interference mitigation lte

Interference Management in

LTE-Advanced Heterogeneous Networks

Using Almost Blank Subframes

HISHAM EL SHAER

Master's Degree Project

Stockholm, Sweden

XREESB 2012:006

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Interference Management In LTE-Advanced Heterogeneous Networks

Using Almost Blank Subframes

Hisham El Shaer

March 2012

Degree Project in Signal Processing

Stockholm, Sweden 2012

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Abstract

Long term evolution (LTE) is the standard that the Third-generation Partnership 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 reason for 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 nowadays, 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 networks 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. As a solution to this problem an offset to the received

power measurements used in cell selection is applied allowing 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.

Enhanced Inter-Cell Interference Coordination (eICIC) schemes have been proposed 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 Subframes (ABS).

The focus of this thesis will be on the ABS solution which consists of reserving a group of subframes

during which the Macro nodes are partially muted allowing the users in the range extension area to be

served with lower interference.

The objective of this thesis work is to introduce a closed form expression to calculate the Almost Blank

Subframes allocation in order to maximize the normalized cell-edge users throughput. The derivations are

carried out for a simplified model of a telecommunications network. The expression will be validated

with simulations involving different users and Pico nodes distributions and different channel models (ITU

channel models and Spatial Channel Models). Another goal is to try to have a deeper understanding and

concrete conclusions about the different heterogeneous deployments.

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Acknowledgment

This work would not have been possible to complete without the support of many people to whom I want

to show my gratitude. First of all I would like to dedicate this thesis to my soon to be born daughter.

I would like to thank my family and my wife for their continuous support and patience. I also want to

thank my supervisor at Ericsson Niklas Wernersson and my manager Maria Edvardsson for their help and

guidance during the project. Finally I want to thank my supervisor at KTH Mats Bengtsson for his

support before and during the thesis.

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Table of Contents

1. Introduction .......................................................................................................................................... 6

Brief about LTE .............................................................................................................................. 6 1.1

1.1.1 LTE requirements .................................................................................................................. 7

1.1.2 LTE downlink transmission scheme ...................................................................................... 7

1.1.3 Cyclic-Prefix insertion ............................................................................................................ 8

1.1.4 Spectrum flexibility ............................................................................................................... 9

1.1.5 Physical resources ............................................................................................................... 10

1.1.6 Enhancements introduced in LTE advanced (Release 10) .................................................. 12

Introduction to Heterogeneous Networks (HetNets) ................................................................. 14 1.2

1.2.1 Motivation and description of HetNets .............................................................................. 15

Goal of the thesis ........................................................................................................................ 16 1.3

2. Range extension and associated problems ......................................................................................... 17

Range extension introduction ..................................................................................................... 17 2.1

Range extension advantages ...................................................................................................... 18 2.2

Interference effects associated to range extension ................................................................... 18 2.3

3. Inter-cell interference available solutions .......................................................................................... 19

Frequency domain multiplexing inter-cell interference coordination scheme .......................... 19 3.1

Time domain multiplexing inter-cell interference coordination scheme (Almost Blank 3.2

Subframes) .............................................................................................................................................. 20

4. Range extension with almost blank sub-frames (ABS) ....................................................................... 22

Common reference signals (CRS) interference ........................................................................... 22 4.1

Proposed formula to calculate the ABS ratio to maximize the performance. ............................ 23 4.2

4.2.1 General model ..................................................................................................................... 24

4.2.2 Simulations validating the previous results ........................................................................ 30

4.2.3 Example to validate the general model results .................................................................. 37

4.2.4 Example to validate the general model results (without the assumption of Ptotal_Pico) ...... 40

4.3 Summary ........................................................................................................................................... 43

5. System simulation results ................................................................................................................... 44

The Raptor simulator .................................................................................................................. 44 5.1

System simulation assumptions .................................................................................................. 45 5.2

Simulation results ....................................................................................................................... 45 5.3

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5.3.1 Who wins and who loses in terms of throughput in a heterogeneous network

deployment? ....................................................................................................................................... 45

5.3.2 Simulations demonstrating the benefits of using ABS ........................................................ 48

5.3.3 Simulations validating the ABS ratio formula for different users and Pico-eNBs

distributions. ....................................................................................................................................... 53

5.3.4 Does having a high range extension give a better performance? ...................................... 68

6. Conclusions ......................................................................................................................................... 71

7. Future work ......................................................................................................................................... 71

8. List of Acronyms .................................................................................................................................. 72

9. References .......................................................................................................................................... 73

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1. Introduction

In this section an introduction about Long Term Evolution (LTE) will be presented focusing only on the

downlink since the thesis work mainly focuses on the downlink1 transmission, then an explanation of the

heterogeneous networks (HetNets) concept, its motivation and its different types will be introduced.

Finally the goal of the thesis and the contributions done in it will be introduced.

Brief about LTE 1.1

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)2 started working on solutions to fulfill the need for high

data rates and came up with HSPA3 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 4th generation of mobile telephony. LTE as defined by the 3GPP [12] is the

evolution of the 3rd

generation 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.

The rest of this LTE introduction will focus on the LTE requirements, the downlink transmission scheme

and the spectrum flexibility.

1 Downlink refers to the communication from the base station to the mobile user.

2 3GPP is a collaboration between groups of telecommunications associations with the goal of standardizing,

developing and maintaining of a globally 3rd

generation mobile phone system. 3 HSPA is short for High Speed Packet Access which is an amalgamation of the 2 protocols High Speed Downlink

Packet Access (HSDPA) and High Speed Uplink Packet Access (HSUPA)

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1.1.1 LTE requirements

The main requirements for an LTE system were identified in [1] and the most important ones can be

summarized in the following points.

- Data rate: Peak data rates of 100 Mbps (downlink) and 50 Mbps (uplink) for a 20 MHz spectrum

allocation.

- Throughput: The target downlink average user throughput per MHz is enhanced 3 to 4 times

compared to release 64. The target for uplink average user throughput per MHz is enhanced 2 to 3

times compared to release 6.

- Bandwidth: Scalable bandwidths of 5, 10, 15 and 20 MHz shall be supported. Also smaller

bandwidths smaller than 5 MHz shall be supported for more flexibility like 1.4 MHz and 3 MHz.

- Interworking: Interworking with existing UTRAN/GERAN5 and non-3GPP systems.

- Mobility: The system should be optimized for low mobile speeds (0-15 km/h) but should also

support higher mobile speeds including high speed train environments.

- Coverage: The targets stated above should be met for 5 km cells6 and some degradation in

throughput and spectrum efficiency for 30 km cells. Finally 100 km cells and larger are not covered

by the specifications.

1.1.2 LTE downlink transmission scheme

The LTE downlink transmission scheme is based on Orthogonal Frequency Division Multiplexing

(OFDM) where the available spectrum is divided into multiple carriers called subcarriers. Data symbols

are independently modulated and transmitted over orthogonal subcarriers where modulation schemes such

as QPSK, 16QAM and 64 QAM are used. The subcarriers being orthogonal means that there is no

interference between the subcarriers. OFDM transmission is a block based transmission where during

each OFDM symbol interval N modulation symbols are transmitted in parallel.

