on the adjacent channel interference in ultra dense …680026/fulltext01.pdfon the adjacent channel...

Master of Science ThesisStockholm, Sweden 2013

TRITA-ICT-EX-2013:244

S E Y E D M E H D I M I R S H O J A E I

On The Adjacent Channel Interferencein Ultra Dense Deployment

K T H I n f o r m a t i o n a n d

C o m m u n i c a t i o n T e c h n o l o g y

On The Adjacent Channel Interference inUltra Dense Deployment

SEYED MEHDI MIR SHOJAEI

Master of Science Thesis performed atthe Communication Systems Department, KTH Royal Institute of

Technology.November 2013

Examiner: Professor Ben Slimane

Abstract

The giant ever increasing demand for higher data rates and better Quality ofService (QoS) is rapidly growing and operators’ main concern is to supportthe growth of mobile data traffic and address the users’ expectations whileat the same time keeping the costs of services reasonable [1], [2], [3]. This ismore vital in residential and dense urban areas where the reception of themacro signal level becomes weak [4]. Therefore, the implementation of ultradense networks becomes a promising approach which is expected to providegood indoor coverage and higher capacity in residential areas. Nevertheless,the potential degradation of network performance due to severe interferenceoriginated from nearby networks should be deeply studied prior to full-scaleimplementation of ultra dense networks. The main concern of this thesiswork is to investigate the coexistence between two operators in Time Di-vision Duplex (TDD) system which are using adjacent frequency channelsand implemented in the same geographical area. For this purpose, the sys-tem level simulation based on Monte Carlo method is performed to revealthe impact of critical parameters including Adjacent Channel Interferencepower Ratio (ACIR), Uplink-Downlink synchronization between operators,Base-Stations positioning, and Internal walls existence on the system per-formance. Afterwards, the effect of densification on the previous findings isstudied.

Results show that in downlink and uplink, approximately 30 dB and55 dB of ACIR is required, respectively, in order to eliminate the impactof adjacent channel interference. Furthermore, in uplink, synchronizationis necessary when base stations of operators are collocated. In downlink,however, synchronization and collocation is beneficial when signal quality ispoor. On the other hand, it is shown that densification is feasible providedbase stations employ adjustable transmission power model. Moreover, inter-nal walls can improve system performance due to attenuation of interferencesoriginated from surrounding cells.

iii

Acknowledgements

I would like to express my deepest appreciation to my supervisor Dr. KiWon Sung for his patient guidance and valuable advices. I believe withouthis great care and support this path was not possible. Also I would like tothank my examiner Prof. Ben Slimane for his precise comments and helpfulfeedbacks which helped me to finish my thesis work successfully.

Furthermore, I would like to specially thank my family for their constantsupports and encouragements through my whole life. Finally I thank myfriends in Wireless Systems program and Communication Systems depart-ment for all their helps.

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Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Ultra-Dense Networks . . . . . . . . . . . . . . . . . . 11.1.2 TDD systems . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Motivation and Problem Definition . . . . . . . . . . . . . . . 51.4 Thesis Contribution . . . . . . . . . . . . . . . . . . . . . . . 61.5 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Adjacent Channel Interference 92.1 Overview on Adjacent Channel Interference . . . . . . . . . . 92.2 Adjacent Channel Interference power Ratio . . . . . . . . . . 10

3 System Model 133.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Critical Parameters . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Adjacent Channel Interference power Ratio (ACIR) . 133.2.2 Uplink-Downlink synchronization between operators . 133.2.3 Base-Station Positioning . . . . . . . . . . . . . . . . . 163.2.4 Internal Walls Existence . . . . . . . . . . . . . . . . . 16

3.3 Densification . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 Path Loss Model . . . . . . . . . . . . . . . . . . . . . . . . . 173.5 Transmission Power Model . . . . . . . . . . . . . . . . . . . 173.6 SINR Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183.7 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 19

3.7.1 5th and 95th percentiles SINR . . . . . . . . . . . . . . 193.7.2 Blocking Probability . . . . . . . . . . . . . . . . . . . 21

3.8 System Status . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

4 Simulation 234.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . 25

vii

viii Contents

5 Simulation Results and Discussions 275.1 Downlink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.1.1 5th and 95th percentiles Downlink SINR . . . . . . . . 275.1.2 Co-Channel Cooperation . . . . . . . . . . . . . . . . 295.1.3 Densification . . . . . . . . . . . . . . . . . . . . . . . 34

5.2 Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385.2.1 5th and 95th percentiles Uplink SINR . . . . . . . . . . 385.2.2 Co-Channel Cooperation . . . . . . . . . . . . . . . . 415.2.3 Densification . . . . . . . . . . . . . . . . . . . . . . . 44

6 Conclusions and Future Works 476.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

References 49

List of Tables

4.1 Numerical values of simulation parameters . . . . . . . . . . . 25

5.1 Comparison between peak SINR in uplink and downlink . . . 41

ix

List of Figures

2.1 Interference due to emission mask and receiver filter imperfectfunctioning(fVr and flt are the centers of assigned frequencychannel and adjacent frequency channel, respectively) [5] . . . 10

2.2 Adjacent channel interference due to limitations of ACLR andACS [5] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3.1 Network Model, ultra dense deployment . . . . . . . . . . . . 143.2 MS-MS and BS-BS interference [6] . . . . . . . . . . . . . . . 153.3 Uplink and Downlink crossed slots [7] . . . . . . . . . . . . . 163.4 Cumulative Distribution Function . . . . . . . . . . . . . . . 193.5 Pattern similarity between average SINR and high percentile

SINR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

4.1 Network simulation model . . . . . . . . . . . . . . . . . . . . 24

5.1 5th percentile downlink SINR based on ACIR for both LOSand NLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 95th percentile downlink SINR based on ACIR for both LOSand NLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3 Improvement of 5th percentile downlink SINR by co-channelcooperation for both LOS and NLOS . . . . . . . . . . . . . . 32

5.4 Improvement of 95th percentile downlink SINR by co-channelcooperation for both LOS and NLOS . . . . . . . . . . . . . . 33

5.5 Impact of densification on 5th percentile downlink SINR forLOS case by considering a) Adjustable Transmission powermodel, and b) Fixed Transmission Power model . . . . . . . . 35

5.6 Receiver saturation points based on ACIR for given blockingprobability threshold equals 1% . . . . . . . . . . . . . . . . . 37

5.7 5th percentile uplink SINR based on ACIR for both LOS andNLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

5.8 95th percentile uplink SINR based on ACIR for both LOS andNLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.9 Improvement of 5th percentile uplink SINR by co-channel co-operation for both LOS and NLOS . . . . . . . . . . . . . . . 42

xi

xii List of Figures

5.10 Improvement of 95th percentile uplink SINR by co-channelcooperation for both LOS and NLOS . . . . . . . . . . . . . . 43

5.11 Impact of densification on 5th percentile uplink SINR for LOScase by considering a) Adjustable Transmission power model,and b) Fixed Transmission Power model . . . . . . . . . . . . 45

5.12 Receiver saturation points based on ACIR for given blockingprobability threshold equals 1% . . . . . . . . . . . . . . . . . 46

List of Abbreviations

ACBS Adjacent Channel Operator’s Base Station

ACI Adjacent Channel Interference

ACIR Adjacent Channel Interference power Ratio

ACLR Adjacent Channel Leakage Ratio

ACMS Adjacent Channel Operator’s Mobile Station

ACS Adjacent Channel Selectivity

BS Base Station

CCBS Co-Channel Base Station

CCI Co-Channel Interference

CCMS Co-Channel Mobile Station

COI Cell of Interest

FDD Frequency Division Duplex

GSM Global System for Mobile Communications

LOS Line Of Sight

LTE Long Term Evolution

MBS Main Base Station

MMS Main Mobile Station

MS Mobile Station

MWF Multiwall-and-Floor-model

NLOS None-Line-Of-Sight

OFDM Orthogonal Frequency Division Multiplexing

xiii

xiv List of Abbreviations

OFDMA Orthogonal Frequency-Division Multiple Access

QoE Quality of Experience

QoS Quality of Service

SINR Signal to Interference and Noise Ratio

SIR Signal to Interference Ratio

SNR Signal to Noise Ratio

TDD Time Division Duplex

UMTS Universal Mobile Telecommunications System

UTRA UMTS Terrestrial Radio Access

WCDMA Wideband Code Division Multiple Access

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

Chapter 1

Introduction

1.1 Background

1.1.1 Ultra-Dense Networks

The demand for higher data rates and capacity is rapidly increasing duringrecent years due to the existence of diverse wireless devices including per-sonal computers, smart phones and tablets. Each of these devices providesdifferent services (i.e. voice, video or web browsing) and consequently re-quires different data rates [1], [2]. On the other hand, indoor users located atthe macro cell border experience poor signal quality [4]. Therefore reducingthe distance between base station and users is a key solution for improvingthe indoor coverage and increasing the capacity. A promising approach toimplements this idea is through ultra dense deployment of heterogeneousand small cell networks [4].

