50%
proposed
(a) Cell spectrum efficiency
When changing the number of muted uplink sub frames defined in the proposed
scenario and which is denoted by α, the only difference detected was in the uplink
figures (fig 5.6).
0
20
40
60
80
100
0 0.1 0.2 0.3 0.4 0.5
CDF
baseline
existing,ABS-50%
proposed
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1
CDF
Jain’s index
baseline
existing,ABS-50%
proposed
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2.4
2.6
All Micro Macro
basic
existing;ABS-50%
proposed
When muting less sub frames in the macro cell, it is normal to observe an im-
provement of macro cell throughput from 6.1 Mbps/cell (fig 5.4c) in the first
scenario to 9 Mbps/cell (fig 5.6c) in the current scenario.

Regarding the micro cell throughput, the proposed solution is still preserving
the highest throughput (25 Mbps/cell) with regards to the baseline scenario and
to the existing solution.This improvement was also reflected when applying the
average of both cells, where the proposed solution is now targeting a throughput
close to the baseline value (fig 5.6c).
To note that a slight improvement in the proposed solution was detected, com-
paring to the first scenario, when plotting the user spectrum efficiency and the
uplink throughput fairness distribution (figures 5.6a and 5.6b).

Chapter 6
Conclusions and Future Work
6.1 Conclusions
In this thesis, heterogeneous networks along with CRE and ABS techniques have
been implemented and evaluated as a part of existing LTE-Advanced features.
The new technique that was proposed to replace both CRE and ABS and preserve
the same benefits, was also implemented and evaluated. The results that have
been obtained in each scenario using the simulator, have been analyzed to evaluate
both techniques, compare their benefits and their disadvantages.
The conclusions from this analysis are summarized as follows:
1. Existing solution reduces downlink performance of macro users, whereas
the new proposed technique reduces the uplink performance of the same
users and doesn’t affect their downlink performance. Therefore, in case of
data traffic or infinite buffer applications, the new proposed solution will
take advantage, knowing that the uplink traffic in this case is not anymore
critical comparing to the downlink traffic.
2. New proposed technique shows a better improvement of UL throughput at
micro level comparing to CRE/ABS techniques.
3. In the proposed solution, the throughput is not fairly distributed among
macro/micro users. Hence, this solution should be updated and enhanced,
as described in the next section, to improve the throughput fairness distri-
bution.
6.2 Future Work
This thesis opens several lines for further long-term investigation as for exam-
ple, enhancing and updating the proposed solution to improve the throughput
fairness distribution. This proposal can be achieved by muting only the macro
users that are close and interfere with the micro nodes, in a way to reduce the
uplink interference impact and at the same time to make fairer the distribution of
43

44 6.2 Future Work
throughput among the users. This enhancement imposed additional development
on the system level simulator.
Another recommendation is deploying more than one micro cell with different
configurations and evaluating the effects on the system performance.

Appendix A
LTE-Sim
A.1 About LTE-Sim
LTE-Sim is an open source framework to simulate LTE networks mainly devel-
oped by G. Piro and F.Capozzi [1] .
It encompasses several aspects of LTE networks, including both the Evolved
Universal Terrestrial Radio Access (E-UTRAN) and the Evolved Packet System
(EPS). It supports single and multi-cell environments, QoS management, multi
users environment, user mobility, handover procedures, and frequency reuse tech-
niques. Three kinds of network nodes are modeled: user equipment (UE), evolved
Node B (eNB) and Mobility Management Entity/Gateway (MME/GW). Four dif-
ferent traffic generators at the application layer have been implemented and the
management of data radio bearer is supported. Finally, well-known scheduling
strategies (such as Proportional Fair, Modified Largest Weighted Delay First,
and Exponential Proportional Fair), AMC scheme, Channel Quality Indicator
feedback, frequency reuse techniques, and models for physical layer have been
developed.
A.2 Running LTE-Sim simulator
Running LTE-Sim under windows was done following the below steps:
1. Install the Java JRE (1.7 or higher) from Oracle.
2. Install MinGW (0.5-beta - 20120426 - 1 or later). During the Select Com-
ponents step, select ”C++ Compiler” and ”MSYS Basic System” for in-
stallation.
3. Download/Install Eclipse IDE for C++ Developers.
4. Create a new C++ Project.
5. Add ”-static-libgcc -static-libstdc++” as Linker flags for the new project
(Under the Project Properties, expand the C/C++ Build menu and click
45

46 A.2 Running LTE-Sim simulator
on Settings. Under the Tool Settings tab, expand the MinGW C++ Linker
menu and click on Miscellaneous. Add the text to the Linker flags field.)
6. Download the LTE-Sim C++ open source project.
7. Copy/paste all files of LTE-Sim/src into the src folder of the newly created
C++ project under Eclipse.
8. Run the project.

Appendix B
Proportional Fair Scheduling
Proportional fair is a compromise-based scheduling algorithm. It is based upon
maintaining a balance between two competing interests: Trying to maximize total
[wired/wireless network] throughput while at the same time allowing all users at
least a minimal level of service. This is done by assigning each data ﬂow a data
rate or a scheduling priority (depending on the implementation) that is inversely
proportional to its anticipated resource consumption.[13]
Scheduling data transfer can be doneusing prioritization coeﬃcients.[14] Here
we schedule the channel for the station that has the maximum of the priority
function:
P =
Ta
Rb
(B.1)
- T denotes the data rate potentially achievable for the station in the present
time slot.
- R is the historical average data rate of this station.
- a and b tune the ”fairness” of the scheduler.
By adjusting a and b in the formula above, we are able to adjust the balance
between serving the best mobiles (the ones in the best channel conditions) more
often and serving the costly mobiles often enough that they have an acceptable
level of performance.
In the extreme case (a=0 and b=1 ) the scheduler acts in a round-robin fashion
and serves all mobiles equally often, with no regard for resource consumption. If
a=1 and b=0 then the scheduler will always serve the mobile with the best chan-
nel conditions. This will maximize the throughput of the channel while stations
with low T are not served at all. Using a 1 and b 1 will yield the proportional
fair scheduling algorithm used in 3G networks.[14]
This technique can be further parameterized by using a ”memory constant” that
determines the period of time over which the station data rate used in calcu-
lating the priority function is averaged. A larger constant generally improves
throughput at the expense of reduced short-term fairness.
47

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