In practice an OFDM signal can be generated using IFFT (Inverse Fast Fourier Transform) digital signal

processing which is an efficient way to generate an OFDM signal. Figure 1 illustrates an OFDM

transmitter where OFDM modulation is done by means of IFFT processing [5].

As a first step the bits from the encoder are modulated into symbols, then these symbols are passed to a

serial to parallel converter to be able to process the N symbols through the IFFT modulator

4 3GPP standards are structured as releases, release 6 added mainly HSUPA and MBMS.

5 UTRAN and GERAN are responsible for the specifications of the Radio Access part of UMTS (3G) and

GSM/EDGE (2G) respectively. 6 A cell is the term used to describe the coverage area of a single base station and is usually illustrated by a

hexagonal shape.

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simultaneously, then the IFFT samples are passed to a parallel to serial converter and to a digital to analog

converter which sends the signal to the up-converter to be transmitted.

Figure 1: OFDM modulation by means of IFFT processing

1.1.3 Cyclic-Prefix insertion

The main advantage of an OFDM signal is that it can be demodulated without any interference between

the subcarriers due to the orthogonality between them.

However, considering a time dispersive channel7, the orthogonality between the subcarriers will, at least,

be partly lost. This loss of orthogonality in the time dispersive channel is due to the fact that the

demodulator correlation interval of one path will overlap with the symbol boundary of another path as

shown in Figure 2.

Figure 2: Time dispersion and received signal timing

Cyclic-prefix insertion implies that the last part of the OFDM symbol is copied and inserted at the

beginning of the OFDM symbol as shown in Figure 3, so cyclic-prefix basically increases the length of

the OFDM symbol from Tu to Tu+Tcp, where Tcp is the length of the cyclic-prefix which in turn reduces

the OFDM symbol rate.

7 Time dispersive channels are channels where multi-path exists and it is characterized by its time delay spread

which is the total time interval during which reflections with significant energy reach the receiver.

Parallel to

serial

converter

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Figure 3: Cyclic prefix insertion

Cyclic-prefix preserves the orthogonality between the subcarriers in the case of a time dispersive channel

as long as the span of the time shift or time difference between symbols is shorter than the cyclic prefix

length.

The problem with cyclic-prefix is that only a part of the received signal power is utilized by the OFDM

modulator, so there is a power loss. Also there is a loss in terms of bandwidth as the symbol rate is

reduced due to the insertion of the cyclic-prefix. One way to combat this loss of bandwidth is to reduce

the subcarrier spacing. A detailed description of OFDM and cyclic-prefix is given in [2] and [18].

1.1.4 Spectrum flexibility

Spectrum flexibility is one of the main characteristics of LTE radio-access technology. The main reason

of this spectrum flexibility is to allow for the deployment of LTE radio-access in different frequency

bands with different sizes since spectrum has become a scarce resource. This flexibility includes 2 main

areas as follows.

1.1.4.1 Flexibility in duplex arrangements

One important aspect of LTE is the possibility to operate in both paired and unpaired spectrum. Paired

frequency bands mean that the uplink and downlink transmissions use separate frequency bands while

unpaired spectrum means that uplink and downlink transmissions share the same frequency band.

LTE supports both frequency and time division based duplex arrangements.

Frequency-Division Duplex (FDD), as shown in Figure 4, implies that uplink and downlink transmissions

take place in different and sufficiently separated frequency bands.

Time-Division Duplex (TDD), as shown in Figure 4, implies that uplink and downlink operate in different

non-overlapping time slots.

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Figure 4: TDD and FDD operation

1.1.4.2 Bandwidth flexibility

Another important aspect in the LTE operation is the possibility to operate in different transmission

bandwidths in uplink and downlink. The reason for that is that the amount of spectrum available for LTE

deployment can vary a lot between frequency bands and also depending on the operator. Also this

bandwidth flexibility gives the possibility for gradual frequency bands migration from other radio-access

technologies.

1.1.5 Physical resources

1.1.5.1 LTE time domain structure

Downlink transmissions are organized in (radio) frames of length 10 ms which, in turn, are divided into

10 equally sized subframes of 1ms duration each. As illustrated in Figure 5, each subframe 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.

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Figure 5: LTE frame structure

1.1.5.2 LTE frequency domain structure

A resource element is the smallest physical resource in LTE and it consists of one subcarrier during one

OFDM symbol, resource elements are grouped into resource blocks. A resource block has a duration of

0.5 ms (one slot) and a bandwidth of 180 KHz (12 subcarriers) so each resource block consists of 12x7 =

84 resource elements in the case of normal cyclic prefix and 12x6 = 72 in the case of extended cyclic

prefix. The LTE physical layer specification allows for a carrier to consist of any number of resource

blocks in the frequency domain, ranging from a minimum of 6 resource blocks up to a maximum of 110

resource blocks which can be translated in frequency to a range between 1 MHz and 20 MHz with very

fine granularity that allows for the spectrum flexibility discussed before.

The time-frequency physical resources in LTE are shown in Figure 6.

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Figure 6: LTE frequency domain structure

1.1.6 Enhancements introduced in LTE advanced (Release 10)

The most important target for LTE release 10 was to be able to fulfill the IMT-Advanced requirements.

IMT is a global broadband multimedia international mobile telecommunication system that the ITU

(International Telecommunication Union) has been coordinating along with governments, industry and

private sector. IMT-Advanced is the term that ITU uses to describe radio-access technologies beyond

IMT-2000.

Some of the IMT-Advanced requirements are listed as follows [4]:

- Support for channel bandwidth up to 40 MHz.

- Peak spectral efficiencies of 15 bit/s/Hz in downlink (corresponding to peak rate of 600 Mbit/s).

- Peak spectral efficiencies of 6.75 bit/s/Hz in uplink (corresponding to peak rate of 270 Mbit/s).

- Control plane latency of less than 100 ms.

- User plane latency of less than 10 ms.

The main reason for LTE release 10 to be called LTE-Advanced is that its radio-access technology is

fully compliant with the IMT-advanced requirements.

In the following we introduce some of the most important enhancements and features introduced in LTE-

Advance.

Time

Freq

uen

cy

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1.1.6.1 Carrier aggregation

As mentioned before the previous releases of LTE have introduced a lot of flexibility in terms of

bandwidth as it allows operating in bandwidths ranging from 1 MHz to 20 MHz in both paired and

unpaired modes. In LTE release 10 the transmission bandwidth can be further extended using carrier

aggregation.

The main idea is to aggregate several component carriers and jointly use them for transmission to and

from single terminals. Up to 5 transmission components can be aggregated whether they belong to the

same frequency range or not and this feature allows the transmission bandwidth to reach 100 MHz, it also

allows to make use of the fragmented spectrum, as operators with fragmented spectrum can use this

feature to offer high data-rates by combining all the small spectrum fragments into a sufficiently large

component.

1.1.6.2 Extended multi antenna transmission

In LTE release 10, downlink spatial multiplexing has been expanded to support up to 8 transmission

layers so together with carrier aggregation a downlink data rate of up to 30 bit/s/Hz can be achieved.