Small cells are low-powered access points that operate in licensed spec-trum with the range of coverage area from 10 meters to few tens of kilo-meters, which support cellular standards such as Global System for Mo-bile Communications (GSM), Universal Mobile Telecommunications Sys-tem (UMTS), Worldwide Interoperability for Microwave Access (WiMAX),Long Term Evolution (LTE) and Wireless Local Area Network (WLAN).Microcells, Picocells and Femtocells are all referred to as small cells but inthis work, we refer mainly to femtocells as small cells [4], [8].

By deploying small cells and bringing the Base Station (BS) closer tousers, they benefit from higher data rates and more reliable data connec-tions and consequently better Quality of Experience (QoE). Furthermore,power consumption in the Mobile Station (MS) will be reduced and the bat-tery life will be prolonged due to reduced distance between MSs and BS. Onthe other hand, operators benefit from increased area spectral efficiency [9]or as mentioned in [10] ”total number of active users per Hertz per unit

1

2 Chapter 1. Introduction

area”, and offloading data to small cell networks. Since the data traffic istransferred via the broadband connection, it will reduce the traffic conges-tion in macro cell network [4], [10], [11].

Generally, there are two common approaches for small cell deployment.In the first one, small cells can operate on the same frequency band as theunderlying macro cell, which is known as “Co-Channel deployment”. In thesecond approach, however, a separate fragment of spectrum can be assignedto small cells. This is known as “Dedicated deployment”. The advantageof the former approach is the increased spectrum utilization efficiency, butthis approach requires interference management techniques due to high in-terference between macro cell/small cell and small cell/small cell layers. Onthe other hand, the dedicated deployment isolates the macro cell from smallcell and minimizes the co-channel interference between the two layers, butAdjacent Channel Interference (ACI) may arise which can lead to signifi-cant reduction in neighbor system capacity. Furthermore, this is not a costeffective approach, because the radio spectrum is scarce and expensive [12].

Although deploying small cells provides some benefits for both users andoperators, at the same time it brings new challenges that should be tack-led before a large-scale adoption of small cells. The most critical one isinterference management between macro cell and small cells and betweenneighboring small cells. Other challenges include handover and mobilitymanagement, time synchronization, optimum resource allocation and secu-rity [4], [13]. It should be noted that our main concern in this work isinterference management between neighboring small cells. In such hetero-geneous network, both BS and MS are the potential sources of interference.Particularly, in Time Division Duplex (TDD) systems, the portion of allo-cated time slots to uplink and downlink directions may differ in neighboringcells, which leads to the interference between two MSs or between two BSs.We will expand this kind of conflict between uplink and downlink in section3.2.2.

1.1.2 TDD systems

Time Division Duplex (TDD) is a duplex communication scheme used inwireless networks, which employs a single frequency band for both uplinkand downlink. In fact, TDD allocates different portions of time known astime slots to upstream and downstream directions. Time slots are separatedby guard times in order to prevent overlapping and consequently successfuldata transmission [14], [15]. Some well-known systems that employ TDDare:

• IEEE 802.16 WiMAX

1.2. Related Works 3

• Wi-Fi

• UMTS 3G supplementary air interfaces

• TD-CDMA for indoor mobile telecommunications

• TD-SCDMA 3G mobile communications air interface

TDD supports different data types including voice and symmetrical com-munication services and also asymmetrical data services. Contrary to TDD,Frequency Division Duplex (FDD) employs two distinct frequency bandsfor upstream and downstream directions, one for each. FDD is particularlyused for voice and symmetrical communication services [16].

There are many advantages of TDD over FDD. TDD is more efficientin terms of spectrum utilization because it employs a single frequency bandfor both uplink and downlink, while FDD requires two distinct frequencybands which are properly separated from each other. Therefor by consider-ing the scarcity of frequency spectrum, TDD becomes more desirable thanFDD. Furthermore, TDD uses an adaptive ratio between downstream andupstream directions and provides different number of time slots for uplinkand downlink for asymmetrical data types like internet services. This flex-ibility improves system capacity by allocating adequate time slots for eachstream direction. Moreover, TDD reduces the cost and complexity of isola-tion between transmitter and receiver in MS by reusing filters and frequencyresources, while FDD requires enough separation between bands and mul-tiple filters for isolation between transmitter and receiver. Despite all ofits advantages, there are still few drawbacks of TDD systems. Since up-link and downlink share a single frequency band, their corresponding sig-nals may interfere with each other. On the other hand due to switchingpoints between uplink and downlink, transmissions in TDD suffer from dis-continuities which require time synchronization between transmitter andreceiver [14], [16], [17], [18].

1.2 Related Works

The coexistence and interference management between macro cell and smallcells has been widely studied, while coexistence between small cells has re-ceived less attention. In this part, we first review the research studies oncoexistence between macro cell and small cells, and then have a look atcompleted and ongoing researches on the small cells coexistence issue.

As mentioned above, the coexistence of macro cell and small cells re-ceived notable attention. The coexistence of two LTE systems is studied


in [19] and [20]. The former evaluates the macro cell performance degra-dation caused by ACI when other small cells operate on adjacent channel,while the latter studies the performance loss of the micro/pico cells whenthe macro cell operates on the adjacent channel and provides some effectivesuggestions to avoid interference. It is shown in [19] that two LTE systemscould not coexist due to high ACI and some interference mitigation methodsincluding antenna techniques and guard bands are suggested. On the otherhand, [20] shows that the coexistence between two systems is possible exceptfor the cases when macro BS is interfering with the micro/pico BS, and alsowhen macro MS is interfering with the micro BS. Therefore additional 40dB isolation is suggested. The authors of [21] investigated the coexistence ofmacro cell and femtocell by comparing two common spectrum sharing meth-ods including shared spectrum usage and partitioned spectrum usage andpropose a hybrid spectrum sharing to take advantages of their merits. In [22]the feasibility of user deployed femtocells in the same frequency band as anexisting macro cell network is investigated and it is shown that femtocells de-ployment can be achieved with only minor impact on macro cell throughput.The effect of interference from femtocell BSs on the macro MSs is studiedin [23] and [24]. Resource allocation is an interesting solution for interferencemitigation. Consequently, some researches focused on proper resource allo-cation techniques to eliminate the interference between macro cell and smallcells. The authors of [25] investigate the uplink resource allocation problemof femtocells in co-channel deployment with macro-cells and advice a semi-distributed algorithm that first assigns subchannels to femto users and thenallocate power to subchannels. The performance of the proposed algorithmis proved through results which show the capacity improvement of both fem-tocell and macro cell. In [26] the efficiency of resource management betweenmacro cell and femtocells is investigated by considering isolated and coupledmodels, and an efficient resource management solution has been providedfor Orthogonal Frequency-Division Multiple Access (OFDMA) based fem-tocells. The effect of interference from femtocell BSs on the macro MSs andcoverage hole around a femtocell due to close access strategy is evaluatedin [23] and the effect of different resource allocation schemes and correspond-ing drawbacks in terms of femtocell performance and system complexity isstudied. They propose a different frequency allocation for Wideband CodeDivision Multiple Access (WCDMA) macro and small cells and dynamicchannel allocation for OFDMA systems.

