analysis of 5g mobile broadband solutions in rural and ...1165918/fulltext01.pdf · analysis of 5g...

IN DEGREE PROJECT ELECTRICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2017

Analysis of 5G Mobile Broadband Solutions in Rural and Remote Areas

A Case Study of Banten, Indonesia

ANNISA SARAH

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INFORMATION AND COMMUNICATION TECHNOLOGY

KTH Royal Institute of Technology

Master Thesis

Analysis of 5G Mobile Broadband Solutions in

Rural and Remote Areas: A Case Study of

Banten, Indonesia

Annisa Sarah

Supervisor: Ki Won SungExaminer : Anders Västberg

August 25, 2017

i

Abstract

Providing a broadband access anytime and anywhere is one of the visionsof the future 5G network. However, deploying a reliable network connection inremote/rural areas has been a challenging task because of its wide area thatneeded to be covered and a low density of user compared to urban area. Dier-ent geography and trac condition may need dierent system solution. In thisthesis, we analyze several solutions to providing a broadband access networkin practical remote and rural area in Banten, Indonesia: Leuwidamar (remote)and Panimbang(rural). Two approaches are discussed, rst one is fullling fu-turistic trac demand by having LTE System, and the second one is by having5G System. We included three key technology components in a 5G network:occupying wide bandwidth in high frequency, applying UE-Specic Beamform-ing, and implementing Carrier Aggregation (CA) scheme. We also account arain attenuation when deploying a network in high operating frequency, sinceIndonesia has a high rain rate thus it is important to be considered. We com-pared ve cases of solution: Case 1 is Single Carrier (SC) LTE 1.8 GHz system;Case 2 is Carrier Aggregation (CA) LTE 1.8 GHz + 2.6 GHz; Case 3 is SC 5G 15GHz; Case 4 is SC 5G 28 GHz; Case 5 is CA LTE 1.8 GHz + 5G 15 GHz. Basedon the evaluation, in Leuwidamar scenario, Case 5 gives us the least number ofBS needed in order to meet the futuristic requirement with only 1.6× densica-tion from the current network. In Panimbang, the least number of BS neededis oered by two cases, Case 3 and Case 5 without any additional BS needed(1× densication). However, the solution with the lowest energy consumptionfor both area is Case 3. This is due to the fact that the carrier aggregation sce-nario needs additional power to generate the second system. Furthermore, if weintroduce cell DTX ability in the 5G network, the Case 3 can give us impressiveamount of energy saving, with 97% saving for Leuwidamar and 94% saving forPanimbang, compared to Case 1 solution without any DTX Capability.

Keywords: 5G, beamforming, case study, rural area, system level simulation,rural communication, carrier aggregation

ii

Abstrakt

Att tillhandahålla bredbandsanslutning när som helst och var som helst är enav visionerna för det framtida 5G-nätverket. Att använda en tillförlitlig nätverk-sanslutning i avlägsna- eller landsbygdsområden har dock varit en utmanandeuppgift på grund av det breda området som måste täckas och den låga täthetenav användare jämfört med stadsområden. Olika geograska förhållanden ochtrakförhållanden kan behöva olika systemlösningar. I denna avhandling anal-yserar vi era lösningar för att tillhandahåller ett bredbandsnätverk i verkligtavlägset eller landsbygdsområde i Banten, Indonesien: Leuwidamar (avlägset)och Panimbang (landsbygd). Två strategier diskuteras, den första uppfyllerframtida trakbehov genom att ha LTE-system och den andra är genom att ha5G System. Vi inkluderade tre viktiga teknikkomponenter i 5G-nätverk: bredbandbredd och hög frekvens, tillämpar UE-specik strålformning och imple-mentering av carrier aggregation (CA). Vi redovisar också en dämpning av regnnär nätverket används i hög bärvågsfrekvens, eftersom Indonesien har en högregnhastighet och det är viktigt att överväga. Vi jämförde fem fall av lösning:Fall 1 är Single Carrier (SC) eller Enkelbärare LTE 1.8 GHz system; Fall 2 ärbärareaggregation (CA) LTE 1,8 GHz + 2.6 GHz; Fall 3 är SC 5G 15 GHz;Fall 4 är SC 5G 28 GHz; Fall 5 är CA LTE 1.8 GHz + 5G 15 GHz. Baseratpå utvärderingen, i Leuwidamar-scenariot,ger Fall 5 oss det minsta antalet BSsom behövs för att möta det futuristiska kravet med endast 1.6 gångers förtät-ning från nuvarande nätverk. I Panimbang erbjuds det minsta antalet BS somkrävs i två fall, fall 3 och fall 5 utan ytterligare BS behövs (1 gångers förtät-ning). Lösningen med den lägsta energiförbrukningen för båda områdena ärfall 3. Detta beror på att bäraraggregations scenariot behöver ytterligare eektför att generera det andra systemet. Om vi introducerar cell DTX-funktionen i5G-nätverket kan Fall 3 ge oss en imponerande energibesparing, med 97% min-skning för Leuwidamar och 94% för Panimbang jämfört med Fall 1-lösning utanDTX-funktion.

Nyckelord: 5G, strålformning, fallstudie, landsbygdsområde, systemnivå simu-lering, landsbygdskommunikation, bärare aggregering

iii

Acknowledgements

Firstly, I would like to thank my supervisor, Ki Won Sung for his kindnessand patience when advising me throughout this thesis project. Thanks for theassistance and the encouragement for keeping me on track and making my thesisbetter. I have learned a lot from his knowledge and guidance.

Secondly, I also would like to thank Anders Västberg who has examining thisthesis project, to all the people at Radio Systems Lab for accepting me andletting me carry out my thesis project, and to my colleague in Indonesia, Bimo,for providing a realistic data to be analyzed in my thesis.

Next, I want to express my gratitude to my family in Indonesia, especially mymom, thanks to not clipping my wings and let me y away to chase my dream.And lastly, thanks to Indonesia Endowment Fund for Education (LPDP) toaward me a scholarship, and provide all the support that I need during mymaster study.

List of Acronyms

ADSL Asymmetric Digital Subscriber Line

ARPU Average Revenue Per User

CA Carrier Aggregation

CAPEX Capital Expenditures

CC Component Carrier

CDF Cumulative Distribution Function

BS Base Station

DL Downlink

DTX Discontinuous Transmission

HPBW Half Power Beamwidth

ICT Information and Communication Technology

ISD Inter-Site Distance

LTE Long-Term Evolution

mmWave Milimeter-Wave

OPEX Operational Expenditures

PA Power Amplier

PC Personal Computer

RB Resource Blocks

SC Single Carrier

SINR Signal-to-Interference-and-Noise Ratio

SNR Signal-to-Noise-Ratio

iv

TV Television

UE User Equipment

List of Figures

1.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2.1 UE Specic Beamforming Illustration . . . . . . . . . . . . . . . 92.2 Illustration of Carrier-Aggregation Scenario in LTE-Advanced . 10

3.1 Evaluation Framework . . . . . . . . . . . . . . . . . . . . . . . 123.2 Network Dimensioning and Evaluation Flowchart . . . . . . . . 143.3 Daily Data Trac Variation Prole . . . . . . . . . . . . . . . . . 17

4.1 Remote with hilly terrain . . . . . . . . . . . . . . . . . . . . . . 194.2 Screen-shot of BS location Monitoring using NetMon . . . . . . . 204.3 Open Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.4 Leuwidamar Layout in System-level Simulator . . . . . . . . . . 244.5 Panimbang Layout in System-level Simulator . . . . . . . . . . . 254.6 Antenna Pattern of 60 HPBW . . . . . . . . . . . . . . . . . . 26

6.1 Impact of Operating Frequency . . . . . . . . . . . . . . . . . . . 316.2 Impact of Rain . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336.3 Impact of Bandwidth . . . . . . . . . . . . . . . . . . . . . . . . . 356.4 Impact of UE-Specic Beamforming . . . . . . . . . . . . . . . . 366.5 Impact of Carrier Aggregation . . . . . . . . . . . . . . . . . . . 386.6 Impact of Threshold in Carrier Aggregation Scheme . . . . . . . 406.7 Proposed Solution of Case 1 Network Layout in Leuwidamar

Area with 36 BS . . . . . . . . . . . . . . . . . . . . . . . . . . . 416.8 Case 1 Solution Performance of Leuwidamar Area . . . . . . . . 426.9 User Performance for 5G Solution in Leuwidamar . . . . . . . . . 446.10 Case 5 Solution Performance of Leuwidamar Area . . . . . . . . 466.11 Proposed Solution of Case 1 Network Layout in Panimbang Area

with 81 BS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476.12 User Performance with and without rain attenuation for 5G

Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496.13 Case 5 Solution Performance of Panimbang Area . . . . . . . . . 516.14 Three-sector Hexagonal Cell with Uniforlmy Distributed Users . 526.15 Impact of ISD in Simplied Model . . . . . . . . . . . . . . . . . 52

List of Figures v

6.16 Simplied and Realistic Model Comparison of Leuwidamar Area 546.17 Simplied and Realistic Model Comparison of Panimbang Area . 556.18 Energy Consumption Comparison . . . . . . . . . . . . . . . . . . 57

List of Tables

4.1 Trac Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5.1 Trac Requirement . . . . . . . . . . . . . . . . . . . . . . . . . 275.2 Parameter Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.1 Summary of BS Number Needed . . . . . . . . . . . . . . . . . . 56

Contents

1 Introduction vii1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Previous works and Research Gaps . . . . . . . . . . . . . . . . . 21.3 Case Study: Banten Area . . . . . . . . . . . . . . . . . . . . . . 3

1.3.1 Motivation of Case Study . . . . . . . . . . . . . . . . . . 31.3.2 Banten Province, Indonesia . . . . . . . . . . . . . . . . . 3

1.4 Benet, Ethics and Sustainability . . . . . . . . . . . . . . . . . . 41.5 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.6 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . 51.7 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.8 Delimitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.9 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Overview of 5G Technology 82.1 Milimeter-wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 UE Specic Beamforming . . . . . . . . . . . . . . . . . . . . . . 82.3 Carrier Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . 92.4 Ultra-Lean Design . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Performance Evaluation Method 123.1 Trac Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . 123.2 Network Performance Evaluation . . . . . . . . . . . . . . . . . . 133.3 Energy Performance Evaluation . . . . . . . . . . . . . . . . . . . 15

4 Network Layout and System Model 184.1 Existing Infrastructure and Trac Modeling . . . . . . . . . . . . 18

4.1.1 Current Condition . . . . . . . . . . . . . . . . . . . . . . 184.1.2 Trac Condition . . . . . . . . . . . . . . . . . . . . . . . 214.1.3 Trac Demand Calculation . . . . . . . . . . . . . . . . . 23

4.2 System-level Environment Model . . . . . . . . . . . . . . . . . . 244.2.1 Remote Area: Leuwidamar Regency . . . . . . . . . . . . 244.2.2 Rural Area: Panimbang Regency . . . . . . . . . . . . . . 244.2.3 Propagation Model . . . . . . . . . . . . . . . . . . . . . . 254.2.4 Antenna Design . . . . . . . . . . . . . . . . . . . . . . . . 26

Contents vi

4.2.5 Rain Attenuation Model . . . . . . . . . . . . . . . . . . . 26

5 Simulations 275.1 Trac Requirement . . . . . . . . . . . . . . . . . . . . . . . . . 275.2 Simulation Scenarios and Parameter Setup . . . . . . . . . . . . . 27

5.2.1 Case 1: Single Carrier (SC) LTE 1.8 GHz . . . . . . . . . 275.2.2 Case 2: Carrier Aggregation (CA) LTE 1.8 GHz + 2.6

GHz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275.2.3 Case 3: SC 5G 15 GHz . . . . . . . . . . . . . . . . . . . 285.2.4 Case 4: SC 5G 28 GHz . . . . . . . . . . . . . . . . . . . 285.2.5 Case 5: CA LTE 1.8 GHz + 5G 15 GHz . . . . . . . . . . 28

6 Result and Discussion 306.1 Impact of Radio Environment . . . . . . . . . . . . . . . . . . . . 30

6.1.1 Impact of Operating Frequency . . . . . . . . . . . . . . . 306.1.2 Impact of Rain Attenuation . . . . . . . . . . . . . . . . . 30

6.2 How Prospective 5G Technology Aect User Performance . . . . 326.2.1 Impact of Wide Bandwidth Availability . . . . . . . . . . 326.2.2 Impact of UE-Specic Beamforming . . . . . . . . . . . . 346.2.3 Impact of Carrier Aggregation . . . . . . . . . . . . . . . 37

6.3 Solution for Remote Area: Leuwidamar Regency . . . . . . . . . 396.3.1 LTE Solutions . . . . . . . . . . . . . . . . . . . . . . . . 396.3.2 5G Solutions . . . . . . . . . . . . . . . . . . . . . . . . . 43

6.4 Solution for Rural Area: Panimbang Regency . . . . . . . . . . . 456.4.1 LTE Solutions . . . . . . . . . . . . . . . . . . . . . . . . 456.4.2 5G Solutions . . . . . . . . . . . . . . . . . . . . . . . . . 48

6.5 Comparison of Case Study and Simplied Model . . . . . . . . . 506.6 Energy Performance . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.6.1 Energy Consumption in Leuwidamar Area . . . . . . . . . 566.6.2 Energy Consumption in Panimbang Area . . . . . . . . . 58

7 Conclusion and Future Work 59

Bibliography 66

CHAPTER 1

Introduction

Chapter 1. Introduction 1

The fth-generation network (5G) is expected to be able to serve super hightrac demand with a wide variety of use cases. 5G is not only the evolution of4G, it is a new unique concept of telecommunication services [1]. Several orga-nizations: Standard bodies (e.g. ITU, 3GPP), Regional initiatives (e.g. FP-7,5G-PPP) and Industry Alliance (e.g. NGMN, SCF); made a vision of 5G net-work. One of the organizations, NGMN, envisioned 5G as:

"5G is an end-to-end ecosystem to enable a fully mobile and connectedsociety. it empowers value creation towards customers and partners,through existing and emerging use cases, delivered with consistent ex-perience, and enabled by sustainable business models." [2].

Therefore, many wide and deep types of research are needed to bring the 5Gnetwork into reality: i.e. from developing telecommunication theories, deningbusiness strategy for the cases, to preparing the 5G standards. This thesis aimsto do a further analysis of prospective 5G network solutions for a practical ruralarea.

1.1 Background

As mentioned in [2], 5G will provide a full connectivity with a high reliabil-ity anytime, anywhere. Most studies are focused on deploying 5G in a denseurban area, the place where the trac demand will be terrically high. How-ever, 5G will be needed not only in a dense urban area but a rural area as well.Rural area connectivity has been an issue in telecommunication eld for a longtime. To deploy a network in such area, there are several important challengesthat needed to be considered. The rural area generally considered as a lowAverage Revenue Per User (ARPU) area. The low-income zones might be athreat for a network provider since the high investments are needed to deploynetwork infrastructure in a rural area: high cost for building tower, electricityservice, etc. Thus most telecom players often defer from such case to keep theireconomic sustainability. [3]. Only few telecommunication industry players aregetting involved in providing the services in a rural area because the revenuethat generated in the area mostly not outweigh the cost investment for networkinfrastructure. However, telecommunication services are important to supportpeople needs. For example, the internet will allow people to access useful infor-mation to educate themselves, enable remote health-care services, and ease theworking process to be more ecient (e.g. smart-farming). In addition, UnitedNations stated that internet access is one of human rights [4]. Therefore, pro-viding a broadband access while maintaining the eciency is important in orderto achieve an economic sustainability for industry.