In terms of uplink, spatial multiplexing of up to 4 layers is supported by release 10, this allows for an

uplink data-rate of 15 bit/s/Hz.

1.1.6.3 Relaying

Relaying implies that the mobile node is connected to its serving cell through a relay node that is

wirelessly connected to the serving node using the LTE radio-interface technology.

From a mobile node perspective the relay node is invisible as the mobile node can only see that it is

connected to the serving base station. This feature has the advantage of improving the coverage especially

in indoor environments.

1.1.6.4 Heterogeneous deployments

Heterogeneous deployments refer to deployments where we have base stations with different transmission

powers and coverage areas sharing, fully or partially, the same set of frequencies and having an

overlapping geographical coverage. An example of Heterogeneous networks is having a Pico-eNB8

placed in the coverage area of a Macro-eNB9.

Heterogeneous networks, also called HetNets, were supported by release 8 and 9 but release 10

introduced improved inter-cell interference handling making HetNet scenarios more robust. The rest of

this report will focus on HetNets and the Enhanced Inter-Cell Interference Coordination (eICIC) used by

release 10 to combat the interference caused by the Macro-eNBs to the Pico-eNB users.

8 Pico-eNB is a low transmitting power base station that has limited coverage and will be explained in details later.

9 Macro-eNB is the normal base station which is called eNB (short for evolved node B.) in LTE.

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Introduction to Heterogeneous Networks (HetNets) 1.2

Mobile broadband traffic has been growing very fast through the past few years; it surpassed voice traffic

and is expected to grow much faster in the future. This growth is mainly driven by new services and the

evolution of terminals capabilities. Annual traffic is predicted to double annually during the next five

years so that by 2014 the average user traffic will be about 1 GB of data per month compared to 100 or

200 MB now [5].

The mobile industry has been striving to improve data rates indoors and outdoors to be able to meet the

evolution of mobile services. There are several options that can be considered to increase the network

capacity and meet traffic and data rates demands such as:

- Improving the Macro layer: Upgrading the radio access of existing sites whether HSPA or LTE

would increase the data rates, this can be done by adding more spectrum which can notably enhance

the downlink data rates although the enhancement is negligible in the uplink.

Another option would be to add more antennas or enhance the processing within and between the

nodes. But at some point the capacity and data rates enhancements introduced by improving the radio

access of the nodes would be insufficient.

- Densifying the Macro layer: Increasing the number of Macro sites in urban and dense areas has

been a popular approach taken by operators to combat the traffic increase, it has the advantage of

decreasing the distance between the user and the serving base station so the uplink data rate is largely

enhanced and of course it has a big effect on the downlink data rates as well. The problem with this

approach is that it is very expensive to add more Macro sites in terms of cost, finding suitable

locations to deploy new sites and interference as we are placing high power nodes closer to each

other.

- Heterogeneous networks: This approach consists of complementing the Macro layer with low power

nodes such as Micro and 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 in

an unplanned manner depending on the areas that generate higher traffic.

Through the rest of the report we will focus on Heterogeneous networks and specifically on the Pico base

stations deployments that will be referred to as Pico-eNB for the rest of the report and will be described in

details in the following section.

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1.2.1 Motivation and description of HetNets

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 [13], 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

performance 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 users10

performance. [2]

Figure 7: Heterogeneous network using Pico-eNBs

1.2.1.1 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.

5) Due to their low transmission power and small physical size they can offer flexible site acquisitions.

In the following section we will explain how to optimize the performance of Pico-eNBs and the problems

that face this approach.

10

Cell-edge users, in this report, are defined to be the worst 5% of the total number of users in terms of capacity or

throughput.

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Goal of the thesis 1.3

The goal of this thesis is to find a closed form expression for the Almost Blank Subframes (ABS)

allocation that optimizes the network performance in terms of cell edge users throughput. Most of the

previous work has focused on the allocation of ABS depending on the ratio between the number of Macro

users per cell and the number of range extension users per Pico cell as in [14] and [17] which basically

means that the ABS allocation depends on the ratio of the number of the Macro users to the number of

range extension users belonging to each Pico-eNB or just choosing the Pico-eNB with the maximum

number of range extension users and applying that to all the Pico-eNBs. Through this thesis we will

deduce a formula, theoretically and using simulations, for the ABS allocation that depends on the ratio of

the number of Macro users to the total number of range extension Pico users in a cell and it will be proven

that it gives a better performance in terms of cell edge users throughput.

The main contributions of this thesis can be summarized in the following points.

1. Running system simulations in order to have solid conclusions about HetNets, concerning the users

who experience an increase or decrease of throughput after adding the Pico layer and the reasons

behind that. Also extract some conclusions about the Almost Blank Subframes as a TDM interference

coordination scheme in terms of its advantages and the winners and losers in this scenario.

2. Deduce a closed form expression for the ABS allocation that optimizes the performance in terms of

cell edge users throughput while keeping a fair level of normalized throughput. The deduction will be

done theoretically and will be validated using system level simulations.

3. Implement a graphical interface for the Raptor system simulator, which is the simulator I am working

on in Ericsson. This graphical interface will be used to illustrate a hexagonal cellular network

featuring the Macro-eNBs, Pico-eNBs and the simulated users. It allows focusing on a specific user or

group of users and studying their statistics. An example of this graphical interface will be presented in

the simulations section.

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2. Range extension and associated problems

Range extension introduction 2.1

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) downlink 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-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 8.

Figure 8: range extension area illustration

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Range extension advantages 2.2

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 applies 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 2.3

Due to the difference in transmission powers of the Macro-eNBs and the Pico-eNBs, in the range

extension area, illustrated in Figure 8, 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 Macro-

eNB.

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 9 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 discussed in the next section.

Figure 9: range extension interference

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3. Inter-cell interference available solutions

The enhanced Inter-Cell Interference Coordination (eICIC) in heterogeneous networks introduced in

LTE-Advanced has been a hot topic lately as without an efficient inter-cell interference scheme the range

extension concept loses its advantage 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 interference 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 subframes (ABS), and they will be discussed in details in the

following.

Frequency domain multiplexing inter-cell interference coordination 3.1

scheme

The main FDM interference cancellation method used in LTE-Advanced is carrier aggregation; this

feature has been discussed in section 1.1.6.1 which is one of the most important features of LTE-

Advanced 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 another carrier.

An example of that concept in a HetNet scenario is to partition the available spectrum into, for example, 2

separate component carriers, and assign the primary component carrier (f1) and the second component

carrier (f2) to different network layers at a time as shown in Figure 10 .

Figure 10: Illustration of eIIC based on carrier aggregation

In the example we have 2 component carriers f1 and f2 where 5 subframes are shown in each carrier.

There are 2 cases, the case of Macro layer usage and the case of Pico layer usage; the subframes are

distributed in control part, the blue part, and data part. The control part in the example only illustrates the

PDCCH, PCFICH and PHICH11

at the beginning of the subframes.

11

See list of acronyms.