Contrary to huge studies on the coexistence of macro cell and smallcells, the research works on coexistence between small cells are still grow-ing. The authors of [27] investigate the interference mitigation schemes forindoor dense deployment of femtocells. They propose a cooperative trans-mission and a semi-static interference mitigation scheme based on fractionalfrequency reuse for indoor dense femtocell networks which are able to yield

1.3. Motivation and Problem Definition 5

significant gains in terms of average throughput. Another one [28], foundthe optimal number of femtocells that maximizes the sum rate gain of thenetwork. A frequency allocation scheme is developed that divide the spec-trum in two disjoint sets, each for macro and femtocell. It has been shownthat although some spectrum is assigned to femtocells, it compensates byincreasing the significant gains from femtocells.

1.3 Motivation and Problem Definition

By reviewing the related works, we observe that most of the studies in thisarea are focused on the coexistence between macro cell and overlaid smallcells. They evaluate either adjacent channel interference or co-channel in-terference and propose interference mitigation techniques. In addition, fewresearch works studied the coexistence possibility between several femtocellsoverlaid on a macro cell network and investigate co-channel interference mit-igation methods.

Up to the best of our knowledge, the coexistence of ultra dense networksincluding small cells that operate on the adjacent frequency channels hasnot been investigated yet.

The lacking of the study in this area is crucial as it is expected thatmost of the data traffic will be carried by femtocells and Wi-Fi in the com-ing years [29]. Therefore, ultra dense deployment of heterogeneous andsmall cell networks becomes unavoidable. On the other hand, as we dis-cussed before, operators would be able to provide better indoor coverageand higher capacity by reducing the cell size and bringing the BSs closerto users. Therefore the competition for finding proper BSs’ locations woulddefinitely arise among operators, which leads to coexistence challenges be-tween them.

In this thesis work, we are interested in investigating the coexistenceamong two different networks implemented by two different operators, whichoperate on adjacent frequency channels in the same geographical area. Weconsider TDD system and will evaluate the parameters that are supposedto have direct impact on the coexistence. For example, it is of great im-portance to study how closely BSs of different networks can operate andcoexist with each other while providing desirable level of service to users andmeeting system requirements including minimum acceptable Signal to In-terference and Noise Ratio (SINR), and maximum allowable blocking prob-ability. Moreover, when different networks are deployed in multi-operatoror multi-frequency band environments, adjacent channel interference acts asa barrier to the coexistence possibility of the networks. Thus considering a


proper value of Adjacent Channel Interference power Ratio (ACIR) seemsto be very essential in ultra dense deployment. Another critical parameteris the uplink-downlink synchronization between operators. As mentionedbefore, in TDD systems, downlink and uplink share the same frequencyband. Due to the asymmetrical nature of data in uplink and downlink, theircorresponding signals can interfere with each other which leads to MS-MSinterference or BS-BS interference in a single cell or adjacent cells operat-ing on the adjacent frequency bands [6]. We elaborate and explain theseparameters in detail in system model, section 3.

1.4 Thesis Contribution

In the previous sections, we discussed the existing research works and re-vealed the missing studies in this area. As explained before, the lacking ofresearches on the coexistence of small cells operating on adjacent bands iscrucial and due to its importance requires deep and thorough study beforefull-scale adoption of small cells.

Therefore, the main contribution of this research work is to investigatethe impact of adjacent channel interference on the performance of small cellnetworks in ultra dense deployment in TDD systems. Please note that weconsider two networks and each network is deployed by distinct operator.Thus more precisely, this thesis work aims to answer the following researchquestions:

1. What is the impact of the following critical parameters on the coexis-tence between two operators using adjacent frequency channels whichare implemented in the same geographical area? (We consider TDDsystem)

Critical parameters including:

(a) Adjacent Channel Interference power Ratio (ACIR)(b) Uplink-Downlink synchronization between operators(c) Base-Stations positioning(d) Internal walls existence

2. How does densification affect the findings of the above question?

1.5. Outline 7

1.5 Outline

The rest of this report is organized as follows:

Chapter 2 briefly reviews the adjacent channel interference and its eval-uation parameters. Chapter 3 elaborates the system model. This chaptercontains the definition of critical parameters, performance metrics and sys-tem status as well as explanation of network model, SINR model and pathloss model. Chapter 4 expands the simulation methodology and parametersused in this research work. Chapter 5 investigates the simulation resultsfor both uplink and downlink by considering all possible scenarios. Finallychapter 6 concludes the findings of the thesis work and wrapping up thereport by providing some suggestions for future studies.

Chapter 2

Adjacent ChannelInterference

2.1 Overview on Adjacent Channel Interference

As explained in detail in [30], the quality of a received wireless signal canbe degraded not only due to equipment noise or the environment, but alsobecause of collision of two signals and their respective bandwidths. When asignal is transmitted, its energy is not concentrated in the center frequencyand spread unequally across over its bandwidth. The power spectral density(PSD) defines how energy is distributed over the bandwidth of the signal.In order to limit the out of band emissions, transmitters use an emissionmask.

Emission mask defines the amount of gain that a transmitted signal willreceive in the center frequency and offsets from it [30]. Since the emissionmask bounds the transmitter’s output signal, it plays an important rolein the ability of a transmitter to coexist with other transceivers in densedeployment. On the other hand, the input signal at the receiver side willexperience attenuation from receiver filter. The emission mask and the filterof transceivers that work on adjacent frequency channels are not ideal andbecause of their imperfect functioning, there is always some leakage of trans-mitted power to the receiver side which cannot be eliminated completely bythe receiver filter and consequently cause adjacent channel interference [31].Figure 2.1 [5] shows the leakage of transmitted power (green color) to thepassband of the receiver which is due to emission mask and receiver filterimperfect functioning.

ACI generally occurs when several transceivers work within the samegeographical area. The near-far effect makes the ACI problem worse. Imag-ine a BS surrounded by several MSs operating on adjacent channels tries to

9

10 Chapter 2. Adjacent Channel Interference

Figure 2.1: Interference due to emission mask and receiver filter imperfectfunctioning(fVr and flt are the centers of assigned frequency channel andadjacent frequency channel, respectively) [5]

Figure 2.2: Adjacent channel interference due to limitations of ACLR andACS [5]

detect a week signal from the cell edge MS. Due to lacking of power controlscheme, BS receives higher level of interference from surrounding MSs ratherthan the desired signal [32].

2.2 Adjacent Channel Interference power Ratio

As mentioned above, due to transmitter non-idealities and imperfect re-ceiver filtering [33] there is always some leakage of the transmitted powerinto adjacent channels. This leakage can be measured by Adjacent ChannelLeakage Ratio (ACLR) [34], which is defined as ”the ratio of the filteredmean power centred on the assigned channel frequency to the filtered meanpower centred on an adjacent channel frequency” [35]. On the other hand,another affecting factor is the ability of the receiver to detect its desirablesignal in the presence of interference originate from adjacent channel [31].This factor is known as Adjacent Channel Selectivity (ACS), which is de-fined as ”a measure of a receiver’s ability to receive a signal at its assignedchannel frequency in the presence of a modulated signal in the adjacent

2.2. Adjacent Channel Interference power Ratio 11

channel. ACS is the ratio of the receiver filter attenuation on the assignedchannel frequency to the receiver filter attenuation on the adjacent channelfrequency” [31]. Figure 2.2 [5] shows the concept of ACLR and ACS andtheir limitations which leads to occurrence of adjacent channel interference.