1.2 Previous works and Research Gaps

New attempts were introduced to provide a better coverage and quality oftelecommunication services in rural area. Giant companies like Google andFacebook launched the unique techniques to bring the base station into the air,by using a balloon or Unmanned Aerial Vehicle [5] [6]. However the sustainabil-ity and scalability of such systems still under investigation. The balloon fromLoon Project can stay in the air up to 100 days. However, the equipment mightfail within those 100 days and it is hard to x and maintain the hardware. TheFacebook's drone might stay longer (5 years) and have better coverage thanone balloon from Loon Project. However, both are using ying objects whichmight go down unexpectedly when the hardware failures occur, and it is a hugeconcern for the safety of people as well [7].

Another possible approach to deploying future network in such area is byanalyzing the Capital Expenditure (CAPEX) and Operational Expenditure(OPEX) of the infrastructure, for both existing and future upgrade, and seewhich sector that can we save more. CAPEX might be lower by using MIMOBeamforming, and OPEXmight be lower by exploiting renewable energy sources,decreased power consumption and use virtual network elements [8]. Such solu-tions indirectly enable the ecient broadband access for future network. Mas-sive MIMO beamforming technology allows signicant increase of energy per-formance, e.g. 5 dB additional link budget from beamforming can be used todecrease 50% network area power consumption for achieving 10 Mbps cell edgeuser throughput target[9]. The main reason is that when we implementing themassive MIMO beamforming techniques, it can enable us to deploy a networkwith larger inter-site distance (ISD) which can decrease the total number ofsites that needed, thus saving more energy. Moreover, the increase of energyperformance is also supported by the ultra-lean design for implementing thediscontinuous transmission (DTX) technique. DTX allows base station (BS) to"switch mode" i.e to sleep mode, which the state depends on the BS utility.The impact of utilizing massive beamforming and an ultra-lean system designin the future radio access technology (RAT) for 5G has been studied. The 5GRAT denoted as 5G-NX in which implementing massive beamforming and CellDTX can save more than 50% energy consumption while giving 10 times morecapacity, compared to LTE services. [10]

Although most research focus to study 5G implementation in urban anddense urban areas, there are some studies of 5G scenarios in the rural area. In[11], the author studied the UE-Specic Beamforming, DTX capability. Theyalso investigate the impact of antenna tilt, a size of antenna arrays, ISD, andfrequency. The result showed that to be able to use a UE-Specic Beamform-ing, one need utilizing high frequency (above 6 GHz) since it is related to theantenna size that will be installed (higher frequency leads to a smaller antenna).Another benet of using high frequency is also the ability to provide wide band-width. The high frequency is good to provide the capacity, but not a coverage


since it will be highly attenuated through the air.

Since the cost also will be the main consideration to deploy telecom services,studies about economic viability on the energy-saving solutions also been taken[12]. The author stated that the hardware upgrade can save up to 60% percentof energy consumption, especially when a high trac demand is required. Theimpact of changing unit energy cost also shows that the higher it is, the morecost eective it will be, which caused the increase of energy saving cost. Itcan be concluded that the energy-ecient network might lead to a cost-ecientnetwork as well.

1.3 Case Study: Banten Area

Literature studies have been taken to analyze the current situation in Banten.We intend to nd the possibility of implementing the future 5G network. Theavailability of the corresponding infrastructure also will be discussed in thissection.

1.3.1 Motivation of Case Study

Current studies were mainly under generic conditions. In fact, there is a pos-sibility that a dierent condition would need dierent solution. Consequently,we need to dene a method to analyze the impact of deploying proposed tech-nology into a practical area, and what type of parameter that needs to beconsidered. Thus analyzing a specic area as a model for implementing future5G network is needed to check whether the proposed techniques are suitable tothe area or not, and which solutions will give the best performance among allproposed solutions.

1.3.2 Banten Province, Indonesia

Indonesia is the fourth most populated country in the world [13]. With morethan 200 million people, only 84% have a mobile phone, and 36% have internetaccess [14]. As such a big country and population, Indonesia still consideredas a developing country. The technology gap in Indonesia is also very high.Most technologies are centralized in big cities or urban areas, but not in therural area. Indonesia has 34 provinces, and Banten is one of the provinces thatleft behind in terms of development, specically in the south part of Banten.This province is located next to the capital city, Jakarta, yet the condition isincompatible. Jakarta is well known as the most populated city in Indonesiawith the density of more than 14 thousand people per square kilometer [15].The over-population condition leads to several problems: trac jam, ooding,pollution, etc. Therefore, developing the surrounding areas (including Banten)might help to solve the migrant labor problems, by providing proper accessfor living in the area. Furthermore, Banten can be considered as a miniature


of Indonesia: it has an urban area, industrial area (e.g. factories, mining),hilly topography area, low coastline land, etc. Last, since Banten has Jakartathe capital city as a neighboring province, Banten has an advantage of well-established infrastructure nearby, then it will be easier to expand the networkto reach Banten. Thus, taking Banten as a representative for the initial studyis reasonable.

Possibility Analysis

Based in [14], there are several household access to ICT devices in Indonesia,e.g. Mobile Phone, Computer, Fixed Phone, Television, Radio. However, mostwell-established communication infrastructures are centralized in the urban areaonly. The challenging geographic and low revenue might be the barriers toproviding rural broadband access. Fixed-line cable access is very limited inBanten area, with a less than 5 % of residents have xed phone cable. Aside fromlow availability, the capacity of Asymmetric Digital Subscriber Line (ADSL)technology is limited. Fiber optics is available from the neighboring area butit might be expensive to deploy ber optics to reach each household. Otherpossibilities to provide communication access is by using a wireless access. Thereis one proposal of utilizing terrestrial television broadcasting to give a broadbandaccess in rural area [16], however, most terrestrial Television (TV) broadcastingsignals are not reaching South Banten area [17]. The villagers tend to usesatellite antenna to receive a TV signal. The satellite signals, however, suerfrom the latency and have limited capacity with the maximum download speedas 20 Mbps only [18]. Another possible solution to deliver 5G services is byutilizing current mobile communication infrastructure. In South Banten, thereis a well-established telecom tower and infrastructure provided by one of thebiggest network providers in Indonesia. For this reason, it is interesting to doa quantitative study of upgrading 4G LTE network to prospective 5G networkand see how are the solution's network and energy performance.

1.4 Benet, Ethics and Sustainability

The benet of studying 5G deployment in a practical rural area could beperceived in many aspects. In social and economic aspects, the energy-ecientbroadband access will enable people in a rural area to aord telecommunicationservices while the industry might able to keep their business to be sustained.Besides, the result of this thesis might support the authorities in the particulararea to prepare the regulations for a future 5G network.

Furthermore, in terms of environmental ethics, the energy-ecient solutionwill help to save more energy consumption which can help the environmentsustainability. One of the world's goals is to reducing carbon footprints, andanalyzing an energy-ecient network can be one of the contributors to make ithappens.


1.5 Contribution

There are three main contributions of this thesis.

1. Evaluate important data needed to do a case study: we summarize whichdata that will be needed to do a case study;

2. Propose a case study method: In this thesis, we introduce a methodologyto study prospective 5G network deployment for a practical rural area;

3. Propose a simplied system-level simulator to do a case study: We intro-duce a system-level simulator which considers a realistic data (e.g. realisticnetwork layout, futuristic trac forecasting, etc) as an input to analyze5G technology impact in a practical rural area.

1.6 Problem Statement

Based on the problems and possibilities of the interested area, the mainproblem statement of this thesis is:

"To analyze network and energy performance of potential solutionsfor deploying a broadband access network that can be applied in thepractical rural/remote areas, in which able to providing services envi-sioned by 5G."

Referred to the main goal, the specic research questions are:

1. What data that need to be considered to do a case study?

2. How will the futuristic trac demand be?

3. Is it important to do a case study? Is there any dierence if we consideringgeneral hexagonal cells rather than a realistic network?

4. How will LTE fulll futuristic trac demand? How much does it cost interms of energy consumption?

5. How will 5G fulll futuristic trac demand? How much does it cost interms of energy consumption?

1.7 Methodology

The main methodology in this thesis is divided into four parts, as depictedin Figure 1.1. Firstly we need to do the possibility analysis, in order to decidewhich solution that might be suitable to deploy a future 5G network in the in-terested area. Secondly, we need to gather important data that represent a real


situation to establish the system-level simulator. Next, the data collection. Aquantitative experimental research [19] on system-level simulation will be taken.We will conduct a simulation several times for dierent scenarios. Lastly, is thedata analysis. We will take the computational mathematics evaluation from thesimulation which will produce the statistics data for the result. The comparisonand conclusion will be carried out by the statistic analysis. All the experimentsis carried out by using a simulator written in MATLAB.

Figure 1.1 Methodology

The motivation of using this methodology is to do a simple case study inlogical order. Firstly we will have a good understanding of the current situ-ation by doing a possibility analysis and gather important data that need tobe evaluated. The quantitative simulation takes part to motivate the thesis insimple, straightforward and less time-consuming way compared to a real lifeexperiment.

1.8 Delimitation

A simplied map is considered for the simulation, i.e. the map which hasbeen generated is not taking a land-use/clutter and topography into account.Yet we apply a uniformly distributed shadow fading with a suggested deviationvalue. The user equipment is assumed to have the same specication withcurrent UE for LTE network. An alternative solution for the prospective 5Gnetwork outside UE-Specic Beamforming, Carrier Aggregation, and Cell-DTXis not investigated in this thesis.

1.9 Outline

This thesis structured as follow: In Chapter 2 we briey discussed the back-ground of technical components which included in 5G networks; In Chapter


3, we introduce the evaluation method to forecast a futuristic trac demand,evaluate the system network performance and energy performance; In Chapter4, we describe the network layout and system-level environment model that im-plemented for the simulation; In Chapter 5 we provided the trac requirement,simulation cases, and parameter setting for the simulation setup; In Chapter 6we present the simulation result; Lastly in Chapter 7 we summarized the thesisand discuss the future work.

CHAPTER 2

Overview of 5G Technology

Chapter 2. Overview of 5G Technology 8

The prospective 5G technology is supported by several cutting-edge mech-anisms that enable 5G to serve high trac demand with a good performance,highly reliable and energy-ecient. This thesis evaluates three key techniquesthat would likely to help 5G to operate: Millimeter-wave, UE-Specic Beam-forming, Carrier-aggregation; and one additional mechanism to enable energy-ecient solution: Cell DTX.

2.1 Milimeter-wave

The main barrier to providing high capacity and high bitrate for a vast traf-c demand is the lack of bandwidth availability for the frequency below 6 GHz.Serving user's demand in a high frequency is one of the key mechanism to givehigh capacity since there is wide bandwidth that can be allocated for broad-band cellular communication. Therefore the future 5G services will utilize highfrequency (above 6 GHz) to operate the system. The prospective frequenciesare 28 GHz, 38 GHz, 60 GHz and 73 GHz with the wavelength 10 mm, 7 mm,5 and 4 mm respectively, hence mmWave [20]. Other than mmWave, there arealso studies to evaluate the feasibility and performance of deploying broadbandcommunication in microwave system, on 15 GHz link [21]

A key challenge of an operating system in high frequency is the high prop-agation loss and signicant attenuation from a blockage (e.g. trees, building),or rain attenuation. Thus, research shows that the mmWave is probably moresuitable for small cells such as microcells or picocells since the distance betweentransmitter and receiver will not be so far.

However, despite the main advantage of having a wider bandwidth, the highfrequency enable us to pack more antennas in the same size because of thesmaller wavelength. The array antennas increase the antenna gain and con-sequently mitigate the high propagation loss. More detail discussion will becarried in Section 2.2.

2.2 UE Specic Beamforming

One of the prospective technology that might enable 5G services is the abilityto have UE-specic beamforming [11]. The prospective operating frequency for5G technology is beyond 6 GHz: 28 GHz, 38 GHz, 73 GHz which give a verywide bandwidth i.e. a high capacity to be used [22]. Utilizing high frequencyleads to worse propagation path loss and other penetration loss (building, trees,rain), yet it gives the opportunity to equip more antenna elements in small size.A large number of antenna elements enable the antenna beam to be narrower.The narrow beam could be steered to a specic user in order to maximize thesignal strength and at the same time decrease interference from others. Theantenna gain from a UE Specic Beamforming technology will be considered in


a simple model as the following:

gMB =ASphere

AMainBeam= 4πr2

4

πr2sinθsinϕ=

16

sinθsinϕ(2.1)

As the elevation half power beam width (sinθ) and azimuth half power beamwidth (sinϕ) getting narrower, the antenna gain is increasing. Furthermore,a narrow beamwidth also contributes to a less interference thus increasing theSignal to Interference Ratio (SINR). An illustration of UE Specic Beamformingconcept is shown in Figure 2.1

Figure 2.1 UE Specic Beamforming Illustration

2.3 Carrier Aggregation

Carrier aggregation is one of mechanism to provide a wide bandwidth, andenable to increase user and system throughput [23]. in the current 3GPP speci-cation, it was written that the component carrier (CC) can combine ve com-ponent carriers in maximum, i.e. the maximum aggregated bandwidth for thesystem is 100 MHz. There are three types of CA dened in LTE-Advance spec-ications, as illustrated in Figure 2.2. The most simple scheme among thosethree would be the Intra-band Contiguous, since the CC operating in the samefrequency band. However, such scenario is not so common. Most telecom op-erators have spectrum licenses in a fragmented band, thus it is hard to apply acontiguous carrier aggregation scheme in realistic. The next scenario is to aggre-gate CC in a non-contiguous scheme, whether it is Intra-Band Non-Contiguousor Inter-Band Non-Contiguous. In an Intra-Band scenario, the allocation is pos-sible to be overlaid and providing almost identical coverage, while in Inter-Bandthe higher frequency will have smaller coverage due to a higher propagation loss[24].

Carrier aggregation is believed to be one of the key helpers for future 5Gtechnology in providing wide bandwidth and capacity [25]. Furthermore, future


Figure 2.2 Illustration of Carrier-Aggregation Scenario in LTE-Advanced

technology is expected to be a user-friendly and backward compatible to avail-able system i.e. working properly in a heterogeneous network. Thus a furtherstudy of carrier aggregation deployment in a prospective 5G system is important.

2.4 Ultra-Lean Design

Energy consumption in telecommunication research eld becomes an im-portant aspect to be evaluated. Although telecommunication industry onlycontributed to 0.5% of total global carbon footprint emissions from human ac-tivities, it is important to support global planning to reduce emissions in orderto meet the world's target by limit the temperature increase to a maximum of2o C. Moreover, the future telecommunication technology is expected to serve1000 times of current trac. If we do not have any improvement in terms ofenergy performance, the energy consumption will increase signicantly. Thus astudy to reduce energy consumption while still maintaining the ability to servefuturistic trac demand is important. [26]

There are several studies to evaluating the power consumption of telecommu-nication equipment e.g. EARTH project [27]. The EARTH project introducesa power consumption models for dierent types of BS: Macro, Micro, Pico cell.The study stated that the consumed power in a BS is highly related to theload condition, i.e. the higher the load, the higher the energy consumption of aBS. The main reason is that the Power Amplier (PA) is highly dependent onthe load prole, and the PA itself consumes 57% of total power consumption ofMacro BS, which is the main type of BS in this thesis study.