20

As shown Figure 10 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 users12

data information on the same carrier that the Macro

layer schedules its users as shown in the third subframe in Figure 10, as the interference 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.

Time domain multiplexing inter-cell interference coordination scheme 3.2

(Almost Blank Subframes)

In this approach transmissions from Macro-eNBs inflicting high interference onto Pico-eNBs users are

periodically muted (stopped) during entire subframes, this way the Pico-eNB users that are suffering from

a high level of interference from the aggressor Macro-eNB have a chance to be served.

However this muting is not complete as certain control signals are still transmitted which are:

- Common reference symbols (CRS) which will be explained later

- Primary and secondary synchronization signals (PSS and SSS)

- Physical broadcast channel (PBCH)

- SIB-113 and paging with their associated PDCCH.

These control channels have to be transmitted even in the muted subframes to avoid radio link failure or

for reasons of backwards compatibility, so muted subframes should be avoided in subframes where PSS,

SSS, SIB-1 and paging are transmitted or in other words subframes #0, #1, #5 and #9. Since these muted

subframes are not totally blank they are called Almost Blank Subframes (ABS).

The basic idea is to have some subframes during which the Macro-eNB is not allowed to transmit data

allowing the range extension Pico-eNB users, who were suffering from interference from the Macro-eNB

transmission, to transmit with better conditions. The outline of ABS has been specified by the 3GPP in

[15].

ABS have specific patterns that are configured and communicated between the eNBs over the X2

interface. These patterns are signaled in the form of bitmaps of length 40 subframes, i.e. spanning over 4

frames and they can be configured dynamically by the network using self-optimizing networks (SON)

feature to optimize the ABS ratio according to some criterion that can be the cell-edge users throughput or

load balancing for instance and of course keeping in mind the above mentioned subframes that should be

avoided.

12

Center Pico-eNB users are the users connected to the Pico-eNB but that are not in the range extension area. 13

See acronyms list.

21

Figure 11: Illustration of TDM ICIC

As shown in Figure 11, TDM ICIC using ABS causes a lot of variation in terms of interference between

the subframes, this fact can be used in the sense that the users that suffer from a high level of interference

should be served during these ABS while the users that are closer to the transmitting node or that are not

very much affected by interference can be served during the non-ABS subframes.

So for a Pico-eNB cell, users are categorized into 2 groups in terms of ABS usage this time:

- Users in the range extension area and these users suffer from a high level of interference as explained

before so these users should only be served during the ABS.

- Users closer to the Pico-eNB that are called center Pico users and they are not heavily affected by the

interference from the Macro-eNB due to the good channel they maintain with their serving node. So

these users can be served by any subframe whether ABS or non-ABS.

One of the properties of LTE release 10 is that it allows eNBs to restrict the channel measurements done

by the users attached to them to a specific set or pattern of subframes. The reason for that is that if the

channel state information (CSI) measurements which are responsible of reporting the channel conditions

were to be done jointly for ABS and non-ABS, they will not accurately reflect the interference of either

type of subframes. So the terminals are configured with different CSI-measurement subsets corresponding

to the subframes that the terminal is allowed to use.

Users belonging to the range extension area are only allowed to report CSI measurements for the ABS as

they are only allowed to transmit during these subframes.

Users belonging to the center Pico-eNB area transmit 2 different subsets of the CSI measurements, one

for the ABS and another for the non-ABS as they are allowed to transmit through all the subframes. CRS

interference will be discussed in the following section.

22

4. Range extension with almost blank sub-frames (ABS)

Common reference signals (CRS) interference 4.1

Common reference signals (CRS) are transmitted in every downlink subframe and in every resource block

in the frequency domain, so they cover the entire cell bandwidth. CRS can be used by the terminal for

channel estimation for coherent demodulation of downlink physical channels [2].

They can also be used by the terminal to acquire channel state information (CSI) which is used as the

basis for cell selection and handover decisions.

Figure 12: Structure of CRS within a pair of resource blocks

As shown in Figure 12, the structure of a single cell-specific reference signal consists of reference

symbols of predefined values inserted within the first and third last OFDM symbol of each slot, so within

each resource block pair there are 8 reference symbols, also the number of different reference signals in a

cell corresponds to the number of antenna ports available in the cell.

CRS is considered as the most important cause of interference in ABS as CRS exists in every resource

block as shown in Figure 12. CRS can be eliminated with different strategies that are explained in [16]:

- Using Multicast-Broadcast Single Frequency Network Subframe: which is a specific subframe where

CRS is not transmitted in the data part but is still transmitted in the control part.

- Interference cancellation of CRS from Macro-eNB cells: Using techniques to cancel the CRS effect

such as successive interference cancellation.

- Puncturing of resource elements in which Macro-eNB transmits CRS: which means not considering

the resource elements where CRS is present.

Throughout the rest of the report we will consider perfect CRS interference cancellation and we will

focus on optimizing the ABS ratio.

23

Proposed formula to calculate the ABS ratio to maximize the performance. 4.2

In this section we will deduce a closed form expression for the ABS (Almost Blank Subframes) allocation

percentage or ratio14

that maximizes the performance of the network in terms of cell-edge users capacity.

As was stated before the ABS configuration is communicated between the nodes using a 40 subframes

pattern, so by optimizing the ABS ratio we mean optimizing the number of subframes that are considered

as ABS in this pattern.

In the following example a round robin scheduler is considered where Macro-eNB users and center Pico-

eNB users are only allowed to be scheduled in the non-ABS while the range extension Pico-eNB users are

only allowed to be scheduled in the ABS. The constraint on the center Pico-eNB users is introduced for

simplicity and to allow the range extension users some fairness in using the ABS because in reality ABS

are shared between center and range extension Pico-eNB users and it becomes harder to determine which

users are scheduled in the ABS. First we start by an introduction about round robin scheduler and why it

is used in this example.

Round robin is a simple scheduling method that is based on assigning the resources to the terminals in

turn, one after another, which means that all the users have equal chances to be scheduled without

considering their CQI (channel quality indicator) which is explained in the flow chart in Figure 13.

Figure 13: Flow chart explaining the round robin scheduler

14

By this we mean the number of subframes that are used as ABS out of the total number of subframes in the pattern, so if we use 10 subframes out of 40 as ABS the ratio would be 0.25.

24

The reason for using round robin scheduling is its simplicity and that it is very convenient to use in a

theoretical example to ensure that all the users have the same chance of being scheduled and then

comparing users in terms of capacity and throughput for instance.

The rest of this section will be divided into 3 parts; the first one is a general model that is used to deduce a

general formula for calculating which is the ABS ratio, and the second part consists of simulations that

validate the theoretical results and finally an example with a specific setup of the model in the first part,

which means specifying the path loss model, transmitting power and position for each node, which is also

used to validate the results.