ACLR and ACS can not be considered separately because both of themaffect the performance of adjacent channel. Therefore, in order to takeinto account the impact of imperfection of both transmitter and receiver,we consider Adjacent Channel Interference power Ratio (ACIR), which isdefined as ”the ratio of the transmission power to the power measured aftera receiver filter in the adjacent channel(s)” [33]. By having ACLR and ACSwe can formulate ACIR as following:

ACS =(PT

PR

)ACLR =

(PT

PL

)where PT is the transmitted power, PL is the leakage power on adjacentchannel and PR is the receiver selectivity on adjacent channel. We rewritethem as:

PR = PT

( 1ACS

)PL = PT

( 1ACLR

)now by defining interference (I ) as:

I = PR + PL

we get:

I = PT

( 1ACS

+ 1ACLR

)=⇒

(I

PT

)=( 1ACS

+ 1ACLR

)

and based on the definition of ACIR we have:

ACIR =(PT

I

)which implies:

1ACIR

=( 1ACS

+ 1ACLR

)(2.1)

All parameters in (2.1) are in linear. It worths to mentioning that lim-itation of MS has a direct impact on adjacent channel performance. Indownlink, the MS’s ACS dominates the BS’s ACLR due to weaker perfor-mance of MS’s receiver filter compare to BS’s non-linear power amplifier.Similarly in uplink, the dominant factor is the MS’s ACLR [33].

Chapter 3

System Model

In this section we elaborate the system model by reviewing the networkmodel, explaining the critical parameters in detail, expanding path lossmodel, transmission power model as well as SINR model, followed by per-formance metrics and system status.

3.1 Network Model

We consider two distinct operators using adjacent frequency channels, whichare deployed in the same geographical area. The TDD mode in UMTSTerrestrial Radio Access (UTRA) has been considered. As shown in thefigure 3.1, we assume a dense urban area where small cells’ BSs of differentoperators are deployed very densely and located close to each other. Theycan even be collocated.

3.2 Critical Parameters

The critical parameters are the core of our research questions and under-standing the concepts is essential. Although they have been reviewed inproblem definition part, we will explain them here in detail.

3.2.1 Adjacent Channel Interference power Ratio (ACIR)

The detailed explanation of ACIR can be found in section 2.2.

3.2.2 Uplink-Downlink synchronization between operators

In TDD systems uplink and downlink utilize the same frequency band butuse different portions of time. Due to asymmetry nature of data, the ratiobetween upstream and downstream data in a frame is not fixed and differs inneighboring cells. Consequently, the uplink and downlink signals of neigh-boring cells may interfere with each other which leads to the MS-MS and

13

14 Chapter 3. System Model

Small Cell BS - Operator 1

Macro Cell BS - Operator 2

Macro Cell BS - Operator 1

Small Cell BS - Operator 2

Figure 3.1: Network Model, ultra dense deployment

BS-BS interference. This is shown in figure 3.2 [6].

As shown in the figure, the victim MS would experience severe interfer-ence from transmitting MS. On the other hand, the interference betweentwo BSs becomes strong in case of having Line Of Sight (LOS). The MS-MS and BS-BS interference may happen either in co-channel or betweenadjacent channels. The latter happens particularly when several operatorsusing adjacent frequency channels are working in the same geographical areaand their corresponding cells overlap. The co-channel interference can beeliminated by synchronization between BSs of a single operator and properusage of sectorized antennas [36]. In this thesis work, we assumed that BSsoperating on co-channel are synchronized, i.e. MS-MS and BS-BS interfer-ence do not happen inside a single operator’s network. The adjacent channelinterference, however, can be decreased by using sharp filters at the receiverside, at a cost of higher complexity and increased implementation price.

But what is the achievable gain if operators agree on a synchronizationscheme such that reduces adjacent channel interference? Answering thisquestion is one of our main concerns in this research work and we aim toevaluate the effect of synchronization between operators on the system per-formance improvement. First, let us briefly review the time slot allocationprocess in TDD systems. As mentioned before, a TDD system is able toallocates time slots for uplink and downlink adaptively based on their corre-sponding traffic loads. Therefore due to this asymmetry, it is quite probable

3.2. Critical Parameters 15

(a) MS-MS interference

(b) BS-BS interference

Figure 3.2: MS-MS and BS-BS interference [6]

that some time slots be used for uplink in a cell, while the neighboring celluse them for downlink transmission. These time slots are know as ”CrossedSlots” [7] which are shown in figure 3.3 [7]. Please note that the BS-BS andMS-MS interferences that are discussed above, take place in crossed slots.It is worth reminding that we assumed the cells belong to a single operatorare synchronized with each other which means there is no crossed slot insidea single operator. We only consider crossed slots as an inter-operator issue,i.e. between cells of different operators.

We define two synchronization situations including ”Uplink-DownlinkMatched” and ”Uplink-Downlink MisMatched”. In our definition, Uplink-Downlink Matched refers to the situation when the two operators are syn-chronized and have matched their allocated uplink and downlink time slots.Uplink-Downlink MisMatched, however, refers to the situation that opera-tors use the crossed slots.


Figure 3.3: Uplink and Downlink crossed slots [7]

3.2.3 Base-Station Positioning

The distance between BSs of different operators is supposed to have consid-erable impact on their coexistence as well as on the system performance. Inthis regard, we define two BS positioning situations including ”Collocation”and ”Separation”. In the former, BSs of both operators are located besideeach other in each cell, while in the latter, the BS of the second operatorthat works on the adjacent frequency band, is located at the cell edge ofthe first operator’s BS. The base-station positioning situations are shownin figure 4.1 in section 4.1, where we discuss about the network simulationmodel.

3.2.4 Internal Walls Existence

In this study, we also evaluate the impact of internal walls existence onsystem performance. For the sake of simplicity, through the whole of thisreport, internal walls existence case is called None-Line-Of-Sight (NLOS),and when there is no internal wall, we call it Line-Of-Sight (LOS).

3.3 Densification

Densification, means reducing the coverage area of the BS and bringing theusers closer to it. The main advantage of densification is the area capacityimprovement. Imagine an ultra-dense network where the small cells’ BSsare densely deployed in the given area. Our layout model which is shownin the figure 4.1, is a sampled sector of the whole network area. Thus, bychanging cell size, we are able to investigate the impact of densification onsystem performance.

3.4. Path Loss Model 17

3.4 Path Loss Model

In this thesis work we employ a general indoor Multiwall-and-Floor-model(MWF) [37], [38] and we adopt it for a single floor building as follows:

Lij(dB) = L0 + 10 α log10(r) +Nwall∑k=1

Lwall (3.1)

where Lij is the path loss between transmitter i and receiver j, L0 is thepath loss at distance 1 meter which is found by (3.2), α is the path lossexponent, r is the distance between transmitter i and receiver j, and Nwall

is the number of walls with Lwall penetration loss. We assumed that allinternal walls are of the same material and have the same penetration loss.Free space path loss at distance d0 meter is equal to:

L0 =(4πd0

λ

)2(3.2)

where L0 is in linear and λ is the wavelength.

3.5 Transmission Power Model

In uplink direction we employ a power control model based on the level ofreceived Signal to Noise Ratio (SNR) such that ensures the same power re-ception from users regardless of their positions and distances to the servingBS. This method not only prolongs the user’s battery life but also decreasesthe level of interference generated by users to their surroundings. Let SNRce

and SNRa be the the received SNR from the cell edge user and a user lo-cated in an arbitrary position inside the cell, respectively.

Thus we have:

SNRce = SNRa =⇒ Pce Gce = Pa Ga (3.3)

consequently:

Pa = Pce Gce

Ga(3.4)

G denotes the path gain between user and serving BS. It is assumed thatcell edge user experiences highest path loss to the serving BS, thus transmitswith the maximum power. By replacing Gce with (Lmax)−1 and Ga with(La)−1 we get:

Pa = Pmax La

Lmax(3.5)


where Pmax is the maximum transmission power which belongs to cell edgeuser, Lmax is the path loss between cell edge user and serving BS, and La

is the existing path loss between an arbitrary user and serving BS.