Despite the fact that PA consumes much energy during the transmissionmode, the BS still consume high power during the 'idle-mode', i.e. when nodata transmission is needed. The current term of BS is always-on, while infuture ultra-lean design the term of BS will be shifted to always-available, i.e.no need to be always active all the time including during the idle mode. Theprinciple of ultra-lean design is to minimize any transmissions which not relatedto the data delivery: such as synchronization signals, control channel, etc [28].Cell discontinuous transmission (Cell DTX) is one of technology that can beimplemented in the ultra-lean design system. It allows the base station to changestate into 'sleep mode' when there is no trac data that need to be submitted.


Since DTX is not completely turning o the BS, there is a term called CellDTX Capacity which represents a capability of cell DTX to deactivated somecomponents. In LTE System, there is plenty of mandatory signals that neededto be transmitted, and the maximum 'sleep' duration is 0.2 ms, hence preventthe cell from reaching a deep-sleep-mode. However, a study in [29] shows thatfor the future 5G network, we could achieve sleep duration up to 99.6 ms, hencehaving a deeper sleep compared to LTE system.

CHAPTER 3

Performance Evaluation

Method

Chapter 3. Performance Evaluation Method 12

Based on previously discussed technologies, we intend to study the impactof designing 5G using wide bandwidth, UE-Specic Beamforming, and CarrierAggregation. We also investigate the LTE network to be a baseline comparison.When designing a network, many research considers simple hexagonal cells ona greeneld scenario. In this thesis, we consider a practical area which has awell-established mobile communication network. With such browneld area, weable to utilize the existing equipment which already installed (such as tower,base station shelter, etc).

The evaluation method practically divided into three steps, as shown inFigure. 3.1. Firstly we need to forecast the trac to dene a futuristic require-ment; Secondly a Network Performance Evaluation is needed to evaluate a userdownlink throughput and base station utilization; Lastly, the Energy Perfor-mance Evaluation will be investigated to compare the energy consumption forthe proposed network solutions.

Figure 3.1 Evaluation Framework

3.1 Trac Forecasting

Several companies such as Ericsson and Huawei forecast a trac demandwhich needed to be served in the near future [30] [31]. Generally, such companies


provide a trac forecasting in global-wide or country perspective, and lack ofdetail for a futuristic trac demand in a dierent type of area e.g. urban,suburban, rural, remote area. In [27] the authors introduce a futuristic tracdemand in several types of areas in Europe. The area types including superdense urban, dense urban, suburban, rural and wilderness. However, the modelis suitable to predict a trac demand in Europe only, while the other part ofthe world would have dierent trac demand because of many factors: dierentpopulation density, economic activity, government policy, geographic condition,etc. Thus a trac forecasting that has a focus on a particular area is needed.The forecasting should account a recorded trac data, population growth, andspatial/district planning from the government.

3.2 Network Performance Evaluation

To evaluate a network performance, rstly we need to dene a radio envi-ronment model for a system-level simulator. A suitable propagation model waschosen to t a rural area. Secondly, the longitude and latitude data for existingbase stations which located in the particular area is needed to mimic a practicalcondition. Lastly, we distribute the user location according to a current realisticcondition.

Feasible Load Model

Downlink user throughput performance is the main parameter which inves-tigated in this thesis. User throughput from a Base Station (BS) may be variedwithin a certain amount of time. It is caused by the dierent utilization ofcell resources that allocated to the user. BS tend to oer high user throughputwhen the BS have low trac demand and oer low user throughput when theBS need to serve high trac demand. The feasible load model analyzes theload or cell resource allocation as the fraction of time-frequency resources in anOFDM system, which scheduled for data transmission in a given cell [29]. Thepurpose of this feasible load concept is to address the relation of BS resourceutilization and its ability in serving trac. The feasible load ηk of BS k for Nkusers in the time T described mathematically as:

ηk =

∑Nk

i=1 Ωi/riT

(3.1)

where Ωi is the trac demand of user i, ri is the data rate of user which can beexpressed as:

ri = WRB min[log2

(1 +

gMBgikPk∑Mj 6=k ηjgSBgijPj +No

), vmax

](3.2)

which dependent on the antenna gain gMB , power transmits P , and bandwidthallocation WRB for the system. The gSB represents a sidelobe antenna gain, Nois the noise power, and vmax is the maximum spectrum eciency in practical.


Network Dimensioning Method

In a simplied hexagonal-cell network, a densication of a network meansto decrease the ISD. Thus we automatically decrease each BS's coverage andincrease the number of BS needed over the area. In practical, densication doesnot mean by simply decreasing ISD. We can not move every deployed networkby several hundred meters just to achieve a uniformly smaller ISD between eachBS. Generally, network planning team uses a manual approach of deciding lo-cations of BS.

Although there are several studies to investigate an optimal method of lo-cating a base station in the practical area (e.g. [32]), it requires more eort andtools. In this thesis we consider a simple algorithm to identify a location fornew BS when we need to upgrade the network, as can be seen in Figure 3.2.

Figure 3.2 Network Dimensioning and Evaluation Flowchart


Firstly we start by locating N BS in our system-level simulator. For exam-ple, in Leuwidamar area, there are 3 BS that already deployed, hence N = 3.Assume we distribute our subscribers in the area, and we evaluate the radiopropagation between users and the base stations. Note that we set "50 Mbpseverywhere" as our target, including the cell-edge users, thus we need to ensurethat our network is able to serve such requirement.

In this evaluation, we intend to maximize the bandwidth available so thenwe always consider reuse-1 network. The throughput performance is calculatedbased on user instantaneous bit rate and processor sharing estimation from allrelated users. The processor sharing estimation accounts the interference fromneighboring cells which aected by cells activity: the more active users in neigh-boring cells, the more interference that will be experienced. Furthermore, theaverage user monthly subscription aects the utilization rate of each base sta-tion. Hence, the higher user monthly subscription leads to higher BS utility andmakes the overall user throughput lower. The 5th percentile of user throughputis considered to be the benchmark, and the 50 Mbps downlink throughput mustbe met at the 5th percentile of throughput performance.

If the network does not fulll the requirement, then we need to identify 10%of users with lowest received power to decide a candidate location for new BS.We believe that if we put a new BS in the area, which surrounded by users witha poor signal power, it will increase the signal quality hence increase overallnetwork performance. After deciding a location for a new BS, we deploy the BSand evaluate the radio performance again. This process repeats until we havefullled the requirement.

When we deploy the future 5G network, there is a possibility to utilize avery wide bandwidth (e.g. 100 MHz, 500 MHz). However, bandwidth licenseswould be costly for the operators, thus adding more bandwidth is not recom-mended. Instead, we maximize the possibility of technology specication (suchas beamforming ability). Thus the next process is to adjust a minimum requiredbandwidth in which still able to fullling our target requirement. These pro-cesses give us a downlink user throughput prole and BS utilization that willbe needed to analyze the energy performance of a network.

3.3 Energy Performance Evaluation

An energy performance evaluation from [33] is adopted in this thesis. InDTX system, energy consumption for a base station for current LTE can bedescribed as Equation 3.3, where NTRX denotes the number of a transceiver andPtx is a transmit power. The ∆p represents a consumption of power amplierneeded to mitigate a feeder loss. The Po represent the power consumption of theactive cooling and the signal processing equipment. Lastly, δ represent the cellDTX capacity which enable to save energy during the idle state of BS where


0 < δ < 1. When we have δ = 1 it is equal to a system without cell DTXcapacity. The lower the value, the more components are able to be deactivated.As discussed in [33], the minimum value for δ in LTE system is δ = 0.84. Thereason is that the primary transmissions required for the LTE system is veryshort with the max 0.2 ms, and preventing LTE cells to have a deep sleep.

PLTEBS = NTRX ×

∆p Ptx + Po if PLTEtx > 0

Po if PLTEtx = 0 (without cell DTX )

δPo if PLTEtx = 0 (with cell DTX )

(3.3)

The prospective 5G BS consumption is described in Eqation 3.4 which con-sidering the additional impact of power amplier eciency ε, and a number ofRF Chains N . A study stated that the power amplier eciency for future 5Gequipment could be 25%. The NTRX and NS is the number of transceivers, ∆p

is the portion of transmitting power dependent power consumption,Ptx is thetransmit power, PB is the xed power consumption in a base station for 5G.We assume the minimum δ value for 5G system is δ = 0.29. The reason is thatwe could achieve longer DTX period up to 99.6 ms, compared to LTE with 0.2ms achievable period. [33]

P 5GBS = Ns ×

P s

tx

ε +NPc + PB if P 5Gtx

s> 0

PB if P 5Gtx = 0 (without cell DTX )

δPB if P 5Gtx = 0 (with cell DTX )

(3.4)

Daily Average Area Power Consumption

A trac condition in dierent periods might be varying. As discussed in[27] which studied under Europe condition, the trac demand is low during theearly morning and might be high during the peak hour on the night. In thisthesis, we adapted a daily trac prole value as discussed in the EARTH model,with small adjustment: we shifted 2 hours back to mimic a daily trac variationin Indonesia with the peak hour during 8 PM - 10 PM1 (See Figure 3.3). Sincethere is a lack of study to model a trac prole in Indonesia, we adapted thepercentage value in Europe, i.e. the percentage of a total user during the peakhour is 16% while during the low-trac hour is 2% of total user. Hence, thetotal daily average power between Europe model and adapted model will be thesame.

The power evaluation model which used in this thesis was adapted from[11]. The power during active mode and sleep mode for LTE and 5G shown

1Based on interview to Indonesia's network operator employee


5 10 15 20

Time (hour)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Tra

ffic

Pro

file

Daily Traffic Variation

Figure 3.3 Daily Data Trac Variation Prole

in Equation 3.5 and Equation 3.6 respectively. For carrier aggregation mode,we assume the power needed will increase 45% due to the need of generatingadditional equipment. The reason to add 45% instead of additional 100% ofpower is that the second system is expected to utilize some part of equipmentfrom the based network [33].

PLTEactive = NTRX(∆pPLTEtx + Po) PLTEsleep = NTRXδLTEPo (3.5)

P 5Gactive = Ns(P

5Gtx

sε+NPc + PB) P 5G

sleep = Nsδ5GPB (3.6)

After we have the power consumption during the active and sleep mode, thetotal daily average area power consumption can be computed as:

Parea =1

24

∑24t=1

∑NBS

i=1 Pactiveηti + Psleep(1 − ηti)

A[kw/km2] (3.7)

where ηti represents the BS load in time t.

CHAPTER 4

Network Layout and System

Model

Chapter 4. Network Layout and System Model 18

In this chapter, we explain how we model the network layout to mimic apractical area. Firstly we investigate what types of data that important to beconsidered. Secondly, we need to understand what kind of trac that we need toserve in the future, thus we discuss a trac forecasting by considering a realisticdata. Lastly, we discuss the system-level environment model that implementedin this thesis, such as propagation model, antenna model, and rain attenuationformula.

4.1 Existing Infrastructure and Trac Modeling

We gathered important data of existing infrastructure and trac conditionfor Leuwidamar and Panimbang area. The existing infrastructure is used asthe baseline to model and dimension a future solution network, and the currenttrac condition is used as the baseline to forecast a futuristic trac demand.

4.1.1 Current Condition

We take two samples of South Banten area. The rst region is Leuwidamar,which located in hilly terrain area and has sparse small villages surrounds, asdepicted in Figure 4.1. In this thesis, we classify Leuwidamar as a remote area.Leuwidamar is the main entrance for a community cultural heritage (Baduytribe, which is an international tourist attraction), thus they have many touristsvisiting the region. Furthermore, as to face ASEAN Economic Community,Baduy tribes also expected to be empowered by a social autonomy and creativ-ity [34]. Hence there will be a need for the area to be served by good and reliabletelecommunication services. The second region is Panimbang, which will repre-sent a rural open space area (see Figure 4.3) with people populated along thecoastline. Panimbang is a shing village, and there will be a high economicsactivity [35] in the region. The higher economic transaction might lead to ahigher trac demand since ICT could assist people to trade their commodity.By having those values, it is suitable to take Panimbang and Leuwidamar as aninitial project since they have good values in social and economic aspects.

As we decided to take LTE Network as a baseline of prospective 5G Net-work, we gather important parameters to model a system-level simulator as thefollowing:

Leuwidamar Area

In this thesis, we consider realistic BS location from one of the biggesttelecommunication operators in Indonesia. The base-station longitude and lati-tude data can be accessed publicly by using Network Monitor (NetMon) Appli-cation. A screenshot from an Android Phone when using NetMon to capturingBS location can be seen in Figure 4.2. There are three existing BS towers thatdeployed in Leuwidamar area (see Figure 4.1). Among those three BS, currently,


BS_1

BS_2

BS_3

Figure 4.1 Remote with hilly terrain

only one BS that able to operate an LTE system in 1.8 GHz, which marked asa red pin (BS_2). Two other BS operate the 2G and 3G network. The averageInter-Site Distance (ISD) in the area is 7.5 km.

In Leuwidamar area, the BS antenna generally installed at 45 m height, with3o of electrical (e-tilt) and mechanical tilting (m-tilt). However, one of the BSin the area placed at 40 m height with 2o of both e- and m-tilt. For an LTEBS, the bandwidth available can be adjusted from 10 MHz, 15 MHz to 20 MHz.Commonly the bandwidth is set to be 15 MHz1.

To evaluate practical network performance, we forecast the futuristic tracdemand by gathering current data trac condition which can be pulled fromthe core network. Since LTE is a new service in the area, the existing capturedtrac payload from LTE system will not represent a real data-trac demand.Therefore we take the example from 3G trac record. Moreover, the data-traconly captured a demand from one operator. Thus to mimic total data tracdemand, we assume that a 60% of the total user subscribes to our operator, andthe rest 40% is to other operators. Detail trac forecasting will be discussed in4.1.2.

To have a realistic forecasting we need to consider the area developmentopportunity, for example, the government's spatial planning or land use plan-ning, the economic activity, and so forth. Currently, people live in small villages

1Data provided by Network Operator


Figure 4.2 Screen-shot of BS location Monitoring using NetMon

which sparsely distributed in the area. In government's plan for LeuwidamarArea between 2010 - 2030, they plan to maintain a production forest areas. Thuswe can consider that the trac demand in the area would remain low since notmany people would be live in the forest.

Panimbang Area

The number of BS deployed in Panimbang area is higher than Leuwidamararea. There are currently eight BS deployed, with two of them providing LTEnetwork at 1.8 GHz. The map of Panimbang Area can be seen in Figure 4.3.In terms of BS Specication, the antenna height in Panimbang is ranging from39 m to 45 m, and two base station installed the antenna at 70 m height. E-tiltand m-tilt is varying from 2o to 5o.

compared to Leuwidamar, the trac demand in Panimbang area is muchhigher because of the higher number of population. Detail trac demand dis-cussion is discussed in Section 4.1.2. Moreover, based on the local government


BS_1

BS_3

BS_2

BS_5

BS_4

BS_7

BS_6

BS_8

Figure 4.3 Open Space

spatial planning (2010 - 2030), Panimbang is planned to be a neighborhood areaand agriculture exploitation. There is a high trading opportunity from the sh-ing industry and agriculture industry with yams as the main commodity. Thus,we could expect a high trac demand in such area.