4.2.1 General model

Considering a simple setup having a 1 cell network with the following features:

a. This cell contains 1 Macro-eNB and a certain number Npico of Pico-eNBs. The Pico-eNBs are

randomly distributed in the cell.

b. The users are randomly distributed throughout the cell area.

c. All Pico-eNBs have the same number of users in the range extension area.

d. Round robin scheduler is used as explained in the previous section

If we consider a channel model15

that is only impaired by additive white Gaussian noise (AWGN) and

interference, then the ith user capacity

16 will be according to the following equation

(1)

where hi is the channel gain, SINRi is the signal to interference and noise ratio and BW is the bandwidth

which is considered to be 1 Hertz through the whole example for simplicity, also the number of subframes

is assumed to be 1. The following notation will be used in the deduction.

Macro-eNB transmission power P1

Pico-eNB transmission power P2

Channel gain from Macro-eNB to the ith user (hm_ue)i

Channel gain from the kth Pico-eNB to the i

th user (hp_ue)k,i

number of ues per Macro-eNB Nm

number of Pico-eNBs Npico

number of center Pico-eNB ues per Pico-eNB Np_c

number of range extension ues per Pico-eNB Np_re

Almost blank subrames ratio (Alpha)

the noise in the system N0

Table 1

15

Here every user has a different channel to each node (Picos and Macro) so each user has a vector (Npico+1) long

of channels that are only impaired by AWGN and interference. 16

Channel capacity is defined as being the tighter upper bound of the amount of information that can be transmitted

over a communication channel.

25

As explained before, cell selection is based on the downlink reference signal power measurements so the

users attached to the Macro-eNB (Nm) have a higher downlink power coming from the Macro-eNB than

the Pico-eNBs, While center Pico-eNB users (Np_c) receive the reference signals from the Pico-eNB with

a higher power than the signals coming from the Macro-eNB. Finally for the range extension Pico-eNB

users (Np_re), although they receive the reference signals from the Macro-eNB with a higher power but

due to the range extension offset, that was explained before, these users are attached to the Pico-eNB.

So using the above notation the capacity for the users attached to the different nodes can be formulated as

follows starting by the ith Macro-eNB user capacity in equation (2).

(2)

. (3)

Then the capacity of the ith center Pico-eNB user attached to the k

th Pico-eNB

(4)

. (5)

And finally the ith range extension Pico-eNB user attached to the k

th Pico-eNB

(6)

. (7)

We can plot the users capacity in equations (2), (4) and (6) as a function of , so by choosing one user

from each group (Macro, center Pico and range extension Pico) and specifying values for the different

parameters (channel gains, P1, P2, Nm, Np_c and Np_re) we get the plot in Figure 14.

26

Figure 14: Plot of the capacity of Macro-eNB, center Pico-eNB and range extension users against

So in order to maximize the cell edge users capacity

17 we need to find the intersection point between the

lowest range extension capacity line, corresponding to the range extension user having the lowest

capacity, and the first line it intersects with which is the lowest Macro-eNB or center Pico-eNB user

capacity line, corresponding to the Macro-eNB or center Pico-eNB user having the lowest capacity.

So we can define the intersection point, which is basically found by a search over , using the following

criterion:

{ } (8)

In this case we will not consider the center Pico-eNB capacity line, so we will only focus on the range

extension and Macro-eNB users as in reality center Pico-eNB users are not affected by the ABS ratio, but

here we assume that center Pico-eNB users are only allowed to transmit during non-ABS to make the

scheduler simpler and giving the Macro-eNB user and Pico-eNB range extension user an equal chance to

be scheduled.

We will denote the Macro-eNB user having the lowest capacity by user m having the following

capacity

(9)

. (10)

17

In this model we maximize the worst user (0% worst user) capacity instead of the cell edge users (5% worst users)

capacity for simplicity.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

10

20

30

40

50

60

alpha

capacity

macro user capacity

center pico user capacity

range extension user capacity

Cap

acit

y (b

its/

sec)

27

We will denote the range extension user having the lowest capacity by user n and assuming that this

user belongs to the kth Pico-eNB with the following capacity

(11)

. (12)

The intersection point can be acquired analytically by equating equations (9) and (11) in order to find the

optimum alpha that maximizes the cell edge capacity as follows

(13)

And by reordering the previous equation we get the following equation which can be considered as the

optimal value of in order to optimize the 0% worst user throughput.

(14)

Since the mth

Macro-eNB user capacity is given by eq. (9) so considering that only this Macro-eNB user

gets all the resources all the time then the capacity would be given by the following expression, i.e.

putting the number of users to 1 in eq. (1)18

.

. (15)

Which we can call the maximum Macro-eNB user capacity, so is the same as but

only assuming that the Macro-eNB is only serving this user m, this is why it is called

because this is the maximum capacity that this user can reach. And doing the same for the nth range

extension Pico-eNB user

(16)

Then can be expressed as

. (17)

From this equation we can clearly see that alpha depends on 2 factors:

1. The ratio between the number of Macro-eNB ues to the number of range extension ues per Pico-eNB.

2. The ratio between the maximum capacity of a range extension user and the maximum

capacity of a Macro-eNB user .

18

This is exactly as if we have only one Macro-eNB user so this user will use the available resources (subframes) all

the time.

28

Focusing on the second factor and trying to simplify it, starting with the maximum Macro-eNB user

capacity

(18)

Since the noise value is very small we can neglect it also assuming the value of P1 to be very large so

(

P1) is much bigger than the term in the denominator then we can approximate the previous

equation to

. (19)

Normally most users attached to the Macro-eNB are placed close to it, although some Macro-eNB users

are placed very close to the Pico-eNB due to the high transmission power of the Macro-eNB but we will

consider only the users closer to the Macro-eNB, who are the majority, and assuming that the interference

to these users is dominated by one or at most two Pico-eNBs while the rest cause negligible interference.

Under this assumption we can approximate the interference term

with a constant (I)

since it is assumed to be independent on the number of Pico-eNBs and is dominated by the interference

caused by the closest 1 or 2 interferer Pico-eNBs.

) . (20)

Since is assumed to be independent on Npico so it can be considered as a constant and can be

denoted by C1.

Now focusing on the second term which is .

. (21)

Inserting the SINR3 expression

(22)

Assuming that we have a very large Npico then N0 can be neglected, considering that P2 0, and the

interference term in the denominator would be larger than the numerator so the previous equation can be

approximated to

(23)

where k is the serving Pico-eNB for the range extension user.

29

Since the Pico-eNBs are distributed randomly in the cell so and can be considered

as independent and identically distributed (IID) random variables. Also since we are trying to optimize

the capacity and we are assuming a large Npico so optimizing would be the same as optimizing its

expected value so we can replace by as follows

. (24)

Since all the values of can be considered as independent identically distributed (IID) random

variables having the same mean value and can be expressed as

19

. (25)

can be considered as a constant value so

( )

(26)

and finally the term

can be considered as a constant and can be denoted by C2 and since

Npico is assumed very large so and can be expressed as

. (27)

Finally

. (28)

So can be expressed as

(29)

where Nre*Npico is equal to the total number of range extension users which can be denoted by Nre_total..

Finally is expressed by

. (30)

19

It is known that

but we will use this approximation anyway to simplify the problem. Also the

variance of the values has been found to be very small, in the order of 10-14

, which verifies the approximations

done in this equation.