In downlink, we consider two different transmission power models includ-ing Adjustable Transmission power model and Fixed Transmission powermodel. In the former one, BSs are able to adjust their output power basedon their coverage area before and after densification. Similar to uplink, BSsadjust the output power to guarantee the minimum required SNR at thecell border. Contrary to former model, in the latter one, BSs fix the outputpower once and keep it constant regardless of the cell size.

3.6 SINR Model

As explained before in section 3.2.2, in MisMatched situation, MSs and BSsoperating on adjacent channel are the sources of interference in downlinkand uplink , respectively. Please note that Mismatch is an inter-operatorissue, because the cells belong to each of operators are synchronized in timeslot allocation, thus there is no Mismatch in intra-operator transmission.Therefore, we can formulate the downlink and uplink SINR denoted by Γfrom the transmitter i to the receiver j as follows:

Γ ij(DL,Matched) = PBSiGij∑K

k=1 PBSkGkj +∑L

l=1 PBSlGlj +N

(3.6)

Γ ij(DL,MisMatched) = PBSiGij∑K

k=1 PBSkGkj +∑L

l=1 PMSlGlj +N

(3.7)

Γ ij(UL,Matched) = PMSiGij∑K

k=1 PMSkGkj +∑L

l=1 PMSlGlj +N

(3.8)

Γ ij(UL,MisMatched) = PMSiGij∑K

k=1 PMSkGkj +∑L

l=1 PBSlGlj +N

(3.9)

where P is the output power of the transmitter, G is the channel gainbetween transmitter and receiver, K is the number of co-channel cells, L isthe number of adjacent channel cells, and N is the noise power.

3.7. Performance Metrics 19

3.7 Performance Metrics

We use two different performance metrics which investigate the system per-formance from different aspects and are supposed to exploit the coexistencepossibility between two operators.

3.7.1 5th and 95th percentiles SINR

Percentile is a value or point in a data set that defines how many percentagesof the data fall below that point [39]. Figure 3.4 demonstrates the cumula-tive distribution function of SINR. As shown in the figure, the 5th percentileis the point in the data set that 5 percent of the data placed below that.Similarly 95th means this point is higher than 95 percent of other values inthe data set.

−30 −20 −13.9 0 10 15 20 30

0.05

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

0.95

SINR (dB)

CD

F

Figure 3.4: Cumulative Distribution Function

In our study, percentiles are used to exploit the system quality of service.More precisely, the 5th and 95th percentiles SINR reveal how poor and strongare the received signals, respectively. We first intended to use average SINR.But the drawback was the dominancy of higher SINR in the average, whichmade it inappropriate for our purpose. Figure 3.5 represents the average ofdownlink SINR and the 95th percentiles downlink SINR, respectively. Thesimilarity of the pattern between corresponding curves in both graphs showsthe fact that higher values of SINR contribute more in the average compareto lower values of SINR.


0 5 10 15 20 25 30 35 40 45−5

0

5

10

15

20

25

30

35

ACIR (dB)

SIN

R (

dB)

Average Downlink SINR

Col, MatchedSep, MatchedCol, MismatchedSep, Mismatched

(a) Average downlink SINR

0 5 10 15 20 25 30 35 40 45

0

5

10

15

20

ACIR (dB)

SIN

R (

dB)

95th percentile Downlink SINR

Col, Matched

Sep, Matched

Col, Mismatched

Sep, Mismatched

(b) 95th percentile downlink SINR

Figure 3.5: Pattern similarity between average SINR and high percentileSINR

3.8. System Status 21

3.7.2 Blocking Probability

The main task of a receiver is to recognize and amplify the desired signal inthe presence of interference and background noise. Reception of significantinterference power can easily overwhelm the receiver and prevents it fromdetecting desired signal [40]. This situation is known as ”Receiver Block-ing”. In order to investigate the severity of interference and also coexistencepossibility of several entities, we define a threshold level which determinesthe maximum allowable amount of receiving interference that do not leadto receiver saturation, and call it saturation point. Receivers with highersaturation points are more robust. Therefore, Blocking Probability can beexpressed with the following formula:

Blocking Probability = Pr[Interference > Saturation point] (3.10)

The higher the blocking probability is, the stronger the interference is, orthe weaker the receiver is.

3.8 System Status

We have already defined synchronization and BS positioning situations insections 3.2.2 and 3.2.3. Since these situations are intertwined, it is notpossible to evaluate them separately. Thus by combining the positioningand synchronization situations, we can define the system status as follows:

• “Collocated, Matched”

• “Separated, Matched”

• “Collocated, MisMatched”

• “Separated, MisMatched”

Since the system always work in one of these four status, we are able tocompletely study the system behavior in different scenarios.

Chapter 4

Simulation

In chapter 3, we have discussed the system model and defined differentsystem situations and status which able us to investigate the system per-formance in different scenarios. In this chapter, we review the employedsimulation methodology and simulation parameters which are used to de-rive the results.

4.1 Methodology

We considered a square area containing nine cells with equal spaces, wherethe cells can be isolated by internal walls. Please note that in the simula-tion procedure, BSs’ positions are not random, i.e. they are either locatedbeside each other (collocated), or placed at the cell border of each other(separated). Figure 4.1 shows our network simulation model for both ”col-location” and ”separation” situations. Two short-range and low-power BSsequipped with omni-directional antenna are located in each cell, one for eachoperator. It is assumed that the network is fully loaded which means at anygiven time there is one active user from each operator in every cell, whichare uniformly distributed over the cells’ area. Thus, we employed the MonteCarlo method in order to cover all possible MSs’ placement. The simulationconsists of 50000 snapshots which guarantee the consistency of the resultsand eliminate fluctuations in the graphs.

As noted before, we considered two distinct operators. Therefore, wecall the operator of interest as ”Main Operator” and the interfering oneas ”Adjacent channel Operator”. In figure 4.1, BSs and MSs of the mainoperator are shown by black color and BSs and MSs of the adjacent channeloperator are shown by red color. For the sake of clarity, we call the centralcell, the Cell of Interest (COI). Furthermore, in our simulation, only theBS and MS of the main operator in the cell of interest are considered asreceivers in uplink and downlink, respectively. Therefore, other BSs and

23

24 Chapter 4. Simulation

MSs of both operators are considered only as the sources of interference.For the rest of this report, we call the BS and MS of the main operatorwhich are located in the cell of interest as Main Base Station (MBS) andMain Mobile Station (MMS), respectively. Moreover, the BSs and MSs ofthe main operator located in 8 surrounding cells are called as Co-ChannelBase Station (CCBS) and Co-Channel Mobile Station (CCMS). Finally wecall the BSs and MSs of the adjacent channel operator located in all 9 cells asAdjacent Channel Operator’s Base Station (ACBS) and Adjacent ChannelOperator’s Mobile Station (ACMS). Please note that in this report, we use”user” and ”MS” terms interchangeably.

(a) Collocation

(b) Separation

Figure 4.1: Network simulation model

As shown in the figure 4.1, we take into account just the first tier ofinterferers, which is enough because the effect of second and third tiers isnegligible. In every snapshot in uplink, each MS senses its surrounding tofind the best serving BS that provides highest quality of signal. This is doneby calculating the existing path losses to all nearby BSs of the serving oper-ator. After connecting to the BS, the MS adjusts its output power to assuresthe required SNR at the BS side. We assumed 30 dB SNR for both uplinkand downlink. Finally, the simulator calculates the received SINR at MBSbased on the desired signal from MMS and total interference generated byMSs of both operators from all cells. Please note that it is possible one oper-

4.2. Simulation Parameters 25

ator transmits in upstream, and another operator transmits in downstream.As explained before in section 3.2.2, this situation is called Mismatched.Imagine the main operator is in uplink, while the adjacent channel operatortransmits in downlink. Therefore, in this situation, the sources of interferesare both CCMSs and the ACBSs. For better understanding of SINR calcu-lation in Matched and Mismatched cases, the reader is referred to section 3.6.

Similarly to uplink, in every snapshot in downlink, BSs transmit basedon their employed power model which is explained in section 3.5. The simu-lator calculates the received SINR at MMS side based on the desired signalfrom MBS and total interference generated by CCBSs and ACBSs. Mis-matched can also happen in downlink as explained above.