4.1.2 Trac Condition

Basically, we forecast the future condition by considering four importantaspects:

Population Density

Device Capability

Additional User Type

Average Subscription

Firstly, When we investigate the trend of population growth in statistics,we can estimate the expected number of population in the following years. Topredict the futuristic trac demand, we should not only consider the increase ofsubscription from each UE, but also the increase in the number of users. The in-crement of subscribers is driven by a higher user penetration and the populationgrowth. Thus having a record of population density growth is important. In thisthesis, we assume that the population density increase 1 person/km2 bi-yearlyin Leuwidamar. In Panimbang, the population density increase 2 persons/km2

each year.

Secondly, the device capability data represents a total number of mobile userthat served by the base stations in the area. The data can be accessed from


the core network. It records a total number of devices which capable to access2G, 3G, and/or 4G. However, by having this data, we only able to records thenumber of users from one operator. Supported by a market-share data whichprovided in [36], we assume that 60% of subscribers are served by our provider.Then to calculate a total number of mobile phone user in the area is Total User= Subscribers

0.6 . By having both population density and device capability data,we can predict the percentage of users from all population. In 2016, the per-centage of mobile phone subscribers from Leuwidamar and Panimbang area is5% and 18% respectively. This is due to the fact that Panimbang has higherpopulation density and has more access compared to Leuwidamar the remotearea (see detail in Table 4.1.

Next, the additional user type. Several studies considered some type ofheavy-user which could be served by broadband access in the future 5G net-work [30]. In this thesis, we also consider two types of heavy users which areMobile PC User and Tablet User. The number of heavy-user in Panimbang ismore likely to be higher compared to Leuwidamar. The reason is that the neigh-borhood in Panimbang is well-established, while in Leuwidamar the villages aresmall and remote. Hence, the probability of having Mobile PC and Tablet inPanimbang is higher.

Lastly, average subscription per user is important to calculate overall tracrequirement. We adopt trac modeling from EARTH project [27] which accounta mix of trac generated from several types of terminal. To dene average sub-scription in each type, we consider a data provided by Ericsson Mobility Reportfor South-East Asia and Oceania Region which stated that the average mobilesubscription in Indonesia is 1.2 GB/month [30]. However, it only captured thegeneral area, without considering urban or rural dierences. We interviewedsome users in the area, and they typically consume 2 GB/month. We assumethe Mobile PC user and Tablet user have a higher subscription, 1.5 times and1.25 times of mobile phone user subscription. There will be an increment of2GB/month in each year for mobile subscribers. The average trac demandper subscriber is calculated by using Equation 4.1, where rk is the monthlydata demand and sk represents the ratio of subscribers for type k device.

rav =∑k

rksk in [GB/month/subscriber] (4.1)

Most of the considered data were gathered from Central Bureau of Statisticsin Indonesia, a white paper from companies (e.g. Ericsson) and interview result.We take a sample of futuristic trac demand in ve stages. The rst three isthe forecasting of trac demand in 2017, 2018 and 2019 while the last two havea two years gap to the previous stage i.e. 2021 and 2023. The reason to takeve samples with a dierent gap is to capture the increasing trend of futuristictrac demand with adequate estimation. Considering the gap between 3G to


Table 4.1 Trac Estimation

Details 2017 2018 2019 2021 2023Leuwidamar Subscriber

Population Density 321 321 323 324 325Total Population 56,692 56,692 57,046 57,222 57,399Mobile Phone User 6% 7% 8% 11% 16%Mobile PC User 1% 1% 1% 1% 1%Tablet User 1% 1% 1% 1% 1%

Panimbang Subscriber

Population Density 389 391 393 397 401Total Population 51,660 51,925 52,191 52,722 53,253Mobile Phone User 19% 20% 21% 23% 25%Mobile PC User 7% 7% 7% 8% 8%Tablet User 2% 2% 2% 2% 2%

Subscription (GB/Month per Subscribers)

Mobile Phone 4 6 8 12 16Mobile PC 6 9 12 18 24Tablet 5 7.5 10 15 20Average Subscription (GB/Month per Subscribers)

Leuwidamar Area 4.22 6.34 8.48 12.55 16.59Panimbang Area 4.57 6.83 9.073 13.65 18.09

4G is approximately 10 years, we decided to evaluate the trac for 6 or 7 yearsas the rst study, which apparently seems sucient. The detail values for eachaspect can be seen in Table 4.1.

4.1.3 Trac Demand Calculation

If we visit a Feasible Load Model Equation (Eq. 3.2), the feasible load isdependent on trac demand of each user Ωi. To calculate Ωi, we need to denea busy hour that represents trac usage during a time unit. In this thesis, weassume the trac usage is spreading uniformly in 12 hours for 30 days. Sincein rural/remote area most people do not have exact oce hours such in a denseurban area (which might have a shorter busy hour, for example, 8 hours for 20days). Aside from busy hour parameter, the trac demand also dependent to(rav)i, as calculated by using Equation 4.2

Ωi = (rav)i8 × 103

30 × 12 × 3600Mbps (4.2)


4.2 System-level Environment Model

A practical network layout in a system-level simulator is discussed in thissection, together with a model of the radio environment to evaluate user per-formance.

4.2.1 Remote Area: Leuwidamar Regency

There are three base stations existed in Leuwidamar, serving approximately176 km2 area. In the system-level simulator, we located the base stations usingrealistic longitude and latitude data. To mimic real user distribution, we dis-tributed users to 50 small villages which sparsely distributed in the area. Thebase stations location, each sector azimuth direction, and village distributioncan be seen in Figure 4.4.

Figure 4.4 Leuwidamar Layout in System-level Simulator

4.2.2 Rural Area: Panimbang Regency

Since the number of population density is higher than Leuwidamar, Panim-bang has more user and denser network. Currently, there are eight deployedbase stations in the area, which distributed along the coastline. The user distri-bution is almost uniformly distributed along the road, but not in all area which


covers around 132 km2. We can see the user distribution and slight picture ofbase stations coverage in Figure 4.5

Figure 4.5 Panimbang Layout in System-level Simulator

4.2.3 Propagation Model

Reliable model of milimeter wave (mmWave) path loss in rural area hasbeen studied in [37]. The study shows that the existing 3GPP and ITU-RRMa models is not suitable for rural area. The authors modied the currentmodels with empirical results from an experiment held in US's rural areas.The propagation model that adopted in this thesis is the "close-in free spacereference distance with height" (CIH-RMa). We consider the CIH-RMa NLOSmodel since generally the distance from BS to UE is quite far. The CIH-RMANLOS can be calculated as the following:

PLCIH−RMaNLOS (fc, d, hBS) = 32.4 + 20 log10(fc) +

30.7(

1 − 0.049(hBS − 35

35

))log10(d) + XσNLOS ; (4.3)

where d is the distance between BS to UE (d ≥ 1 m), fc is the operatingfrequency, hBS is the BS transmitter height and σNLOS is the standard deviationof the shadow fading with σNLOS = 6.7 dB


4.2.4 Antenna Design

The antenna pattern in [38] is considered in this thesis. The horizontalantenna pattern for a three sector cell site is expressed as:

A(θ) = −min

[12

(θ

θ3dB

)2

, Am

](4.4)

where θ3dB represents the azimuth HPBW, and Am = 20 dB i.e the dierenceof main lobe and side lobe is 20 dB. An example of antenna pattern with theHalf Power Beamwidth (HPBW) 60 is depicted in Figure 4.6.

-200 -150 -100 -50 0 50 100 150 200

-20

-15

-10

-5

0

Degree

dB

Figure 4.6 Antenna Pattern of 60 HPBW

4.2.5 Rain Attenuation Model

Rain attenuation in mmWave is signicant. The most widely acceptablerain attenuation model is from ITU-R Recommendation which illustrates in4.5, where R is the rain rate (mm/h), k and α are cocients which dependentto functions of frequency [39]. A study in [22] shows a rain attenuation value (indB/km) with respect to dierent frequencies in Jakarta. The author accountsthe rain attenuation during a heavy rain with 100 mm/h rain rate. In thisthesis, we consider a rain attenuation for 15 GHz link is 6 dB/km, and for 28GHz link is 14 dB/km.

γR = k Rα (4.5)

CHAPTER 5

Simulations

Chapter 5. Simulations 27

Table 5.1 Trac Requirement

Details / Year 2017 2018 2019 2021 2023Leuwidamar Area

Avg. GB/month/user 4.22 6.34 8.48 12.55 16.59Number of User 4,150 5,000 5,800 7,500 10,400

Panimbang Area

Avg. GB/month/user 4.57 6.83 9.073 13.65 18.09Number of User 13,140 13,740 14,320 16,000 17,220

In this chapter, we discuss two aspects. First is the trac requirementthat needed to be fullled, and the second one is the parameter setting for thesimulation scenarios.

5.1 Trac Requirement

We need to set a trac requirement that needed to be fullled for the futurenetwork solution. There are two main important parameters to be considered.The rst parameter is the average trac subscription which calculated by usingEquation 4.1. The second parameter is the total number of subscribers whichneeded to be accounted. The trac requirement details is shown in Table 5.1

5.2 Simulation Scenarios and Parameter Setup

As discussed in Chapter 2, we consider four technology components to di-mension networks in order to serve futuristic trac demand. We proposed vesystems to be evaluated for Leuwidamar and Panimbang areas:

5.2.1 Case 1: Single Carrier (SC) LTE 1.8 GHz

We intend to evaluate how current LTE system serve futuristic trac de-mand. This will be the main baseline of comparison to other four scenarios.The motivation to use 1.8 GHz as the operating frequency is because it is thefrequency used for LTE in Indonesia. Currently our operator use 15 MHz band-width, however in this thesis we assume the operator utilize 20 MHz as theyhave the license up to 20 MHz.

5.2.2 Case 2: Carrier Aggregation (CA) LTE 1.8 GHz + 2.6

GHz

CA was introduced in LTE-Advanced standard, thus applying CA in LTEsystem is need to be evaluated. Currently, some popular frequencies (e.g. 800MHz, 2.1 GHz, 2.3 GHz, 3.5 GHz) are fully allocated in Indonesia1. Although

1Based on an interview with Ministry of Information and Communication team in In-donesia


2.6 GHz is also allocated for other services, it is one of popular frequency for LTEin some countries2. When the current licenses for 2.6 GHz band are expired,future allocation for the band still has not decided. Hence, we evaluate theperformance of aggregating 1.8 GHz LTE system with 2.6 GHz LTE system asone of the future solutions.

5.2.3 Case 3: SC 5G 15 GHz

In the rural/remote area, most cases classied into coverage limited area.However, in the future, we might face a capacity limited scenario in a rural areaas well. Thus evaluating mid-solution (between low frequency and mmWavesystem) is needed. It can provide a wide bandwidth and not highly suer froma worse propagation environment compared to mmWave. Some references statedthat there will be up to 100 MHz bandwidth available at this frequency. [33]

5.2.4 Case 4: SC 5G 28 GHz

Most studies evaluate 28 GHz performance as one of the future 5G solutions.Generally, the study took a dense urban area as a based network layout. In [40],there would be up to 850 MHz bandwidth proposed for a mobile communication,and it can give us a huge capacity. However, 28 GHz link will suer from highpropagation loss, especially in a rural/remote area when a wide coverage isrequired. Hence a 28 GHz 5G link performance in the rural/remote area isimportant to be investigated.

5.2.5 Case 5: CA LTE 1.8 GHz + 5G 15 GHz

Since a backward-compatible network is expected in the future, evaluatingcarrier aggregated link between existing LTE system and a future 5G system isimportant. Thus we investigate how current 1.8 GHz LTE system will performwith 15 GHz 5G system. The reason why we choose 15 GHz is it can give usa wide bandwidth with no dramatic loss of propagation loss compared to 28GHz link since broadband access in the rural area is expected to serve a widecoverage.

The parameters used in the simulations for all cases can be seen in Table 5.2

21800 MHz and 2.6 GHz still the most popular LTE bands; available onhttp://www.policytracker.com/headlines/over-two-thirds-of-global-lte-networks-use-the-1800-mhz-or-2.6-ghz-bands


Table 5.2 Parameter Setup

System and Path Loss Parameters

Parameter ValueCase 1 / Case 2 Case 3 / Case 4 Case 5 unit

Carrier Frequency 1.8 / 1.8 + 2.6 15 / 28 1.8 + 15 MHzBandwidth 20 / 20 + 40 ≤ 100 / ≤ 500 20 + ≤ 100 MHzBS Antenna Gain 18 24 / 27 18 / 24 dBiBS Antenna HPBW (65/15) (20/10) (10/10) (5/15) (20/10) DegBS Antenna Height 45 mUE Antenna Gain -3 dBiBeamforming No UE-Spec No / Ue-SpecNoise Figure 9 dBShadow Fading 6.7 dB

Power Consumption Parameters

Parameter ValueLTENum. of TRX NTRX 2 /SectorPower Slope ∆p 4.7Transmit Power PtxLTE 20 WattBaseline Power Po 130 WattCell DTX Capacity δ Min. 0.845GPA Eciency ε 0.25Circuit power / RF Pc 1 WattTransmit Power P 5G

tx 40 WattBaseline Power 260 WattCell DTX Capacity Min. 0.29

CHAPTER 6

Result and Discussion

Chapter 6. Result and Discussion 30

There are two main results discussed in this chapter. The rst one is thediscussion of how radio environment and future technologies will aect user per-formance. The second one is the performances of ve dierent system solutionsin which able to fullling futuristic trac demand.

6.1 Impact of Radio Environment

To be able to give a suitable network solution, rstly we need to understandhow the environment aects our user performance. In this section, we rstlystudy the impact of changing operating frequency and how rain attenuation af-fect the performance when we go to a higher frequency. Secondly, we investigatethe impact of utilizing prospective 5G technologies to our user performance.

6.1.1 Impact of Operating Frequency

The main advantage to occupy a higher frequency is the ability to use anempty wide bandwidth. However, a high frequency also gives us a worse propa-gation path loss. We evaluate the propagation path gain for the low frequencywhich represented by 1.8 GHz link, and how it will change when we utilizehigh frequency such as 15 GHz and 28 GHz. We analyze the 20th percentileof users as an example, to have a grip of how the system perform in betweenof average user to the cell-edge user. To have a fair comparison, we investigatesame network (Leuwidamar), set the same number of users, same base stations,same bandwidth (20 MHz), same antenna gain (18 dBi) and same antenna'sbeamwidth (Azimuth HPBW = 65o, 3-Sectoral Antenna). The result can beseen in Figure 6.1

As we can see from Figure 6.1a, the path gain decrease dramatically whenwe going from low to high frequency. From 1.8 GHz link to 28 GHz link, thepath gain decrease 24 dB. Consequently, the downlink (DL) user throughputperformance also decreased as depicted in 6.1b.

6.1.2 Impact of Rain Attenuation

A Rain attenuation commonly neglected when we designing a mobile com-munication network. This can be accepted if we use a low frequency i.e. below6 GHz. However, there is a signicant loss when we use a frequency above 6GHz during the rainy day. Referred to the result which provided in [22], therain attenuation linearly increases with frequency. In this thesis, we consider aheavy rain with 100 mm/h rain rate, which has 6 dB attenuation per km for 15Ghz link and 14 dB attenuation per km for 28 GHz link.