30

So if the values of C2 and C1 are assumed to be approximately equal, which will be shown in the following

sections, then we can introduce which is considered, according to simulations, to be the optimized

value that gives the optimal or suboptimal value of and is expressed by:

. (31)

This means that the ABS ratio is proportional to the ratio between the number of users attached to the Macro-eNB and the total number of range extension users attached to the Pico-eNBs.

4.2.2 Simulations validating the previous results

In this section a small MATLAB system simulator that performs Monte Carlo20

simulations [11] will be

introduced to verify the results in the previous section specifically equations (29) and (31) as they are

considered the most important results in the deduction. The simulations consist of a 1 cell network with a

Macro cell at a predefined position and a specific number of Pico-eNBs and users are dropped randomly

throughout the cell area.

The path loss is calculated according to 2 models, the ITU channel model and the Spatial Channel Model

(SCM) which will be explained in details in the following.

- ITU channel model: we will use the urban Macro-eNB (UMa), for Macro-eNB users, and urban

micro (UMi), for Pico-eNB users, models in [6].

Assuming that all users have line of sight to the serving base station so the path loss in dB for Macro-eNB

users will be calculated according to

for d < 160 m (32)

for d > 160 m (33)

where d is the distance between the user and the node, hBS = 24m, hUT = 0.5 m and fc=1 GHz.

And for Pico-eNB users the path loss is given as

for d < 120 m (34)

for d > 120 m (35)

where d is the distance between the user and the node, hBS = 9m, hUT = 0.5 m and fc=1 GHz.

20

Monte Carlo method is a class of computational algorithms that depends on repeated random sampling to compute

its results which in our case means to drop the users and Pico-eNBs repeatedly and in a random way to compute the

end result.

31

- Spatial channel model: This model will be calculated according to the equations in [10] and

assuming no line of sight for both Macro-eNB and Pico-eNBs.

For the Macro-eNB users the path loss in dB is given by

10 10

10 10

[ ] 44.9 6.55log log ( ) 45.51000

35.46 1.1 log ( ) 13.82log ( ) 0.7

bs

ms c bs ms

dPL dB h

h f h h C

(36)

where hbs is the base station antenna height in meters, hms is the MS antenna height in meters, fc the carrier

frequency in MHz, d is the distance between the BS and the user in meters, and C is a constant which is

equal to 3dB for urban Macro-eNB. These parameters are set to hbs = 32m, hms = 1.5m and fc=1900MHz.

And the path loss for Pico-eNB users is given by

PL = -55.9 + 38*log10(d) + (24.5 + 1.5*fc/925)*log10(fc) (37)

where fc = 1900 MHz.

The idea is to use the Monte Carlo method to compare the optimum alpha, given by equation (14), with

the deduced alpha in (29) and (31). In order to do that, an average of 100 drops21

, with a random

realization for the positioning of the Pico-eNBs and users for each drop, will be used to calculate an

average value of alpha and this process will be repeated 500 times so that we will have 500 calculated

alpha for each equation at the end then we compare the results.

4.2.2.1 Validating the alpha expression:

In this section we will validate the expression given by equation (29), the idea is to calculate the value

of according to equations (29) and the optimum value of according to equation (14), this process will

be iterated 500 times, as explained before, so at the end we will have 2 vectors of , each consisting of

500 values, that we can compare and if the values in both vectors are approximately equal, then equation

(29) can be validated to give an optimal value for .

Since in the deduction we assume having a large number of Pico-eNBs, we will drop 100 Pico-eNBs and

200 users randomly and alpha will be calculated according to equations (14) and (29) and both values will

be compared, listed in Table 2 are the parameters used in this simulation.

Cell area22

50m x 50m

Macro-eNB position X:0 Y:25

Pico-eNBs positions Random but keeping a minimum distance of 10 m

from the Macro-eNB.

Users positions Random

Macro-eNB transmitting power 40 W

Pico-eNB transmitting power 1 W

Number of drops 100

Table 2

21

A drop is defined as one simulation run over a certain time period. 22

The reason for having a very small cell area is to decrease the distance between the Pico-eNB-eNBs to increase

the interference between them to fulfill the assumption of having a very big number of Pico-eNB-eNBs in the cell.

32

The only problem in equation (29) is that the value of in C2 is not known; also the

assumption that all the Pico-eNBs have the same number of range extension users is not present in this

simulation so we will go back 1 step to equation (25) which was given by

23

(38)

and will consider to be the constant and will be denoted by so the value of alpha will be

given by

(39)

4.2.2.1.1 ITU channel model

We start by the ITU channel model. Figure 15 represents the PDFs of the 500 alpha values calculated

from equations (14) and (39). And it shows that the PDFs are concentrated at very close values.

Figure 15: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (39) (Green)

Figure 16 represents a plot of the alpha value in both cases for 500 iterations; each iteration is an average

of 100 drops. If we compare both values at any of the 500 measurements we will see that the difference

between them is always less than 0.1 which means that the value of alpha calculated in equation (39)

gives the optimal or the suboptimal value of the ABS ratio24

. It can be seen from these results that the

result from equation (39) can be validated to give the optimal or suboptimal ABS ratio for the ITU

channel model.

24 It will be shown in the simulations section that if the formula gives a solution that is 0.1 less or more than the

optimal one this solution is the suboptimal one, which means that it is the second best solution, and is very close to

the optimal solution.

0.65 0.7 0.75 0.8 0.850

20

40

60

80

100

120

Alpha

Optimal Alpha according to eq (14)

Alpha according to eq (29)

33

Figure 16: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (40) (Green)

4.2.2.1.2 Spatial channel model

In this part we will repeat the previous simulation but using the Spatial Channel Model instead of the ITU

channel model. Figure 17 represents the PDFs of the results from equations (14) and (39). And it shows

that the pdfs are concentrated at very close values.

Figure 17: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (39) (Green)

Figure 18 represents the plot of the alpha value in both cases for 500 iterations; each iteration is an

average of 100 drops. If we compare both values at any of the 500 measurements we will see that the

difference between them is always less than 0.1 which means that the value of alpha calculated in

equation (39) gives the optimal or the suboptimal ratio of ABS. It can be seen from these results that the

result from equation (39) can be validated to give the optimal or suboptimal ABS ratio for the spatial

channel model.

0 50 100 150 200 250 300 350 400 450 500

0.65

0.7

0.75

0.8

0.85

Itteration number

Alp

ha



0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.750

20

40

60

80

100

120

140

Alpha



34

Figure 18: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (40) (Green)

In this section equation (39) which is the same as equation (29) has shown to be giving results very close

to those of equation (14) which, in turn, shows that equation (29) gives the optimal ABS ratio in terms of

cell edge users throughput in the case of the ITU channel model and spatial channel model. It is also

worth noting that the difference between the alpha values according to equation (29) and equation (14) is

higher in the case of Spatial Channel Model compared to the ITU channel model and this is due to the fact

that the path loss in the case of SCM is lower than in the case of ITU channel model, which means that

the interference in the ITU case is higher, so by putting the values of the channel gains according to SCM

in equation (14) we get a larger value of alpha.