Please note that since our purpose was to evaluate the effect of synchro-nization, it was enough to consider only one time slot of the whole frame.

For densification, we reduced the cells’ area, which leads to the cell to celldistance reduction. We used the wrap-around technique in order to takinginto account all potential sources of interference to the cell of interest. Pleasenote that BSs can employ either adjustable transmission power model orfixed transmission power model. More details of transmission power modelsare explained in section 3.5 and the impact of using each power model isinvestigated in next chapter.

4.2 Simulation Parameters

Table 4.1 contains the numerical values of simulation parameters.

Parameter ValueCell Radius 40 mFrequency 2.4 GHzBandwidth 10 MHzWhite noise power density -174 dBm/HzBlocking probability threshold 1 %BS and Ms Noise Figure 7 dBBS and Ms Antenna Gain 0 dBiRequired SNR 30 dBLwall 10 dBα 3L0 40.2 dB

Table 4.1: Numerical values of simulation parameters

Chapter 5

Simulation Results andDiscussions

In this section, the numerical results and graphs, which are derived basedon the simulation method and simulation parameters, will be investigatedso as to reveal the unknown characteristics of the system and provide usdeep understanding of system behavior in different scenarios and situations.For this purpose, we first present the system performance based on thepredefined metrics and then concentrate on BSs densification and its impacton system performance, for both downlink and uplink, respectively.

5.1 Downlink

5.1.1 5th and 95th percentiles Downlink SINR

Figure 5.1 shows the 5th percentile downlink SINR based on different ACIRlevels between two operators, considering both LOS and NLOS cases as wellas all four possible cases of system status, i.e. ”Collocated, Matched”, ”Sepa-rated, Matched”, ”Collocated, MisMatched”, and ”Separated, MisMatched”.From the figure we can observe that the system has the best performance in”Collocated, Matched” case, independent of wall existence. This is due tocollocation of the MBS and ACBS located in the cell of interest which leadsto the reception of approximately same amount of signal and interferencepowers by the MMS when ACIR is zero. Obviously, the effect of ACI willbe decreased by increasing ACIR.

In LOS and ”Separated, Matched” case, the system experiences the high-est SINR degradation which belongs to users located close to ACBS and farfrom MBS. Thus due to constant transmission power of BSs, closer usersto ACBS would receive higher interference while at the same time receivelower signal from MBS. On the other hand, MisMatched case (blue and green

27

28 Chapter 5. Simulation Results and Discussions

0 5 10 15 20 25 30 35 40 45−18

−16

−14

−12

−10

−8

−6

−4

−2

ACIR (dB)

SIN

R (

dB)

5th percentile Downlink SINR, No Wall


(a) LOS

0 5 10 15 20 25 30 35 40 45−15

−10

−5

0

5

10

ACIR (dB)

SIN

R (

dB)

5th percentile Downlink SINR, Wall Loss = 10dB


(b) NLOS

Figure 5.1: 5th percentile downlink SINR based on ACIR for both LOS andNLOS

5.1. Downlink 29

curves in fig. 5.1) has better performance than ”Separated, Matched”. Thiscan be explained by reminding the fact that in downlink and MisMatchedcase, ACMSs in all cells are the sources of interference. They can adjusttheir transmission powers based on the existing path loss to their servingACBSs . Therefore MMS suffers less from interference compare to ”Sepa-rated, Matched” case. Furthermore, we can observe that both Collocationand Separation in MisMatched has approximately the same performance(less than 1 dB difference) which is again due to the users’ ability to adjusttheir transmission power.

In NLOS case due to high wall penetration loss, interferences from ad-jacent cells are significantly attenuated and the maximum achievable SINRis increased around 10 dB compare to LOS case. We can observe that therequired ACIR for eliminating the effect of ACI is 20 dB and 30 dB for LOSand NLOS cases, respectively. Please note that by eliminating ACI we meanthe area in the graph where curves corresponding to all four cases of systemstatus encounter or become very close to each other.

Figure 5.2 shows the 95th percentile downlink SINR for both LOS andNLOS cases. Contrary to 5th percentile, in 95th percentile which belongs tothe users experiencing highest quality of service, the ”Collocated, Matched”case has the worst performance regardless of wall existence. This is quitereasonable because in ”Collocated, Matched”, due to constant transmissionpower of BSs, the whole cell is covered with the same level of both signal andinterference (when ACIR is zero). We can easily see that the difference be-tween 5th percentile and 95th percentile in LOS and ”Collocated, Matched”case is just 6.8 dB, while this difference for other three system status isaround 28 dB which is approximately 4 times bigger than the former one.Thus we can conclude that in ”Collocated, Matched” case, the quality ofsignal remains more or less the same regardless of users’ locations in thecell. On the other hand, it is obvious that the only effect of wall existenceis the higher achievable SINR and it does not change the system behaviorfrom other aspects.

5.1.2 Co-Channel Cooperation

The co-channel interference can significantly degrade system performance,and an advanced mitigation scheme will absolutely boost system perfor-mance to a desirable level. However, the main question that needed to beanswered prior to any implementation is: “What is the potential achiev-able gain of such co-channel cooperation at the cost of system complexityand possible traffic congestion?” Figure 5.3 and 5.4 exploit the system per-formance improvement by employing an advanced interference mitigationtechnique that totally eliminates co-channel interference. The 5th percentile


0 5 10 15 20 25 30 35 40 45−5

0

5

10

15

20

ACIR (dB)

SIN

R (

dB)



(a) LOS

0 5 10 15 20 25 30 35 40 45−5

0

5

10

15

20

25

30

35

ACIR (dB)

SIN

R (

dB)



(b) NLOS

Figure 5.2: 95th percentile downlink SINR based on ACIR for both LOSand NLOS

5.1. Downlink 31

and 95th percentile downlink SINR is investigated for both LOS and NLOScases.

As expected, system performance is improved dramatically after apply-ing co-channel cooperation technique. The 48 dB peak SINR happens in95th percentile in both LOS and NLOS which belongs to users located closeto MBS and receive high amount of signal. Please note that the peak SINRoccurs when ACIR is 55 dB. Thus both co-channel interference and adjacentchannel interference are eliminated.

Results show that divergence points of curves before and after cooper-ation occur with the higher level of ACIR in NLOS compare to LOS. Thisis mainly because in NLOS case, co-channel interference is attenuated dueto high wall penetration loss and adjacent channel interference is dominant.Therefor, users in LOS case benefit more from co-channel cooperation com-pare to NLOS case, due to higher amount of existing co-channel interference.


0 10 20 30 40 50−20

−15

−10

−5

0

5

10

15

20

25

30

ACIR (dB)

SIN

R (

dB)


Col, Matched

Sep, Matched

Col, Mismatched

Sep, Mismatched

Corp−Col, Matched

Corp−Sep, Matched

Corp−Col, Mismatched

Corp−Sep, Mismatched

(a) LOS

0 10 20 30 40 50−15

−10

−5

0

5

10

15

20

25

30

ACIR (dB)

SIN

R (

dB)


Col, Matched

Sep, Matched

Col, Mismatched

Sep, Mismatched

Corp−Col, Matched

Corp−Sep, Matched

Corp−Col, Mismatched

Corp−Sep, Mismatched

(b) NLOS

Figure 5.3: Improvement of 5th percentile downlink SINR by co-channelcooperation for both LOS and NLOS

5.1. Downlink 33

0 10 20 30 40 50−5

0

5

10

15

20

25

30

35

40

45

50

ACIR (dB)

SIN

R (

dB)


Col, MatchedSep, MatchedCol, MismatchedSep, MismatchedCorp−Col, MatchedCorp−Sep, MatchedCorp−Col, MismatchedCorp−Sep, Mismatched

(a) LOS

0 10 20 30 40 50−5

0

5

10

15

20

25

30

35

40

45

50

ACIR (dB)

SIN

R (

dB)



(b) NLOS

Figure 5.4: Improvement of 95th percentile downlink SINR by co-channelcooperation for both LOS and NLOS


5.1.3 Densification

So far we have seen the effect of ACI on system performance and investigatedthe impact of critical parameters on it. At this point, we are interested toevaluate the effect of densification on our previous findings.