To evaluate a rain attenuation impact to the user performance, we investi-gate three parameters: received power, SINR, and downlink user throughput.We take Leuwidamar area as an example of the network layout. We use the


-180 -160 -140 -120 -100 -80

dB

0

0.2

0.4

0.6

0.8

1C

DF

Pathgain

1.8 GHz

15 GHz

28 GHz

(a) Pathgain comparison for frequency 1.8 GHz, 15GHz and 28 GHz

4 6 8 10 12 14 16 18

Traffic per Subscriber [GB/month]

0

20

40

60

80

100

Use

r T

hro

ug

hp

ut

[Mb

ps]

DL User Throughput @20thperc

1.8 GHz

15 GHz

28 GHz

(b) 20th Percentile DL User Throughput compari-son for frequency 1.8 GHz, 15 GHz and 28 GHz

Figure 6.1 Impact of Operating Frequency to the (a) Pathgain and (b) DLUser Throughput. Higher frequency leads to worse performance for bothpathgain and throughput


same number of users and the same number of BS. The bandwidth and antennagain follow the parameters by Case 3 for SC 5G 15 GHz link and Case 4 for SC5G 28 GHz link.

In Figure 6.2a we can see a huge decrease of the received power for bothlinks, 25 dB for 15 GHz link and 58 dB for 28 GHz link. The same trend hap-pens when we evaluating the rain attenuation impact to SINR which depictedin Figure 6.2b. The low signal received was caused by a high attenuation whichaccounts 6 dB/km and 14 dB/km for 15 GHz and 28 GHz link, respectively.Since we deploy only three BS on 176 km2 area, with a far ISD, the distancebetween users and base stations is quite long. With such distant, a user whichlocated more than 1 km from BS will highly suer from a rain attenuation.If we evaluate the throughput dierence from a not rainy day to a rainy day,which depicted in Figure 6.2c, the downlink user throughput drop away fromhundreds of Mbps to zero Mbps.

By having this result, we surely need to consider the rain attenuation if wewant to dimension a system which uses a high operating frequency. Especiallywhen we planning a network in equator countries such as Indonesia, who has ahigh rain rate.

6.2 How Prospective 5G Technology Aect User Perfor-

mance

In this section, we investigate the impact of applying three key technologyaspects in prospective 5G Networks. Firstly, we study the wide bandwidthavailability which can be occupied in a high operating frequency. Secondly,we evaluate the impact of UE-Specic Beamforming. Lastly, we analyze theimpact of applying carrier aggregation scheme and how it aects the networkperformance.

6.2.1 Impact of Wide Bandwidth Availability

The rst motivation of evaluating high frequency (or mmWave) performanceis because of its wide bandwidth availability for mobile broadband communica-tion that can be used to served futuristic trac demand. Wide bandwidth givesmore usable Resource Blocks (RB) e.g. 10 MHz have 50 usable RBs, 20 MHzhave 100 usable RBs, 100 MHz have 5 x 100 usable RBs.

To evaluate the impact of provided bandwidth, we consider 5G 15 GHz linkin Leuwidamar as the baseline. We use the same number of users, the samenumber of base station, same power transmit, and same antenna gain. Weevaluate four dierent values of bandwidth, ranging from 20 MHz to 100 MHz.Since the received power from all cases has no signicant dierence, we only


-250 -200 -150 -100 -50 0

Received Power [dBm]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Rain Attenuation to Received Power

15 GHz No Rain

15 GHz Rain

28 GHz No Rain

28 GHz Rain

25 dB

58 dB

(a) Rain attenuation impact to received power in 15GHz and 28 GHz link

-100 -50 0 50 100

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Rain Attenuation to SINR

15 GHz No Rain

28 GHz No Rain

15 GHz Rain

28 GHz Rain

7 dB

42 dB

(b) Rain attenuation impact to SINR in 15 GHzand 28 GHz link

4 6 8 10 12 14 16 18


0

100

200

300

400

500

User

Thro

ughput [M

bps]

Impact of Rain Attenuation to DL User Throughput

15 GHz No Rain

15 GHz Rain

28 GHz No Rain

28 GHz Rain

(c) Rain attenuation impact to 20th Percentile DLUser Throughput in 15 GHz and 28 GHz link

Figure 6.2 Impact of Rain to (a) Received Power, (b) SINR and (c) DL UserThroughput. The rain is highly attenuate the signal especially in 28 GHz link


evaluate SINR and downlink user throughput as depicted in Figure 6.3.

A wider bandwidth will give us a higher capacity, thus decreasing the pos-sibility of having interference. The possibility of having interference is linearlycorrelated to the load of the BS. As we can see in Figure 6.3a there is a 10dB decrease when we enlarge the bandwidth from 20 MHz to 100 MHz. Inresult, we can experience a better downlink throughput performance if we use awide bandwidth compared to a narrow one. However, we must note that thereis an extra loss of SNR when we enlarge our bandwidth due to the increasednoise power. For example, enlarging bandwidth from 20 MHz to 100 Mhz leadsto 10 log10(100/20) = 7 dB extra loss. In the same time, it can decrease theinterference and increase throughput performance.

6.2.2 Impact of UE-Specic Beamforming

Worse propagation in the high operating frequency can be mitigated usinga high antenna gain. With a high frequency, the antenna size can be smallerthus we can pack tens and hundreds more of single antenna elements with areasonable size. By utilizing multi-elements of the antenna, we can achieve avery directive antenna. Thus we will have a very high gain in the particular di-rection, and at the same time suppress the interference from neighbors. Anotheradvantage of using massive array antennas is that we can have a high numberof beams and allocate each beam to each user to give a better performance.

To evaluate the impact of UE-Specic Beamforming in our system-level sim-ulator, we study the received power, SINR and downlink user throughput. Weuse Leuwidamar area as a baseline network, and operating 5G 15 GHz linksystem. We assume the same number of users, base stations, and bandwidth.Referred to Equation 2.1 in Section 2.2, the antenna gain is dependent to itsHPBW, both in azimuth and elevation. Thus in this simulation, the antennagain for 3-sectoral antenna with Azimuth HPBW θ = 65o and Elevation HPBWϕ = 15o is 18 dBi. In practical, the ϕ is narrower than 15o, however, it willgive us a too higher gain, with more than 18 dBi. An 18 dBi gain is commonfor three-sectoral antenna gain in LTE system [33]. Next, to model 15 GHzantenna, we assume narrower beamwidth with θ = 20o as referred to [41], andϕ = 10o for our highest possibility of UE-Specic beamforming technology inthis system. We also study the 15 GHz link system that has θ = 30o and ϕ =15o with 20 dB gain, to understand how the width of HPBW aect the userperformance. The result can be seen in Figure 6.4.

With a higher antenna gain, the received power increase. As depicted in Fig-ure 6.4a, when we applying UE-Specic Beamforming we can achieve approxi-mately 11 dB higher received power compared to a normal 3-sectoral antenna.The main contributor to this increase is a higher antenna gain and the directedbeam that serving each UE. In [33], a 5 × 20 array can accounts for 400 beamsin the grid. Hence it is reasonable to allocate one beam to each user. In this


-10 0 10 20 30 40 50 60 70

SINR

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Bandwidth to SINR

100 MHz

60 MHz

40 MHz

20 MHz

(a) SINR Comparison of 20 to 100 MHz Bandwidth

4 6 8 10 12 14 16 18


0

100

200

300

400

500

User

Thro

ughput [M

bps]

Impact of Bandwidth to DL User Throughput 20th

perc

100 MHz

60 MHz

40 MHz

20 MHz

(b) Impact of bandwidth to User DL Throughput20th Percentile

Figure 6.3 Impact of Bandwidth to (a) SINR and (b) DL User Throughput,Wide bandwidth leads to higher number of RB so then user experience lessinterference and able to use more RB. Thus we have better performance forboth SINR and Throughput


-140 -120 -100 -80 -60 -40

Received Power [dBm]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Beamforming to Received Power

NO BF (3 sectoral antenna)

with BF HPBW 20/10

with BF HPBW 30/15

11 dB

(a) Impact of beamwidth to received power

-20 0 20 40 60 80

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Beamwidth to SINR


with BF HPBW 20/10

with BF HPBW 30/15

10 dB

(b) Impact of beamwidth to SINR

4 6 8 10 12 14 16 18


0

100

200

300

400

500

Use

r T

hro

ug

hp

ut

[Mb

ps]

Impact of Beamwidth to DL User Throughput 20th

perc


with BF HPBW 20/10

with BF HPBW 30/15

(c) Impact of beamwidth to User DL Throughput20th Percentile

Figure 6.4 Impact of UE-Specic Beamforming to (a) Received Power, (b)SINR and (c) DL User Throughput. Narrow beam leads to higher gain andmakes performance better for those three aspects


simulation, each user will choose beam with the best performance.

The received power of HPBW= 30/15 antenna is better compared to HPBW= 65/15 antenna. When we narrow the beam to HPBW = 20/10, the receivedpower is higher. The reason behind this is the narrower beamwidth will give ushigher gain (with 4 dB increase for 10o narrower beamwidth in this particularscenario). The same impact occurs when we evaluating SINR, as shown inFigure 6.4b. The main contributor to 10 dB decrease when we utilizing a UE-Specic Beamforming is the narrow beamwidth that decreases the interferencefrom neighboring BS. Consequently, downlink user throughput performance willhave a signicant increase when we applying UE-Specic Beamforming. Thethroughput also linearly increasing with a narrower beamwidth.

6.2.3 Impact of Carrier Aggregation

Another way to have a higher capacity is by using Carrier AggregationScheme. CA helps to give more capacity by combining bandwidth in two dier-ent frequency bands (or more). In this section, we evaluate the impact of usingCA scheme to the user performance.

To evaluate the impact of CA, we use an LTE system which operating in1.8 GHz and 2.6 GHz frequencies and take Leuwidamar as our network layout.We use the same number of users, same number of base stations, same antennagain, but dierent bandwidth. We assume that 2.6 GHz will be able to give us alarger bandwidth that can be used for mobile broadband communication. Thecomparison of SINR and user downlink throughput between Single Carrier sys-tem and Carrier Aggregated system is shown in Figure 6.5. When we implementa carrier aggregation scheme, we basically adding more system network over thesame area. Users will be connected to a dierent system, thus decreasing thepossibility of having interference. In Figure 6.5a, we can see there is a 15 dBincrease of SINR if we compare 20th percentile of SC 1.8 GHz system to CAsystem. Consequently, it boosts our downlink user performance, as depicted inFigure 6.5b.

The resource allocation for users is decided by setting up a 'threshold'. Themain reason to set a threshold is to have a clear boundary of two dierentsystem coverage. A lower frequency is more likely to have a larger coverage,compared to the higher frequency. A higher frequency gives us a higher ca-pacity, thus allocating the users to higher frequency is a priority. To illustrate,consider we set -90 dBm as system threshold. If a user's received power fromhigh-frequency band system is larger than -90 dBm, it will be allocated to thehigher frequency until the serving cell is fully utilized. If the received power fromthe high-frequency band is lower than the threshold, the user will be allocatedto a lower frequency with a better signal power. To have a better understandingof the impact of a threshold value, we can see Figure 6.6. As our target require-ment is to serve 50 Mbps throughput in the latest trac stage, we dimension


-20 0 20 40 60 80

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of CA to SINR

SC LTE 1.8 GHz (20 MHz)


CA LTE 1.8 GHz + 2.6 GHz

15 dB

(a) Impact of CA to SINR

4 6 8 10 12 14 16 18


0

50

100

150

200

250

300

Use

r T

hro

ug

hp

ut

[Mb

ps]

Impact of CA User DL Throughput 20th

Percentile




(b) Impact of CA to User DL Throughput 20th Per-centile

Figure 6.5 Impact of Carrier Aggregation to (a) SINR and (b) User DLThroughput. By using carrier aggregation scenario, all users spread into twodierent network link i.e. LTE 1.8 GHz link and 2.6 GHz link. Hence, eachuser experience better network performance


a network with such requirement, and we try to set dierent threshold overthe same scenario. When we set too high threshold (-90 dBm), the user seemsforced to choose a low-frequency band in which having a low capacity. Thus theperformance in earlier trac stage demand is lower. However, if we set a toolow threshold (-130 dBm), the user will be allocated to a high-frequency bandwith a higher capacity, and consequently, the users which located far from BSwill suer from worse propagation path loss compared to the lower frequencyband.

To dimensioning a network with CA scheme, especially for Case 2 and Case5 which will be discussed in the next section, we implement an optimal thresholdto maximize user performance in all stages of trac demand.

6.3 Solution for Remote Area: Leuwidamar Regency

In this section, we evaluate the performance of several possible solutions ofLeuwidamar Area. The trac demand is forecasted by having a realistic data,and the network layout is designed based on practical area. We proposed twoapproaches to serving a futuristic trac demand: designing an LTE Systemwhich represented by Case 1 and Case 2; and designing a 5G System whichrepresented by Case 3, 4 and 5.

6.3.1 LTE Solutions

Referred to the research question of: How will LTE fulll futuristic tracdemand? ; We evaluate the network performance of LTE system as prospectivesolutions to serving futuristic trac demand. Two cases are considered i.e. Case1 as a Single Carrier system of LTE network in 1.8 GHz, and Case 2 as a CarrierAggregated LTE system in 1.8 GHz and 2.6 GHz.

Case 1: SC LTE 1.8 GHz

Currently, there are 3 BS deployed in the area, yet only one BS implementingLTE system. In this scenario, we investigate how would SC LTE 1.8 GHz willperform if we relied on this system only to fullling futuristic trac demand.First priority is to fully utilize existing network infrastructure, thus in this the-sis, we assume all existed BS implementing an SC LTE system. We evaluatethe system, to nd out whether it can meet futuristic trac requirement ornot. However, we cannot only use 3 BS to serve all users because of its limitedcapacity, which leads to a very low network performance. By using a networkdimensioning method as discussed in Section 3.2, we add more BS in order tofulll futuristic trac demand. In this case, the requirement is 50 Mbps down-link user throughput in 20th percentile of users. By using this approach, thesolution is to have 36 BS in total which means we need to have 33 additional BSfrom the current network. The network layout for Case 1 solution illustrated in


-20 0 20 40 60 80

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of CA Threshold to SINR

Threshold = -90 dBm

Threshold = -110 dBm

Threshold = -130 dBm

(a) Impact of CA Threshold to SINR

4 6 8 10 12 14 16 18


0

50

100

150

200

250

300

Use

r T

hro

ug

hp

ut

[Mb

ps]

Impact of CA User DL Throughput 20th

Percentile




(b) Impact of CA User DL Throughput 20th Per-centile

Figure 6.6 Impact of Threshold in Carrier Aggregation Scheme for (a) SINRand (b) User DL Throughput. The CA Threshold must be set wisely. If thethreshold is set too low, most users connected to high frequency and cell-edgeusers suer from worse signal quality, if the threshold is set too high, mostusers connected to low frequency and suering from low capacity


Figure 6.7, and the network performance comparison between before and afterthe densication is shown in 6.8

Figure 6.7 Proposed Solution of Case 1 Network Layout in Leuwidamar Areawith 36 BS

As depicted in 6.8a, the SINR of 3 BS network is very low. Consequentlyit only able to serve trac demand until the second stage (when the averageuser subscription around 6 GB/month for 13 thousands subscribers, see 5.1).After the densication to 36 BS, the user SINR increase approximately 15 dB at20th percentile and have signicant increase for the downlink user throughputperformance. The reason why we need such a huge number of a base station isthat the lacking capacity available in SC LTE 1.8 GHz system. It only provides20 MHz bandwidth, which insucient to fulll futuristic trac demand by onlyhaving a few number of BS. Thus, to be able to serve futuristic trac demand,Case 1 need 36 Base Station, with 33 additional BS (12 × densica-tion).