4.2.2.2 Validating the alpha expression:

In this section we will validate expression given by equation (31), the idea is to calculate the value of

according to equations (31) and the optimum value of according to equation (14), this process will be

iterated 500 times, as explained before, so at the end we will have 2 vectors of , each with 500 values,

that we can compare and if the values in both vectors are close enough then equation (31) can be

validated to give an optimal value for .

For this part we use a more realistic example where we drop 6 Pico-eNBs placed randomly in the cell, in

addition 200 users are dropped randomly throughout the cell area. The simulation parameters are listed in

Table 3.

Cell area 500m x 500m

Macro-eNB position X:0 Y:250

Pico-eNBs positions Random but keeping a minimum distance of 70

m from the Macro-eNB and the other Pico-eNBs.

Users positions Random

Macro-eNB transmitting power 40 W

Pico-eNB transmitting power 1 W

Number of drops 100

Table 3

0 50 100 150 200 250 300 350 400 450 5000.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

0.75

Itteration number

Alp

ha



35

4.2.2.2.1 ITU channel model

We start by the ITU channel model. Figure 19 represents the PDFs of the results from both equations.

And it shows that the PDFs are almost coinciding.

Figure 19: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)

Figure 20 represents the plot of the alpha value in both cases for 500 iterations, each iteration is a 100

drops.

Figure 20: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)

0.5 0.52 0.54 0.56 0.58 0.6 0.62 0.64 0.66 0.680

20

40

60

80

100

120

140

Alpha



0 50 100 150 200 250 300 350 400 450 5000.48

0.5

0.52

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

Itteration number

Alp

ha



36

These results show that the result in equation (31) is very close to the optimum value given by (14) when

using the ITU channel model therefore it can be validated.

4.2.2.2.2 Spatial Channel Model (SCM)

In this part we will repeat the previous simulation but using the spatial channel model instead of the ITU

channel model. Figure 21 represents the PDFs of the results from equations (14) and (31). And it shows

that the PDFs are concentrated at very close values.

Figure 21: PDFs of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)

Figure 22 represents the plot of the alpha value in both cases for 500 iterations, each iteration is an

average of 100 drop and as seen the values resulting of both equations are very close.

Figure 22: Plot of the 100 values of Alpha according to eq (14) (Blue) and Alpha according to eq (31) (Red)

0.58 0.6 0.62 0.64 0.66 0.68 0.7 0.72 0.740

20

40

60

80

100

120

Alpha



0 50 100 150 200 250 300 350 400 450 5000.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7

0.72

0.74

Itteration number

Alp

ha



37

These results show that the result in equation (31) is very close to the optimum value given by (14) when

using the spatial channel model therefore it can be validated. Same as the previous case, the difference

between the alpha values according to equation (31) and equation (14) is higher in the case of Spatial

Channel Model compared to the ITU channel model and this is due to the fact that the path loss in the

case of SCM is lower than in the case of ITU channel model, which means that the interference in the

ITU case is higher, so by putting the values of the channel gains according to SCM in equation (14) we

get a larger value of alpha. To summarize, it has been shown that the theoretical deductions in equations

(29) and (31) can be validated to give optimal or suboptimal results for the ABS ratio using Monte-Carlo

simulations. In the following section an example that is a special case of the general model used in the

deduction will be introduced to elaborate more on the theoretical results.

4.2.3 Example to validate the general model results

Through this example we will apply the previous theoretical model into a more practical scenario using

specific path loss distributions, Pico-eNB distributions and nodes transmitting powers.

4.2.3.1 Defining the network topology and new parameters used in this example

In this subsection the network topology and different parameters used in the example are stated.

a) We use a 1 cell network which contains 1 Macro-eNB and 2 groups of Pico-eNBs where group 1 are

the Pico-eNBs closer to the Macro-eNB and group 2 are the Pico-eNBs farther from the Macro-eNB

and we will start the example by 4 Pico-eNBs, 2 in each group, as shown in Figure 23 and then we

will generalize the model for any number of Pico-eNBs (Npico).

b) Same as the general model we will consider only the Macro-eNB user that has the lowest capacity

and the range extension Pico-eNB user that has the lowest capacity.

c) We will assume, for simplicity, that the Pico user has the same path loss from all the other Pico-

eNBs. This can be the case when we have only 4 Pico-eNBs as they are equidistant, see Figure 23,

but we will assume that this can be extended to any number of Pico-eNBs which is a strong

assumption but it can be motivated due to the fact that we are not considering inter-cell interference in

this example but in reality if we have a large number of Pico-eNBs (Npico), as we will assume later,

then the Pico user placed at the cell border suffers from a larger inter-cell interference than the Pico-

eNB user placed in the cell center for instance, in that sense we can assume a close interference value

for all the Pico-eNB users.

d)

path loss from group 1 Pico-eNBs to Macro-eNB user hp1_ue

path loss from group 2 Pico-eNBs to Macro-eNB user hp2_ue

Table 4

38

e) Transmission powers for the different nodes.

P1 40 W

P2 1 W

Ptotal_Pico 4 W

Table 5

f) Defining the distances between the different nodes.

Distance between Macro-eNB and Macro-eNB user 40m

Distance between Pico-eNB and center Pico-eNB user 10m

Distance between Pico-eNB and range extension Pico-eNB user 20m

Distance between Macro-eNB and group 1 Pico-eNBs center user 110m

Distance between Macro-eNB and group 2 Pico-eNBs center user 150m

Distance between Pico-eNBs and other Pico-eNBs center user 40m

Distance between Pico-eNBs and other Pico-eNBs range extension user 30m

Distance between group 1 Pico-eNBs and Macro-eNB user 80m

Distance between group 2 Pico-eNBs and Macro-eNB user 120m

Table 6

Figure 23: Macro-eNB and Pico-eNBs in a cell

4.2.3.2 Calculating the values of C1 and C2 according to the example.

The path loss is calculated according to the urban Macro-eNB (UMa), for Macro-eNB users, and urban

micro (UMi), for Pico-eNB users, which belong to the ITU channel model in [6] and they were explained

in details in 4.2.2.

After defining the different parameters for this example we calculate the values of C1 and C2 25

according

to this example to find a closed formula for . We start by C1 which is given by

25

C1 and C2 are the same as the ones deduced in section 4.2.1 but adapted to the scenario of the example.

39

). (40)

Putting the value of the interference according to the specifications of the example, the

becomes

. (41)

And since we have 2 groups of Pico-eNBs as shown in Figure 23 we can assume that the number of Pico-

eNBs increases to form 2 circles for group 1 and 2 to maintain the distance from the Macro-eNB as

shown in Figure 2426

.

Figure 24: Increasing the number of picos in the cell

So the previous equation can be rewritten as follows

(

) . (42)

Assuming that there is a specific constant budget for the total power transmitted by all the Pico-eNBs

which can is denoted by so this equation can be re-written as follows

(

) . (43)

Now considering C2 which was equal to

, but since and have specific

values in this example then they are no longer random variables and C2 can be expressed as

26

The reason for this distribution of Pico-eNBs is to simplify the equations by having only 2 channel gain values (one for the first group of Pico-eNBs and the other for the second group of Pico-eNBs).