Figure 5.5, shows the 5th percentile downlink SINR based on ACIR inLOS case before and after densification by considering two different BSspower control models, i.e. Adjustable Transmission power and Fixed Trans-mission Power, which are explained in detail in system model, section 3.

The first important observation from the graphs is that in Matched case,the SINR level remains the same after densification regardless of the typeof power control model employed by BSs. This is reasonable because inMatched case, CCBSs and ACBSs are the sources of interference which fol-low the same power control model as the MBS does, thus the level of Signalto Interference Ratio (SIR) does not change after densification.

In MisMatched case, however, the type of power control model signif-icantly affects the received signal quality after densification. First, noticethat CCBSs and ACMSs are the sources of interference in MisMatched case.When Adjustable Transmission power model is used by BSs, they always ad-just their transmission power to assure the 30 dB SNR for cell edge users.Thus by reducing the cell size, BSs decrease their corresponding transmis-sion powers. On the other hand, ACMSs modify their transmission powerbased on the existing path loss to their corresponding serving BSs. There-fore in this case, MBS, CCBSs and also ACMSs adjust their transmissionpower which leads to the same performance after densification. Contrary toAdjustable Transmission power model, when BSs utilize Fixed TransmissionPower model, although ACMSs reduce their transmission power by cell sizereduction, CCBSs and MBS still transmit with their fixed power which is setbased on the first sensing of the cell coverage area. Therefore, the level ofSIR increases and we observe SINR improvement after densification. Pleasenote that although figure 5.5 shows that SINR is improved after densifica-tion, we still need to investigate system performance from other aspects.For this purpose, we evaluate the blocking probability before and after den-sification.

Figure 5.6 shows the receiver saturation points based on different levelsof ACIR for the given blocking probability threshold equals to 1%. As wesee in the first graph which belongs to the case when BSs employ adjustabletransmission power model, the receiver saturation points remain the sameeven after densification. However, as shown in the second graph, the level ofsaturation points in Matched case are increased around 8 dB which means

5.1. Downlink 35

0 5 10 15 20 25 30 35 40 45−18

−16

−14

−12

−10

−8

−6

−4

−2

ACIR (dB)

SIN

R (

dB)

Densification, Adjustable BS Tx Power Model

R=40(m), Col, MatchedR=40(m), Sep, MatchedR=40(m), Col, MismatchedR=40(m), Sep, MismatchedR=20(m), Col, MatchedR=20(m), Sep, MatchedR=20(m), Col, MismatchedR=20(m), Sep, Mismatched

(a) Adjustable Transmission power model

0 5 10 15 20 25 30 35 40 45−18

−16

−14

−12

−10

−8

−6

−4

−2

ACIR (dB)

SIN

R (

dB)

Densification, Fixed BS Tx Power Model


(b) Fixed Transmission Power model

Figure 5.5: Impact of densification on 5th percentile downlink SINR for LOScase by considering a) Adjustable Transmission power model, and b) FixedTransmission Power model


either more complex and robust receivers are required for the same levelsof ACIR, or higher level of ACIR should be employed in order to providethe same blocking probability compared to the system performance beforedensification. Thus it is desirable that BSs employ the adjustable trans-mission power model which leads to reasonable system performance afterdensification.

5.1. Downlink 37

0 5 10 15 20 25 30 35 40−70

−65

−60

−55

−50

−45

−40

−35

ACIR (dB)

Rec

eive

r S

atur

atio

n P

oint

(dB

m)

Blocking probability threshold = 1%, Adjustable BS Tx Power Model


(a) Adjustable Transmission Power model

0 5 10 15 20 25 30 35 40−70

−65

−60

−55

−50

−45

−40

−35

−30

−25

ACIR (dB)

Rec

eive

r S

atur

atio

n P

oint

(dB

m)

Blocking probability threshold = 1%, Fixed BS Tx Power Model


(b) Fixed Transmission Power Model

Figure 5.6: Receiver saturation points based on ACIR for given blockingprobability threshold equals 1%


5.2 Uplink

In the previous part we have thoroughly investigated the system performancein downlink from different aspects in detail and evaluated the effect of criticalparameters and densification on system performance. In this part we willuse the same performance metrics and follow the same simulation methodas we did in downlink part, in order to investigate the system performancein uplink since the system is supposed to behavior differently in uplink.

5.2.1 5th and 95th percentiles Uplink SINR

Figure 5.7 shows the 5th percentile uplink SINR based on ACIR for bothLOS and NLOS. It is obvious that the “Collocated, MisMatched” has muchworse performance than other three system status, which is due to the se-vere interference from collocated ACBS to the MBS. Specifically in LOS,the interference is too strong that even after employing 35 dB of ACIR, thesystem performance is still lower than the performance of other three caseswhen ACIR is zero. Surprisingly, “Separated, MisMatched” has very closeperformance to “Collocated, Matched”, because in the former, MBS receivesconstant interference from surrounding ACBSs and also constant signal fromMMS regardless of its location. Similarly, in ”Collocated, Matched”, bothMMS and ACMS more or less create the same level of signal and interferenceto the MBS. Finally in “Separated, Matched” case, although MBS alwaysreceives constant amount of signal from MMS, still nearby ACMS and alsoACMSs located in adjacent cells’ borders create high amount of interferencewhich degrade the SINR.

As we expected in NLOS case the maximum achievable SINR is in-creased, similar to downlink. Please note that contrary to other three systemstatus that increase their performance in NLOS, “Collocated, MisMatched”still has the same performance as it does in LOS. For example “Separated,Matched” case at 20 dB ACIR, can provide -2 dB and 6 dB SINR in LOSand NLOS, respectively, but the level of SINR in “Collocated, MisMatched”case is -29 dB in both LOS and NLOS. This phenomenon shows the factthat the collocated ACBS dominants existing interferences originated fromadjacent cells, thus the only solution for ACI reduction is high level of ACIR.

Figure 5.8 shows the 95th percentile uplink SINR in LOS and NLOScases. Contrary to downlink, the ”Collocated, Matched” case has approx-imately the same performance as ”Separated, Matched” and ”Separated,MisMatched” cases regardless of wall existence. NLOS cases.

5.2. Uplink 39

0 10 20 30 40 50 60−50

−45

−40

−35

−30

−25

−20

−15

−10

−5

0

ACIR (dB)

SIN

R (

dB)

5th percentile Uplink SINR, No Wall


(a) LOS

0 10 20 30 40 50 60−50

−40

−30

−20

−10

0

10

ACIR (dB)

SIN

R (

dB)

5th percentile Uplink SINR, Wall Loss = 10dB


(b) NLOS

Figure 5.7: 5th percentile uplink SINR based on ACIR for both LOS andNLOS


0 10 20 30 40 50 60−50

−40

−30

−20

−10

0

10

ACIR (dB)

SIN

R (

dB)



(a) LOS

0 10 20 30 40 50 60−50

−40

−30

−20

−10

0

10

20

ACIR (dB)

SIN

R (

dB)



(b) NLOS

Figure 5.8: 95th percentile uplink SINR based on ACIR for both LOS andNLOS

5.2. Uplink 41

Percentile Uplink Downlink5th percentile - LOS -1.3 dB -2.8 dB5th percentile - NLOS 9.18 dB 8.3 dB95th percentile - LOS 7.6 dB 19 dB95th percentile - NLOS 19.8 dB 30 dB

Table 5.1: Comparison between peak SINR in uplink and downlink

Table 5.1 compares the achievable peak SINR for both uplink and down-link in LOS and. The comparison shows an interesting result that althoughin 5th percentile both uplink and downlink can reach the same peak SINR,but downlink is able to provide approximately 10 dB higher peak SINR in95th percentile.