Case 2: CA LTE 1.8 + 2.6 GHz

In this case, we can occupy a wide bandwidth by aggregating two dierentfrequency bands. In 1.8 GHz, we use 20 Mhz bandwidth and in 2.6 GHz link, weuse 40 MHz bandwidth. When dimensioning the network for Case 2 to 5, we setmore strict requirement in order to meet the expectation of "50+ Mbps every-


-20 0 20 40 60 80

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

SINR of SC LTE 1800 Network

BS = 3

BS = 36

(a) SINR Performance of SC LTE 1.8 GHZ Network

4 6 8 10 12 14 16 18


0

50

100

150

200

250

Use

r T

hro

ug

hp

ut

[Mb

ps]

DL User Throughput Performance 20thPerc

BS = 3

BS = 36

(b) DL User Throughput Performance 20th Per-centile of 1.8 GHZ Network

Figure 6.8 SINR Performance (a) and Throughput Performance (b) of Case1 Solution in Leuwidamar Area. LTE 1.8 GHz network is lacking of capacity,hence densication of network is needed to fulll futuristic trac demand


where", which also includes the cell-edge users. In the system-level simulator,the cell-edge users are represented as the 5th percentile of users. To evaluate theperformance of Case 2, we compare the network performance between applyingsingle carrier network and the aggregated carrier scheme. Firstly we evaluatehow CA works in the existing BS network layout (with only 3 BS deployed), andthe result shows that the futuristic trac demand is not met. We densify thenetwork by adding 4 additional BS, thus we have 7 BS in total. By deploying 7BS and implementing CA scheme, the futuristic trac demand is fullled. Notethat the threshold for resource allocation is -110 dBm.

As discussed in Section 6.2.3, the SINR for SC schemes only is very low. CAscheme helps to increase the SINR as well as the throughput. This is mainly dueto the fact that we decrease the interference for every user hence the SINR isincreased. In this particular case, when we applying SC for 1.8 GHz link or 2.6GHz, the latest stage of trac demand is not fullled. In contrast, it is fullledwhen we applying CA scheme. Thus, in Case 2 we need 7 Base Station,with 4 additional BS (2.3 × densication).

6.3.2 5G Solutions

Next, we intend to answer other research question of How will 5G fulll fu-turistic trac demand?. In this section, we investigate several possible scenariosof how future 5G network will be designed to serve futuristic trac demand.When we want to upgrade a network, the rst priority is to exploit existinginfrastructure i.e. installed the system in current existing BS and see whether itmeets the requirement or not. Thus, we deployed 5G network both in 15 GHzlink and 28 GHz link for all three existing BS. The result can be seen in Figure6.9. Both 15 GHz and 28 GHz link is able to serve futuristic trac demand,with a minimum bandwidth 75 MHz and 80 MHz respectively. Thus, in thiscase, 5G network works properly without any additional BS. Noted that in thisgure, we have not included any rain attenuation.

However, as discussed in Section 6.1.2, in a high operating frequency, therain attenuation highly aect the user performance. Thus evaluating the solu-tion which accounts a rain attenuation is important. The rest of this section isdiscussing the 5G solution during the heavy rain.

Case 3: SC 5G 15 GHz

In this section, we discussed how the SC 5G 15 GHz system fulll a futuris-tic trac demand which at the same time mitigate the rain attenuation. Whenwe account additional 6 dB/km loss during the rainy day, 3 BS will no longerable to serve the user demand, thus we need to add more BS. As previouslydiscussed, the SINR will perform better if we increase the number of BS. The


4 6 8 10 12 14 16 18


0

100

200

300

400

500

Use

rT

hro

ug

hp

ut[M

bp

s]

5th

DL User Throughput for 5G Network Solution

15 GHz (BW = 100 MHz)

28 GHz (BW = 500 MHz)

15 GHz (BW = 75 MHz)

28 GHz (BW = 80 MHz)

Figure 6.9 5th DL User Throughput Performance Comparison for several 5GSolution in Leuwidamar. Wider bandwidth available in higher frequency (28GHz). In this particular case, 28 GHz link with 500 MHz has the best perfor-mance

same impact also happens to the downlink user throughput. We intend to di-mension a network with 50 Mbps everywhere requirement which at the sametime giving us a least possible BS number to save more energy and cost. SevenBS is the minimum BS in Case 3 to fullling futuristic trac demand. Themaximum bandwidth available for 15 GHz link is 100 MHz, and also the min-imum requirement in this particular case. If we decrease the bandwidth, thenour requirement will not meet. So, to fulll futuristic trac demand by usingCase 3 scheme, we need 7 BS (2.3× densication).


In 28 GHz link, we have a very wide bandwidth available. In this thesis sim-ulation, we assume the maximum bandwidth available for 28 GHz is 500 MHz.However, the high frequency also gives us a worse propagation loss. We analyzethe performance of 5G network in 28 GHz link, which operating in Leuwidamararea with a sparse user distribution and a wide area. As we know that theperformance getting better when we add more BS. However, after we deploy 14BS with 500 MHz bandwidth, the futuristic trac demand still not met. Thedemand is met when we deploy 21 BS in minimum. With 500 MHz bandwidth,21 BS network perform far better than expected. If we want to have 50 Mbpseverywhere as the minimum requirement, the minimum bandwidth is 60 MHz.Furthermore, the SINR of 21 BS network using 60 MHz is better comparedto 21 BS network using 500 MHZ. The main reason is that when we enlargedbandwidth from 60 MHz to 500 MHz, we will suer from noise power which


accounts for 10 log10(500/60) = 9 dB.

The high number of BS needed shows that the solution is a coverage limitedscenario. Because we can meet the demand for a narrower bandwidth com-pared to Case 3, yet we need to densify the network because of the bad radioperformance. Thus, to meet future requirement, Case 4 needs 21 BS (7×densication).

Case 5: CA LTE 1.8 + 5G 15 GHz

We analyze the performance of carrier aggregated network between LTE in1.8 GHz and 5G in 15 GHz link. The existing BS (BS = 3) could not fulll thefuture trac requirement. After dimensioning the network, we found that theminimum BS to be deployed is 5 BS. In Figure 6.10 we can see the performanceof three dierent scenarios: SC LTE 1.8 GHz, SC 5G 15 GHz and CA for thetwo networks. For SC 1.8 GHz network, it has a good performance for the lowtrac demand. On the other hand, the SC 5G 15 GHz perform worse comparedto SC LTE 1.8 Ghz. This is due to the fact that 15 GHz link is suering fromworse propagation and have smaller coverage. However, if we aggregated bothnetworks, the performance is better and able to meet the requirement. Byhaving CA, the users that close to BS can be served by 5G 15 GHz network.While for the cell-edge user, they can be served by LTE 1.8 GHz. Since choosing5G 15 GHz is the priority, then LTE 1.8 GHz has many capacities availableespecially for the cell-edge user, hence the network performance is better. Notethat the threshold for resource allocation is -110 dBm, thus the users will onlyallocate to 1.8 GHz link when the propagation is worst. Case 5 need the leastnumber of BS compared to Case 1 to 4. In this case, we only need 5 BS tobe deployed (1.6× densication).

6.4 Solution for Rural Area: Panimbang Regency

In this section, we investigate the performance of the proposed solution inPanimbang Area. The trac requirement and network layout are based onrealistic condition. We proposed two approaches to nding a solution for thefuturistic network: First is to designing an LTE System which represented byCase 1 and Case 2, and the second is by designing a 5G System which representedby Case 3, 4 and 5.

6.4.1 LTE Solutions

We evaluate the network performance of LTE system as the prospective solu-tions to serve futuristic trac demand in Panimbang. Two cases are consideredi.e. Case 1 as a Single Carrier system of LTE network in 1.8 GHz, and Case 2as a Carrier Aggregated LTE system in 1.8 GHz and 2.6 GHz.


-20 0 20 40 60 80

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

SINR

SC LTE 15 GHz (60 MHz)


CA LTE 1.8 GHz + 5G 15 GHz

(a) SINR Performance

4 6 8 10 12 14 16 18


0

20

40

60

80

100

120

140

160

User

Thro

ughput [M

bps]

5thPerc. DL User Throughput CA LTE 1.8 + 5G 15 GHz



SC LTE 15 GHz (60 MHz)

(b) 5th Percentile DL User throughput CA LTE 1.8GHz + 5G 15 GHz Network

Figure 6.10 Case 5 Solution Performance of Leuwidamar Area for (a) SINRand (b) DL User Throughput. CA Scheme performs better compared to SCLTE or SC 5G


Case 1: SC LTE 1.8 GHz

In practical, there are 8 BS existed in the area. However, because of its lowcapacity, the LTE system with 8 BS will not fulll the futuristic trac demand.In fact, it needs a huge number of BS, with around 10× of densication. Thenetwork layout illustration of 81 BS deployment can be seen in Figure 6.11.

Figure 6.11 Proposed Solution of Case 1 Network Layout in Panimbang Areawith 81 BS

A same phenomena to the discussion in 6.3.1, the SINR increase when wedensify the network. Downlink user throughput also performs better when weincreasing the number of BS deployed. The reason why we need to have sucha huge number of BS is that its lack of capacity, which only provide 20 MHzbandwidth per cell. As we discussed earlier, the trac demand in Panimbangarea is higher than Leuwidamar area (See Table 5.1). Thus it makes senseto deploy a much higher number of BS to meet the futuristic demand. Bydeploying Case 1 scheme in Panimbang Area, we need 81 BS (10.125×densication).

Case 2: CA LTE 1.8 + 2.6 GHz

For Case 2 to Case 5 network, we set more strict requirement e.g. to have 50Mbps everywhere including the cell edge. Thus we intend to meet the 50 Mbpsin minimum for the 5th percentile of user. When we provide 20 MHz bandwidthfor LTE 1.8 GHz, 40 MHz for LTE 2.6 GHz, with -85 dBm threshold, we needto deploy 12 BS in order to meet the futuristic trac demand. The reason weset a higher threshold (compared to Case 2 in Leuwidamar Area) is that theexisted BS is denser and Panimbang area is smaller, thus we can set smallercoverage threshold between LTE 1.8 GHz and LTE 2.6 GHz.

As discussed in Section 6.2.3, there is a dramatic increase when we applyingCA scheme, compared to SC scheme. The main contributor is the decrease ofinterference. Consequently, the downlink user throughput performance for CA


performs better and able to meet the futuristic trac demand. In conclusion,Case 2 scenarios need 12 BS (1.5× densication) to served futuristictrac demand.

6.4.2 5G Solutions

We intend to evaluate how 5G will fulll futuristic trac demand in Panim-bang Area. As investigated in Leuwidamar Area, we also study the performanceof Case 3 to Case 5 in Panimbang. There are 8 BS existed in Panimbang, andit is highly recommended to utilize existed BS to deploy the future 5G network.We evaluate the 5G network both in 15 GHz and 28 GHz, deployed in 8 BSnetwork. As discussed in previous sections, we can not neglect the rain atten-uation when operating a high-frequency system. The performance comparisonof the 5G network during the not rainy and rainy day is illustrated in Figure 6.12.

The rain attenuation clearly aects the user performance, however the 5G15 GHz link still able to perform during the rainy day, but not the 5G 28 GHzlink. If we see Figure 6.12a, the SINR for 28 GHz link is very bad, with morethan 25 dB decrease. Note that the rain attenuation for 28 GHz is 14 dB/km,while in 15 GHz it is only 6 dB/km. Thus it makes sense to expect zero Mbpsfor 28 GHz.


As discussed earlier, the SC 5G 15 GHz network able to perform both innot rainy and rainy day, by using existed 8 BS network. In this section, weevaluate the network performance during the rainy day. When we use 100 MHzbandwidth, the performance is way higher than expected, even when it is a rainyday (See Figure 6.12b. In this case, we can decrease the minimum requirementof bandwidth in order to meet the future requirement. After evaluating theminimum bandwidth, 65 MHz is able to give us 50 Mbps minimum. To conclude,in order to meet futuristic trac demand by deploying Case 3 network, weneed 8 BS without additional BS (1× densication)


When we evaluate SC 5G 28 GHz performance in 8 BS network during therainy day, the futuristic demand is not fullled, thus we need to densify thenetwork. As previously discussed, the number of BS able to increase SINR andDL User Throughput. In this case, the minimum number of BS needed forCase 4 is 11 BS, and one BS dierence can make a huge impact. If we increasethe number of BS to 11 BS by using 500 MHz bandwidth, the futuristic tracdemand can be served until the last stage. In fact, the performance is beyond theexpectation, which reaches up to 110 Mbps for the cell-edge users. We evaluatethe minimum bandwidth needed to meet the future demand, and the answer is110 MHz bandwidth is the minimum requirement. Thus, we can conclude that


-50 0 50 100 150

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

Impact of Rain Attenuation to SINR @15 GHz and 28 GHz link

15 GHz No Rain

28 GHz No Rain

15 GHz Rain

28 GHz Rain

(a) SINR Performance during rainy day and notrainy day for 5G at 15 GHz and 28 GHz link inPanimbang

4 6 8 10 12 14 16 18 20


0

200

400

600

800

1000

User

Thro

ughput[M

bps]

5thPerc. DL Throughput @15 GHz and 28 GHz link

15 GHz No Rain

28 GHz No Rain

15 GHz Rain

28 GHz Rain

(b) 5th Percentile DL User throughput performanceduring rainy day and not rainy day for 5G at 15GHz and 28 GHz link in Panimbang

Figure 6.12 User Performance with and without rain attenuation for 5G So-lution in Panimbang for (a) SINR and (b) DL User throughput. During rainyday, 5G 15 GHz link is able to perform with sucient quality while 28 GHzlink is not available because of high attenuation


the number of BS needed for Case 4 is 11 BS (1.4 × densication)with a minimum Bandwidth = 110 MHz.

Case 5: CA LTE 1.8 + 5G 15 GHz

We intend to analyze the performance of aggregating LTE 1.8 GHz networkand 5G 15 GHz network in Panimbang Area. As we discussed in the previoussection, the LTE 1.8 GHz network can not meet the future requirement if it op-erates only in 8 BS network deployment. On the other hand, for SC 5G 15 GHzin 8 BS network deployment, it works properly and satises futuristic trac de-mand (See Figure 6.13) . However, we intend to evaluate the performance whenwe aggregating the 5G 15 Ghz link to LTE 1.8 GHz network and see what willbe the minimum bandwidth requirement of deploying the 5G 15 GHz system ifwe have the ability to aggregate the network with current LTE System. In thiscase, we use -105 dBm as a threshold, and 20 MHz in LTE 1.8 GHz system, and55 MHz in 5G 15 GHz system.