40

. (44)

Now calculating the values of C1 and C2 according to the path loss and transmitting powers stated above.

C1 = 8.7025 C2 =8.6194

So

So this example shows that the optimum value of can be expressed according to equation (45).

(45)

4.2.4 Example to validate the general model results (without the assumption of Ptotal_Pico)

In this example the previous example is repeated but without the constraint of Ptotal_pico, so we try to

generalize the validation of the result for by removing the assumption that we have a budget for the

Pico-eNBs transmitting power so we go back to equation [19] and rewrite it according to our example as

follows

(

) . (46)

And introducing the values for the power and path loss stated before.

. (47)

Also for the range extension user maximum capacity given by

( ) . (48)

It is simplified to

( ) . (49)

So the ratio

can be expressed in terms of as follows

( ) . (50)

In order to understand this expression we plot it against Np and plot with it

for comparison.

41

Figure 25: Comparison between

and

From this figure we see that

is very close to

even for small Npico, so this validates the

expression for that we deduced before which is given by eq. (30).

This result shows that is dependent on the total number of range extension users in a cell. But logically it should be dependent on the number of range extension users per Pico-eNB instead of the total number

since the resources are reused for each Pico-eNB but this can be explained in the next figure where we

plot the maximum range extension user capacity and also the Macro-eNB user maximum capacity against

the number of Pico-eNBs, which means that we plot the user capacity while changing the number of Pico-

eNBs in the cell and see how the capacity behaves.

Figure 26: Plot of the range extension user capacity against the number of picos.

0 10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

number of picos (Np)

C-re-max/C-macro-max

1/Np

2 3 4 5 6 7 80

20

40

60

80

100

120

140


capacity

range extension user capacity against the numner of picos

Cap

acit

y (b

its/

sec)

Range extension user capacity against the number of picos

42

Figure 27: Plot of the Macro user maximum capacity against the number of picos.

From Figure 26 we see that the range extension user capacity decreases when increasing the number of

Pico-eNBs in the cell. This means that the range extension user capacity is interference limited and that

the capacity depends very much on the interference coming from other Pico-eNBs which in turn depends

on the number of Pico-eNBs. Also from Figure 27 it is obvious that the Macro-eNB user capacity is

almost not affected by the number of Pico-eNBs or in other words the interference caused by the Pico-

eNBs to the Macro-eNB users is not significant.

So this explains the dependence of the alpha calculations on the number of Pico-eNBs or more generally

the total number of range extension users in the cell as will be shown in the next section.

Now we validate the result by trying different user distributions for the same Pico-eNBs distribution

(4 Pico-eNBs) and compare the alpha we get by simulation and that we get using equation (31). As

shown in the following example:

Considering case1, for example, we have 36 Macro-eNB users, 4 range extension users and 10 center

Pico-eNB users.

2 3 4 5 6 7 80

50

100

150

200

250


capacity

macro user maximum capacity against the number of picos

Cap

acit

y (b

its/

sec)

43

Figure 28

The optimized alpha according to simulations, see the intersection point in the figure, is 0.085 while

calculating the alpha value according to the formula gives 0.1. ( =1/ (1+ (36/4)) =0.1). The rest of the

results are listed in the following table

Nrof_Macro-eNB_users Nrof_re_users/Pico-eNB Sim_alpha Calculated_alpha( )

36 1 0.085 0.1

32 2 0.176 0.2

28 3 0.27 0.3

24 4 0.363 0.4

20 5 0.462 0.5

16 6 0.563 0.6

12 7 0.671 0.7

8 8 0.78 0.8

4 9 0.887 0.9

Table 7

As seen from Table 7 the simulation results for are very close to the value of calculated from (31).

4.3 Summary

As a conclusion, from the last subsection, the value of from equation (31) is applicable in

interference limited situations, i.e. situations where Pico-eNBs are causing interference to each

other. Through section 4 a closed form expression for has been deduced and it has been tested

to be valid in the case of the ITU channel model and the Spatial Channel Model (SCM), but it

might not be the best solution in cases where there is no interference between Pico-eNBs.

The equation in (31) will be tested more in the next section where simulations are conducted using

more realistic channel models and bigger networks.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

5

10

15

20

25

30

35

40

45

alpha

capacity

macro user capacity

center pico user capacity

range extension user capacity

Cap

acit

y (b

its/

sec)

44

5. System simulation results

This section is for the simulation results, we start by introducing the simulator used in this section which

is the Raptor simulator, then listing the assumed simulation parameters used and finally presenting the

simulations in different subsections.

The Raptor simulator 5.1

All the simulations in this project are performed using a simulator called Raptor which is a property of

Ericsson. Raptor is an LTE-Advanced system simulator which means that it performs physical layer

simulations.

The simulator is divided into 3 parts:

1) Input parameter files: MATLAB files containing all the simulation parameters that will be used as

input to the simulator.

2) Main simulator: the main body of the simulator which is developed in C++ and this simulator

generates MATLAB result files.

3) Graphical interface: MATLAB graphical interface that processes the MATLAB result files to

illustrate the results in the form of CDFs, bar charts and scatter plots as will be shown in the next

section.

I contributed mainly in creating my own input parameter files and optimizing the graphical interface to

show more illustrative plots.

45

System simulation assumptions 5.2

The criterion that we focus on optimizing is the cell-edge users throughput

27 while keeping a fair level of

average throughput.

The simulation assumptions are listed in the following table28

:

Parameter Description

Network topology 21 cell network (i.e. 7 three-sector sites)

Number of ues 30 ues per cell

Number of Pico-eNBs From 2 to 10 per cell depending on the tested scenario and all the Pico-

eNBs are outdoors and located at predefined locations.

Deployments Configuration 129

and 4b30

[7]

Traffic model Full buffer 31

Range extension offset From 0 to 18 dB depending on the scenario

Downlink scheduling Proportional fair scheduler [2]

Carrier frequency 2 GHz

Path loss mode ITU Channel Model and Spatial Channel Model (SCM)

Downlink link adaptation Ideal link adaptation32

CRS interference modeling Assuming perfect CRS interference cancellation.

Total bandwidth 20 MHz

Antenna tilting According to TR36.819- 12 degrees for Macro-eNB, 0 degrees for Pico-

eNB

Table 8

Simulation results 5.3

5.3.1 Who wins and who loses in terms of throughput in a heterogeneous network

deployment?

Who are the winners and losers in terms of throughput in a Macro-Pico heterogeneous deployment is a

very crucial question, we mean by winners or losers the users who experience an increase or decrease of

throughput when adding the Pico layer to the Macro layer. To answer this question we will compare the

following 2 network deployments:

1) Macro-eNB only deployment: we only have 1 Macro-eNB per cell. 2) Macro-eNB + Pico-eNB deployment: we have 1 Macro-eNB and 4 Pico-eNBs per cell, with no range

extension applied to the Pico-eNBs.

27

Cell edge users

interference mitigation lte

Documents

pico nodes users

macro nodes

pico nodes distributions

low power nodes

different users

range extension area

lower interference

range extension case