5.2.2 Co-Channel Cooperation

In this part we apply the same co-channel interference mitigation schemethat we used in downlink part, in order to evaluate the system performanceafter interference mitigation and compare the results with downlink to re-veal any meaningful difference of similarity between them.

Figure 5.9 and 5.10 demonstrate the 5th and 95th percentiles uplink SINRbased on ACIR for LOS and NLOS after Co-Channel Interference (CCI)mitigation, respectively. The maximum achievable SINR in both graphsis around 28 to 30 dB for higher values of ACIR regardless of wall exis-tence, which is closely equal to the 30 dB required SNR. The reason of thisequality is that CCI and ACI are already eliminated by means of employedco-channel cooperation scheme and also high levels of ACIR, respectively.Thus SINR becomes equal to SNR.

The divergence points of ”Collocated, MisMatched” curves (blue color)before and after CCI mitigation happen only at very high level of ACIRwhich demonstrate the severity of ACI from collocated ACBS that dominatesother existing interferences from adjacent cells. It is very interesting thatin 95th percentile SINR in NLOS, even by considering 65 dB of ACIR, theperformance of ”Collocated, MisMatched” after CCI mitigation is still belowthe performances of other cases before CCI mitigation.


0 10 20 30 40 50 60−50

−40

−30

−20

−10

0

10

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SIN

R (

dB)



(a) LOS

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ACIR (dB)

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R (

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(b) NLOS

Figure 5.9: Improvement of 5th percentile uplink SINR by co-channel coop-eration for both LOS and NLOS

5.2. Uplink 43

0 10 20 30 40 50 60−50

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Figure 5.10: Improvement of 95th percentile uplink SINR by co-channelcooperation for both LOS and NLOS


5.2.3 Densification

5th percentile uplink SINR of the system before and after densification inLOS is shown in figure 5.11. Please note that in downlink, system perfor-mance remains the same after densification. In uplink, however, adjustabletransmission power model leads to around 10 dB improvement in ”Collo-cated, MisMatched”. Although this result is interesting, it is reasonablebecause collocated ACBS adjusts its transmission power after densificationand makes less interference to serving BS. The reader might ask why theperformance of ”Separated, MisMatched” is not changed. This question canbe answered by referring to the utilized transmission power model. In ad-justable transmission power model, BSs modify their output power based onthe cell coverage area in order to ensure 30 dB SNR at cell borders. Sincein ”Separation” BSs of two operators are located at the cell edge of eachother, the MBS always receives the same amount of interference from nearbyACBSs regardless of the cell size. On the other hand, curves of Matched casedo not change after densification, because both sources of desirable signaland interference are MMS and ACMS, respectively, which can adjust theiroutput power to ensure 30 dB SNR in MBS side and ACBS side, respectively.

When fixed transmission power model is employed by BSs, all systemstatus curves remain the same except the curve of ”Separated, MisMatched”.As we explained above, in separation BSs of different operators are locatedat the cell edge of each other. Since BSs keep their transmission power con-stant even after densification, the level of interference from ACBS becomesmore sever after densification.

Although 5th percentile of system SINR would provide interesting find-ings, we still need to have a look at another performance metric to reconfirmabout the system behavior after densification. Figure 5.12 demonstrates thereceiver saturation points based on different levels of ACIR for the givenblocking probability threshold equals to 1% in LOS case. The results arequite compatible with findings from SINR graphs. Interestingly after densi-fication, saturation points in ”Collocated-MisMatched” case are reduced byemploying adjustable transmission power model. However, in ”Separated,MisMatched” case, we observe increased saturation points by employingfixed transmission power model. This finding reveals the fact that by usingadjustable transmission power model, uplink benefit more from densificationthan downlink, because the level of saturation points decrease which showsreduction of interference.

5.2. Uplink 45

0 10 20 30 40 50 60−50

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0

ACIR (dB)

SIN

R (

dB)

Densification, Adjustable BS Tx Power Model


(a) Adjustable Transmission power model

0 10 20 30 40 50 60−50

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ACIR (dB)

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Densification, Fixed BS Tx Power Model


(b) Fixed Transmission Power model

Figure 5.11: Impact of densification on 5th percentile uplink SINR for LOScase by considering a) Adjustable Transmission power model, and b) FixedTransmission Power model


0 10 20 30 40 50 60

−65

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Rec

eive

r S

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atio

n P

oint

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m)

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(a) Adjustable Transmission Power model

0 5 10 15 20 25 30 35

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eive

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(dB

m)

Blocking probability threshold = 1%, Fixed BS Tx Power Model

R=40(m), Col, Matched

R=40(m), Sep, Matched

R=40(m), Col, Mismatched

R=40(m), Sep, Mismatched

R=20(m), Col, Matched

R=20(m), Sep, Matched

R=20(m), Col, Mismatched

R=20(m), Sep, Mismatched

(b) Fixed Transmission Power Model

Figure 5.12: Receiver saturation points based on ACIR for given blockingprobability threshold equals 1%

Chapter 6

Conclusions and FutureWorks

6.1 Conclusions

Our main concern in this thesis work was to evaluate the impact of ad-jacent channel interference on the system performance and investigate thecoexistence possibility between different operators using adjacent frequencychannels, which are deployed in the same geographical area. We defined sev-eral critical parameters which were expected to have direct impact on thesystem performance. Then by means of performance metrics and utilizingsimulation model, we derived the results based on different scenarios.

Results show that in downlink we require approximately 30 dB of ACIRin order to mitigate adjacent channel interference. In uplink, the requiredACIR is around 55 to 60 dB which is almost difficult to achieve and revealsthe severity of adjacent channel interference in ”Collocated, MisMatched”case. As explained already, the severity is due to the collocation of MBSand ACBS in the cell of interest, as well as uplink-downlink Mismatchedbetween operators. Therefore while the MBS is receiving, ACBS is trans-mitting, which leads to significant interference.

The effect of BSs positioning and uplink-downlink synchronization areintertwined, thus we have to interpret their impacts simultaneously. Oursimulation results demonstrate that in upstream direction, uplink-downlinksynchronization between operators is necessary when BSs are collocated,otherwise we do not gain much from synchronization. In downstream direc-tion, however, we observe that the benefit of synchronization is dependent onthe BSs positioning as well as signal quality. More precisely, in 5th percentileSINR, i.e. when signal is poor, synchronization and collocation enhance sys-tem performance significantly. However, in 95th percentile, i.e. when signal

47

48 Chapter 6. Conclusions and Future Works

quality is good, synchronization becomes unnecessary.

Furthermore, densification is feasible provided BSs employ adjustabletransmission power model, otherwise the blocking probability will be in-creased. Although more robust and complex receivers can be used in orderto overcome this problem, the implementation costs will be increased no-tably. On the other hand, it has been shown that, uplink benefits morefrom densification than downlink. By comparing figures 5.6a and 5.12a, wecan observe that in the latter one, receiver saturation points are decreasedsignificantly in ”Collocated, MisMatched” case (blue curve) which demon-strates the reduction of interference. However, as shown in the figure 5.6a,receiver saturation points remain the same after densification.

Finally, we observe that internal walls existence improves system perfor-mance considerably, which is due to significant attenuation of interferencesoriginated from adjacent cells.

In this research work, we have reviewed and evaluated the benefits andchallenges of small cells deployment from technical point of view. However,there are other aspects of small cells that are beneficial for the society andsurrounding environment. More precisely, implementation of small cells pro-vide good coverage almost every where in residential areas which increasethe customers satisfactions. On the other hand, the reduction of macro cellsimplementation leads to more environmentally-friendly procedure.

6.2 Future Works

In this research work, we have thoroughly investigated the coexistence be-tween operators by evaluating the impact of critical parameters. Futureresearches may propose a coordination scheme between operators whichcan reduce the inter-operator interference. Furthermore, a system modelwhich is more compatible with Orthogonal Frequency Division Multiplex-ing (OFDM) systems can be considered. Since we did not include the shadowfading in our study, it can also be taking into consideration for future works.

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