The CA scenario denitely helps increasing network performance. In Pan-imbang, we can say that it will be a capacity limited scenario since the tracdemand will be very high in the future, and the area is not very wide (as com-pared to Leuwidamar). Aggregating the LTE 1.8 GHz network with 5G 15 GHzwhich can provide bandwidth larger than 40 MHz, is able to provide a highercapacity and oer better performance, without any additional BS deployment.Noted that the 15 GHz link performance accounts the rain attenuation. In con-clusion, for fullling future requirement in Panimbang, we only need 8 BS (1×densication, without any additional BS) when we considering Case5 Scenario.

6.5 Comparison of Case Study and Simplied Model

To answer one of research question of this thesis: Is it important to do a casestudy? Is there any dierence if we considering general hexagonal cells ratherthan a realistic network?, we need to compare the performance of a simpliedmodel network and realistic model network.

Simplied model means a hexagonal cell, with three sectors, and has uni-formly distributed users, as illustrated in Figure 6.14. The simplied networkperformance is highly dependent to the ISD, if we want to densify the network,we can just simply decrease the ISD, and vice versa.

To have more understanding, assume we have the same user density andtrac demand as in Leuwidamar Area. We simulate the Single Carrier LTE 1.8GHz network, with 20 MHz bandwidth, and all parameter setting as in Case1 (See Table 5.1). We compare three dierent ISDs and evaluate the perfor-mances. As we can see in Figure 6.15, the downlink user throughput is better


-20 0 20 40 60 80 100 120

SINR [dB]

0

0.2

0.4

0.6

0.8

1C

DF

SINR of CA LTE 1.8 + 5G 15 GHz

SC LTE 1.8 GHz

SC 5G 15 GHz


(a) SINR of CA LTE 1.8 GHz + 5G 15 GHz

4 6 8 10 12 14 16 18 20


0

50

100

150

200

250

User

Thro

ughput [M

bps]

5th

Perc. DL User Throughput CA LTE 1.8 + 5G 15 GHz

SC LTE 1.8 GHz

SC 5G 15 GHz


(b) 5th Percentile DL User throughput CA LTE 1.8GHz + 5G 15 GHz

Figure 6.13 Case 5 Solution (a) SINR and (b) DL User Throughput Perfor-mance of Panimbang Area. CA scheme of LTE and 5G performs better com-pared to SC LTE and SC 5G network


when we decrease the ISD. To fulll our requirement (50 Mbps at 20th per-centile), the maximum ISD is 2.6 km. If we set the ISD larger than that, it willnot meet the future requirement.

Many studies consider the simplied network to be a baseline of networkanalysis. In this section, we compare the simplied model solution and realisticmodel solution, by using the same number of user density, trac demand, andrequirement.

-2000 -1000 0 1000 2000 3000

-2000

-1500

-1000

-500

0

500

1000

1500

2000

Simplified Network Layout

1

2

3

4

5 6

7

8

9

10

11

12 13

14

15

16

17

18

19 20

21

Figure 6.14 Three-sector Hexagonal Cell with Uniforlmy Distributed Users

4 6 8 10 12 14 16 18


0

50

100

150

200

250

300

Use

r T

hro

ug

hp

ut

[Mb

ps]

Impact of ISD in Simplified Model of Leuwidamar

ISD 4.5 km

ISD 1.5 km

ISD 2.6 km

Figure 6.15 Impact of ISD to DL User Throughput performance in SimpliedModel. Smaller ISD performs better compared to wider ISD

There are two main dierences when we applying simplied model over the


realistic network model. First, in a simplied model, the ISD is uniform, whilein a realistic model the ISD is varying between each BS. Second, the user dis-tribution in simplied network layout is randomly distributed in the area, whilein the realistic model, we have a dierent distribution. In Leuwidamar area,users distributed over 50 villages which sparsely located, while in Panimbang,users randomly distributed along the road/coastline, but not over all the area.In the following section, we intend to compare the user performance betweensimplied and realistic network when we have a same number of BS deployed.

Comparison of Leuwidamar Area

By applying LTE 1.8 GHz system with 20 MHz bandwidth, the simpliednetwork model is able to meet the futuristic trac demand by only having 30BS, with ISD 2.6 km. As discussed earlier, the realistic model needs 36 BS tobe deployed in order to serve the futuristic requirement. In this section, wecompare the SINR and downlink throughput of the simplied and realistic net-work for the same number of BS, 30 BS. The result can be seen in Figure 6.16.The SINR and downlink user throughput of the simplied model have a betterperformance compared to the realistic model. When we consider a uniformlydistributed user in the hexagonal-cells network, the trac load is balanced overthe area. The utilization between all BS is almost the same. While in therealistic case, the BS which located near several villages will be more utilizedcompared to those BS which surrounded by only one or two villages. In thesimplied model, the oered capacity is fairer compared to the realistic model.

It can be concluded that the BS location and user distribution aect thenetwork planning deployment. When we using a simplied model to fulllingrealistic demand in Leuwidamar, we account 30 BS will be needed, whilewhen we consider a realistic layout, we need to add 20% more.

Comparison of Panimbang Area

The same idea is applied in comparing simplied and realistic model for Pan-imbang Area. We set a same user density and trac demand in both models,with a dierent distribution of BS and user locations. In the realistic case, usersdistributed densely along the coastline, not all over the area of 132 km2. Whilein the simplied case, we consider randomly distributed users all over the area.By considering a simplied model, the futuristic trac demand in Panimbangis fullled by only having 60 BS with ISD 1.6 km. Yet in the realistic model,we need 81 BS (35% additional BS from the simplied model). To understandhow the performance of these model dier to each other, we evaluate SINR anddownlink user throughput of 60 BS network in a simplied and realistic model.The result can be seen in Figure 6.17.

As we can see in Figure 6.17, the performance of the simplied network isbetter, both in SINR and downlink user throughput. In the realistic case, all


-20 0 20 40 60 80 100

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

SINR Comparison

Simplified Model

Realistic Model

(a) SINR of Simplied and Realistic Model ofLeuwidamar

4 6 8 10 12 14 16 18


0

50

100

150

200

250

Use

r T

hro

ug

hp

ut

[Mb

ps]

Comparison of 30 BS performance

Realistic Model

Simplified Model

(b) DL User Throughput performance of Simpliedand Realistic Network layout with 30 BS

Figure 6.16 Comparison of (a) SINR performance and (b) DL User Through-put Performance in Simplied and Realistic Model of Leuwidamar Area. Withsame number of BS, Simplied model performs better since all users are uni-formly distributed within hexagonal cells. Thus there will be no users isolatedand located far away from BS that makes overall performance worse


-20 0 20 40 60 80 100

SINR [dB]

0

0.2

0.4

0.6

0.8

1

CD

F

SINR Comparison of Panimbang

Simplified Model

Realistic Model

(a) SINR of Simplied and Realistic Model of Pan-imbang

4 6 8 10 12 14 16 18 20


0

50

100

150

200

250

Use

r T

hro

ug

hp

ut

[Mb

ps]

Comparison of 60 BS Performance

Realistic Model

Simplified Model

(b) DL User Throughput performance of Simpliedand Realistic Network layout with 60 BS

Figure 6.17 Comparison of (a) SINR performance and (b) DL User Through-put Performance in Simplied and Realistic Model of Panimbang Area. Bydeploying same number of BS, the simplied model network performs better


Table 6.1 Summary of BS Number Needed

Case/Area Leuwidamar (Remote) Panimbang (Rural)

Existing BS 3 BS 8 BSSC LTE 1.8 GHz 36 BS (12×) 81 BS (10.125×)CA LTE 1.8 + 2.6 GHz 7 BS (2.3×) 12 BS (1.5×)SC 5G 15 GHz 7 BS (2.3×) 8 BS (no add)SC 5G 28 GHz 21 BS (7×) 11 BS (1.375×)CA LTE 1.8 + 5G 15 GHz 5 BS (1.6×) 8 BS (no add)

users concentrated along the coastline while in the simplied model the usersare uniformly distributed. By comparing simplied model and realistic modelin two dierent scenarios (Leuwidamar prole and Panimbang prole), we canconclude that there is a signicant dierence in deciding the number of BSneeded when we consider dierent network layout and user distribution. Hence,it is important to do a case study when we design a network for the particulararea.

6.6 Energy Performance

To support world's future goal to reduce emission, it is highly recommendedto evaluate the energy consumption for previously discussed solutions and choosethe most energy-ecient network. By using a method discussed in Chapter 3,we investigate the energy consumption of each scenario based on its daily aver-age area power consumption [kw/km2]. We utilize the DTX capability with adierent DTX capacity δ to investigate how much saving we can achieve if weable to have a certain DTX capacity value. Note that in LTE, the minimum δfor LTE System is 0.84, and the minimum δ for 5G is 0.29. A DTX Capacityδ=1 means we do not have any ability to use cell DTX technique. The compar-ison of energy consumption for Case 1 to Case 5 in Leuwidamar Area can beseen in Figure 6.18a, and for Panimbang Area is in Figure 6.18b.

In both areas, we can see that the energy consumption for Case 1 is the mostenergy-consume solution. This is due to the fact that the number of BS neededis also the highest (36 BS in Leuwidamar, and 81 BS in Panimbang, see the sum-mary in Table 6.1). The amount of energy consumption linearly increases witha higher number of BS, since there will be a signicant number of additionalequipment system that needs to be deployed. In contrast the Case 3 solution isthe least energy-consume solution, both in Leuwidamar and Panimbang area.

6.6.1 Energy Consumption in Leuwidamar Area

Although the number of BS needed for Case 2 and Case 3 is the samei.e. 7 BS, the Case 2 implementing a Carrier Aggregation scheme, and need


Energy Consumption Comparison for Leuwidamar

=1 =0.84 =0.6 =0.29

Cell DTX Capacity

0

0.5

1

1.5

2

2.5

3

3.5

4

Tota

l D

aily

Pow

er

Consum

ption [kW

/km

2]

SC LTE 1.8 GHz (20th

prc.)

CA LTE 1.8 GHz + 2.6 GHz (5th

prc.)

SC 5G 15 GHz (5th

prc.)

SC 5G 28 GHz (5th

prc.)

CA LTE 1.8 GHz + 5G 15 (5th

prc.)

(a) Energy Consumption Comparison for Leuwidamar Area tothe variety of Cell DTX Capacity

Energy Consumption Comparison for Panimbang Area

=1 =0.84 =0.6 =0.29

Cell DTX Capacity

0

2

4

6

8

10

12

Tota

l D

aily

Pow

er

Consum

ption [kW

/km

2]

SC LTE 1.8 GHz (20th

prc.)

CA LTE 1.8 GHz + 2.6 GHz (5th

prc.)

SC 5G 15 GHz (5th

prc.)

SC 5G 28 GHz (5th

prc.)

CA LTE 1.8 GHz + 5G 15 (5th

prc.)

(b) Energy Consumption Comparison for Panimbang Area tothe variety of Cell DTX Capacity

Figure 6.18 Energy Consumption Comparison of (a) Leuwidamar Area and(b) Panimbang Area to the variety of Cell DTX Capacity. LTE only able toutilize 0.84 DTX Capacity, while 5G network able to use lower DTX capacityup to 0.29


additional 45% energy consumption due to the additional equipment neededwhen generating the second system. This is also the reason why the Case 5is consumed such high energy (See Figure 6.18a), although it only need 5 BSwhich is the least number of BS needed among other solutions. Case 5 needto generate the energy for 5 BS and add 45% of total power consumption togenerate the second system. In case 4, it is the second highest energy-consumesolution since the system needs a high number of BS to serve the area. Whenwe applying Case 4, the scenario will be a coverage limited because of the worseradio propagation during the rainy day. However, if we have activated Cell DTXcapability, the Case 4 solution can have more saving compared to Case 2. Yetthe Case 3 still winning to be the most energy-ecient network among all caseswith 97% energy saving compared to Case 1.

6.6.2 Energy Consumption in Panimbang Area

In Panimbang Area, we have much higher user density and trac demand,thus it makes sense to have a high number of the average daily power consump-tion. The Case 1 needs an impressive amount of energy to be able to operate.This is because the number of BS needed in deploying Case 1 network is 81BS, which is the highest among other solutions. The second highest energy-consume solution is Case 5, although we have the same number of BS to Case3 solution, the Case 5 implement a Carrier Aggregation Scheme thus we haveadditional equipment that needs to be generated. If we compare Case 5 andCase 2 solution, we notice that the number of BS in Case 2 is higher than Case5, yet Case 5 consume higher energy. The reason behind this phenomena iswhen we generating the 5G system, we have a higher needs of energy e.g. dif-ferent baseline power, dierent transmit power (see Table 5.2). In Case 4, itis the second least energy-consume network after the Case 3. This is becausein Panimbang Area, the total area is smaller and the user density is densercompared to Leuwidamar Area thus the Case 4 do not need a high number ofBS, and not consume a high amount of power. However, the 28 GHz link stillnot very important to be deployed because of its worse radio propagation loss,especially during the rainy day. By implementing Case 3 network, it has enoughbandwidth to be oered with a medium loss of radio propagation even in a rainyday. Thus Case 3 give us the least energy-consume solution among the others.Furthermore, if we maximize the cell DTX capacity for Case 3, it can save 94%energy consumption compared to Case 1.

CHAPTER 7

Conclusion and Future Work

Chapter 7. Conclusion and Future Work 59

In this thesis, we analyze the performances of several possible network solu-tions that able to providing services envisioned by 5G: 50+ Mbps Everywhere.The number of subscribers and trac demand will have a sharp increase in theupcoming years, and providing a 50+ Mbps guarantee especially in the ruralarea can be a challenge. Thus, it is important to investigate several possiblenetwork solutions and suggest to deploy a network with the best performance.

We take a case study in practical remote and rural areas in Indonesia:Leuwidamar and Panimbang Area. Note that there is broadband network infras-tructure that already established in the area (Browneld). Referred to researchquestion in section 1.6:

1. What data that need to be considered to do a case study?

Answer: To have a realistic trac condition, we forecast a futuristictrac demand based on the current trac situation which gathered fromthree important data: current user number, population density, and areasituation. The futuristic trac demand data will be the main referenceof dimensioning a network solution. Furthermore, we also need two otherimportant data to dimension realistic network layout: BS location andtechnology specication that already deployed.

2. How will the futuristic trac demand be?

Answer: Based on realistic data in previous discussion, we can estimatefuturistic trac demand. In this thesis we forecast trac from 2017 to2023 which summed up in Table 4.1.

3. Is it important to do a case study? Is there any dierence if weconsidering general hexagonal cells rather than a realistic net-work?

Answer: Yes it is important to do a case study. In Section 6.5, we investi-gated the number of BS needed when we considering simplied model andrealistic model of LTE Single Carrier 1.8 GHz Network. The study shows asignicant dierence when we took simplied over a realistic network witha 20% and 35% dierences in the number of BS needed for Leuwidamarand Panimbang cases. Thus taking a realistic network layout is important.

4. How will LTE fulll futuristic trac demand? How much doesit cost in terms of energy consumption?


Answer: LTE system needed to be densify in order to fulll future re-quirement. The study showed that the Single Carrier LTE 1.8 GHz systemneeds a vast amount of BS in order to serve futuristic trac demand, withmore than 10 times densication in both areas. The operators will need toinvest such a high cost, and at the same time, not an environment-friendlysolution since we need a high amount of power consumption. Detail en-ergy consumption for LTE Network, see Figure 6.18.

The reason why we need such high number of BS is that the lack of capac-ity available in the system. With only 20 MHz bandwidth available, thesystem will no longer perform in the high trac demand. To add morecapacity, it can be achieved in two ways: Aggregating carrier to other fre-quency bands, or going to high frequency since there is a large bandwidthavailable for mobile broadband communication. The main challenge ofaggregating frequency to another band below 6 GHz is there would be nospace available since the frequencies in this band are fully utilized. Onthe other hand, there is also a challenge of utilizing high frequency, be-cause of the worse propagation loss. However, in this thesis we considerUE-Specic Beamforming technology to be the one of cutting-edge tech-nology that enables us to operate in high frequency. We investigated theimpact of applying UE-Specic Beamforming and it denitely improvesour user performance. Moreover, with a narrower HPBW, we can achievebetter performance e.g. with 10 dB increase of SINR for a 10o narrowerbeam. Another challenge from high frequency is the rain attenuation. Itis important to account a rain attenuation, especially in a rural area. Westudied that on some level, the high-frequency system will no longer ableto operate during the rainy day. Thus evaluate the impact of rain atten-uation and design a network to mitigate a rain attenuation is important.

5. How will 5G fulll futuristic trac demand? How much does itcost in terms of energy consumption?

Answer: Based on the benet in previous discussion, 5G network fulllfuture demand by utilizing wide bandwidth in high frequency and UE-Specic beamforming. In terms of energy, futuristic 5G network consumelower power compared to LTE because of its eciency of deploying net-work (low number of BS, see Figure 6.18 and Table 6.1). Detail discussionin the following.

We investigate four other solutions aside from SC LTE 1.8 GHz system:Case 2 is Carrier Aggregation LTE 1.8 GHz + 2.6 GHz; Case 3 is SC 5G15 GHz; Case 4 is SC 5G 28 GHz; Case 5 is CA LTE 1.8 GHz + 5G 15GHz. Result shows that CA and 5G system eectively reduce the numberof BS needed to serve the futuristic trac demand. In Leuwidamar, the


least number of BS needed is when we applying Case 5 (CA LTE 1.8 GHz+ 5G 15 GHz) with only 1.6× densication, from 3 BS to 5 BS. And inPanimbang, the least number of BS needed is when we applying Case 3(SC 15 GHz) and Case 5 (CA LTE 1.8 GHz + 5G 15 GHz) since we donot need to add more BS from existed BS (1× of densication). Case4, however, is not considered to be a good solution. When Leuwidamarapplying Case 4, it needs 7× of densication, and Panimbang needs 1.38×densication. A very high frequency has a bad radio performance, and itis not suitable for the remote and rural area. The reason why Leuwidamarneeds a high amount of BS when considering Case 4 system is that it hasa coverage constraint, i.e. it turns out to be a coverage limited scenariosince Leuwidamar is a remote area with a 176 km2 in total and has sparselydistributed users. In Panimbang, it has a smaller area with a denser user,thus a coverage is not a high constraint of dimensioning Case 4 system.

In Leuwidamar area, the number of BS needed for Case 5 is the smallest,followed by Case 3 and 4 which have a same number of BS. In Panimbang,the Case 3 and Case 5 has a same number of BS. Thus to choosing a so-lution with the best performance, we investigate the energy consumptionfor all solutions. The result shows that the Case 1 is the most energy-consume among those ve solutions, both in Leuwidamar and PanimbangAreas. And the least energy-consume solution is the Case 3 system, andit is applicable in Leuwidamar and Panimbang Areas. As we know thatthe least number of BS needed in Leuwidamar is when we apply the case5 system, yet it consumes higher energy because the system implementinga carrier aggregation scheme, and need to generates 45% more energy intotal. Same phenomena also happen in Panimbang, although the numberof BS needed for Case 3 and Case 5 is the same, yet the Case 5 consumehigher energy because of the carrier aggregation scheme.

To conclude, several solutions perform dierently in dierent areas, and asuggested solution might be dierent. However based on this thesis analysisresult, by maximizing the ability to have a UE-Specic Beamforming and awide bandwidth, it is suggested to deploy the Case 3 system network in bothLeuwidamar and Panimbang area because of its eciency of energy consumptioncompared to other four solutions.

Future Work

The motivation of comparing a number of BS needed and the energy con-sumption is to have a big picture of how much does the system will cost compareto each other. In order to have a better understanding, it is highly recommendedto analyze the economic aspect by having a cost model structure and accountsthe cost performance for the proposed solutions. The cost structure may in-clude a price of equipment upgrade and other infrastructure costs. Moreover,


In order to have more realistic solutions, considering a correlated shadow fad-ing and a practical clutter map/land use in the system-level simulator is alsorecommended. Lastly, since the future 5G network might be operating by acombination of network design, it is interesting to investigate 5G network de-ployment in the heterogeneous network in the rural area.

Bibliography

Bibliography 63

[1] 5G Vision, 5G-PPP Publication, 2015, european Commission.

[2] R. E. Hattach and J. Erfanian, NGMN 5G Initiative White Paper, NGMNAlliance, 2015.

[3] A. Anand, V. Pejovic, E. M. Belding, and D. L. Johnson, Villagecell: Costeective cellular connectivity in rural areas, in Proceedings of the FifthInternational Conference on Information and Communication Technologiesand Development, ser. ICTD '12. New York, NY, USA: ACM, 2012, pp.180189. [Online]. Available: http://doi.acm.org/10.1145/2160673.2160698

[4] T. Sandle, UN Thinks Internet Access is a Human Right, Business Insider.

[5] X. Company, Project Loon. [Online]. Available: https://x.company/loon/

[6] A. Hern, Facebook Launches Aquila Solar-Powered Drone for Internet Access. [Online]. Avail-able: https://www.theguardian.com/technology/2015/jul/31/facebook-nishes-aquila-solar-powered-internet-drone-with-span-of-a-boeing-737

[7] S. Katikala, Google Project Loon, Rivier Academic Journal, vol. 10, Fall2014.

[8] L. Chiaraviglio, N. Blefari-Melazzi, W. Liu, J. A. Gutierrez, J. V. D. Beek,R. Birke, L. Chen, F. Idzikowski, D. Kilper, J. P. Monti, and J. Wu, 5gin rural and low-income areas: Are we ready? in 2016 ITU Kaleidoscope:ICTs for a Sustainable World (ITU WT), Nov 2016, pp. 18.

[9] P. Frenger, M. Olsson, and E. Eriksson, Radio network energy perfor-mance of massive mimo beamforming systems, in 2014 IEEE 25th AnnualInternational Symposium on Personal, Indoor, and Mobile Radio Commu-nication (PIMRC), Sept 2014, pp. 12891293.

[10] A. Karlsson, O. Al-Saadeh, A. Gusarov, R. V. R. Challa, S. Tombaz, andK. W. Sung, Energy-ecient 5g deployment in rural areas, in 2016 IEEE12th International Conference on Wireless and Mobile Computing, Net-working and Communications (WiMob), Oct 2016, pp. 17.

[11] Y. Yu, Energy- and Cost-Ecient 5G Networks in Rural Area, Mas-ter's thesis, KTH Royal Institute of Technology, School of Information andCommunication Technology, Sweden, 2016.

[12] S. Tombaz, K. Won Sung, S.-w. Han, and J. Zander, An economicviability analysis on energy-saving solutions for wireless access networks,Comput. Commun., vol. 75, no. C, pp. 5061, Feb. 2016. [Online].Available: http://dx.doi.org/10.1016/j.comcom.2015.10.005

[13] The world population and the top ten countries with the highestpopulation, Internet World Stats. [Online]. Available: http://www.internetworldstats.com/stats8.htm

http://doi.acm.org/10.1145/2160673.2160698

https://x.company/loon/

https://x.company/loon/

https://www.theguardian.com/technology/2015/jul/31/facebook-finishes-aquila-solar-powered-internet-drone-with-span-of-a-boeing-737

https://www.theguardian.com/technology/2015/jul/31/facebook-finishes-aquila-solar-powered-internet-drone-with-span-of-a-boeing-737

http://dx.doi.org/10.1016/j.comcom.2015.10.005

http://www.internetworldstats.com/stats8.htm

http://www.internetworldstats.com/stats8.htm

Bibliography 64

[14] ICT Indicators Infographic, Ministry of Communications and InformationTechnology Republic of Indonesia, Research and Development Center forPost and ICT Resources Publication, Indonesia, 2016.

[15] Kepadatan penduduk menurut provinsi, 2000-2015, Badan PusatStatistik - Statistics Indonesia. [Online]. Available: https://www.bps.go.id/linkTableDinamis/view/id/842

[16] E. Stare, Wib: a new system concept for digital terrestrial television(dtt), IET Conference Proceedings, pp. 41 (10 .)41 (10 .)(1). [Online].Available: http://digital-library.theiet.org/content/conferences/10.1049/ibc.2016.0041

[17] Roadmap for the transition from analogue to digital terrestrial televisionbroadcasting in the republic of indonesia, Telecommunication Develop-ment Bureau, 2013.

[18] L. Cosseboom, This satellite rm wants to be the `budget airline' of theinternet game in rural indonesiaa, 2015.

[19] A. Håkansson, Portal of research methods and methodologies for researchprojects and degree projects, in Proceedings of the International Confer-ence on Frontiers in Education : Computer Science and Computer Engi-neering FECS'13. CSREA Press U.S.A, 2013, pp. 6773, qC 20131210.

[20] Y. Niu, Y. Li, D. Jin, L. Su, and A. V. Vasilakos, A surveyof millimeter wave (mmwave) communications for 5g: Opportunitiesand challenges, CoRR, vol. abs/1502.07228, 2015. [Online]. Available:http://arxiv.org/abs/1502.07228

[21] P. Ökvist, H. Asplund, A. Simonsson, B. Halvarsson, J. Medbo, andN. Sei, 15 ghz propagation properties assessed with 5g radio access pro-totype, in 2015 IEEE 26th Annual International Symposium on Personal,Indoor, and Mobile Radio Communications (PIMRC), Aug 2015, pp. 22202224.

[22] A. S. Mirfananda and M. Suryanegara, 5g spectrum candidates beyond6 ghz: A simulation of jakarta environment, in 2016 IEEE Region 10Symposium (TENSYMP), May 2016, pp. 3035.

[23] J. Wannstrom, Carrier aggregation explained. [Online].Available: http://www.3gpp.org/technologies/keywords-acronyms/101-carrier-aggregation-explained

[24] Z. Shen, A. Papasakellariou, J. Montojo, D. Gerstenberger, and F. Xu,Overview of 3gpp lte-advanced carrier aggregation for 4g wireless commu-nications, IEEE Communications Magazine, vol. 50, no. 2, pp. 122130,February 2012.

https://www.bps.go.id/linkTableDinamis/view/id/842

https://www.bps.go.id/linkTableDinamis/view/id/842

http://digital-library.theiet.org/content/conferences/10.1049/ibc.2016.0041

http://digital-library.theiet.org/content/conferences/10.1049/ibc.2016.0041

http://arxiv.org/abs/1502.07228

http://www.3gpp.org/technologies/keywords-acronyms/101-carrier-aggregation-explained

http://www.3gpp.org/technologies/keywords-acronyms/101-carrier-aggregation-explained

Bibliography 65

[25] V. R and K. P, Dynamic carrier aggregation in 5g network scenario, in2015 International Conference on Computing and Network Communica-tions (CoCoNet), Dec 2015, pp. 936940.

[26] P. Frenger, P. Moberg, J. Malmodin, Y. Jading, and I. Godor, Reducingenergy consumption in lte with cell dtx, in 2011 IEEE 73rd VehicularTechnology Conference (VTC Spring), May 2011, pp. 15.

[27] e. Gunther Auer, Energy eciency analysis of the reference systems, areasof improvements and target breakdown, 2012.

[28] H. Forssell, Energy eciency of heterogeneous lte networks, Master's the-sis, KTH, Communication Theory, 2015.

[29] S. Tombaz, S. w. Han, K. W. Sung, and J. Zander, Energy ecient networkdeployment with cell dtx, IEEE Communications Letters, vol. 18, no. 6,pp. 977980, June 2014.

[30] Ericsson, Ericsson Mobility Report South EastAsia and Oceania, June 2016. [Online]. Avail-able: https://www.ericsson.com/assets/local/mobility-report/documents/2016/south-east-asia-and-oceania-ericsson-mobility-report-june-2016.pdf

[31] Huawei, 5G: A Technology Vision. [Online]. Available: http://www.huawei.com/ilink/en/download/HW_314849

[32] H. D. Gadi, R. V. Murthy, R. Shanker, and V. Nagadevara, Antennaelocation methodology for a telecom operator in india, 2014.

[33] S. Tombaz, P. Frenger, F. Athley, E. Semaan, C. Tidestav, and A. Furuskar,Energy performance of 5g-nx wireless access utilizing massive beamformingand an ultra-lean system design, in 2015 IEEE Global CommunicationsConference (GLOBECOM), Dec 2015, pp. 17.

[34] E. Nurlia and I. Akbar, Autonomy and Empowerment of Local Cultureto Challenge AEC (ASEAN Economic Community) Study of Baduy Com-munity in Lebak Banten, 2016.

[35] S. Maesaroh, B. Barus, and L. S. Iman, Analysis of Coastal Areas Utiliza-tion in Pandeglang District, Banten Province, 2016.

[36] M. of Communication and I. Information, Provider yang digunakan olehpetani tahun 2015. [Online]. Available: https://statistik.kominfo.go.id/site/data?idtree=422&iddoc=1444&data-data_page=13

[37] G. R. MacCartney and T. S. Rappaport, Rural macrocell path loss modelsfor millimeter wave wireless communications, IEEE Journal on SelectedAreas in Communications, vol. PP, no. 99, pp. 11, 2017.

[38] A. White and G. Liu, NGMN Radio Access Performance EvaluationMethodology, 2009.

https://www.ericsson.com/assets/local/mobility-report/documents/2016/south-east-asia-and-oceania-ericsson-mobility-report-june-2016.pdf

https://www.ericsson.com/assets/local/mobility-report/documents/2016/south-east-asia-and-oceania-ericsson-mobility-report-june-2016.pdf

http://www.huawei.com/ilink/en/download/HW_314849

http://www.huawei.com/ilink/en/download/HW_314849

https://statistik.kominfo.go.id/site/data?idtree=422&iddoc=1444&data-data_page=13

https://statistik.kominfo.go.id/site/data?idtree=422&iddoc=1444&data-data_page=13

Bibliography 66

[39] ITU-R, Technical feasibility of imt in bands above 6 ghz. recommendationitu-r m.2376-0, 2015.

[40] N. Instruments, mmWave: The Battle of the Bands. [Online]. Available:http://www.ni.com/white-paper/53096/en/

[41] C. Tidestav, Massive beamforming in 5G radio ac-cess. [Online]. Available: https://www.ericsson.com/research-blog/5g/massive-beamforming-in-5g-radio-access/

http://www.ni.com/white-paper/53096/en/

https://www.ericsson.com/research-blog/5g/massive-beamforming-in-5g-radio-access/

https://www.ericsson.com/research-blog/5g/massive-beamforming-in-5g-radio-access/

TRITA -ICT-EX-2017:65

www.kth.se

analysis of 5g mobile broadband solutions in rural and ...1165918/fulltext01.pdf · analysis of 5g...

Documents