Master of Science Thesis performed at the Radio Systems Lab,Communication Systems Department School of EECS, KTH

Improving Residential In-building EnergyPerformance for Multioperator and

Multi-standard Radio Access in DistributedAntenna Systems (DAS)

Alberto Kawahara (aaky@kth.se)Internal Advisor : Mats Nilson

External Advisor : Tord Sjolund and Kuldeep PareekExaminer : Ben Slimane

July 13, 2018

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Contents

Abstract v

Acknowledgment vii

List of Figures vii

List of Tables x

Acronyms and Abbreviations xiii

Chapter 1 Introduction 11.1 Background and Previous Work . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.5 Environmental Impact in the Society . . . . . . . . . . . . . . . . . . . . 5

Chapter 2 Technology Background 72.1 Mobile Technologies Background Applied for Indoors . . . . . . . . . . . 7

2.1.1 Global System for Mobile communications (GSM) . . . . . . . . . 72.1.2 Wideband Code Division Multiple Access (WCDMA) and High-

Speed Packet Access (HSPA) . . . . . . . . . . . . . . . . . . . . 82.1.3 Long Term Evolution (LTE) . . . . . . . . . . . . . . . . . . . . . 10

2.2 Distributed Antenna System (DAS) . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Base Station Solution (Passive DAS) . . . . . . . . . . . . . . . . . 112.2.2 Fiber DAS Solution (Active DAS) . . . . . . . . . . . . . . . . . . 142.2.3 Hybrid Active DAS Solutions . . . . . . . . . . . . . . . . . . . . 162.2.4 General Considerations in a DAS RF Design . . . . . . . . . . . . 17

Chapter 3 Theory 213.1 Thermal Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2 Indoor Propagation Models . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.1 Free Space Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.2 Path Loss Slope (PLS) Model . . . . . . . . . . . . . . . . . . . . 22

3.3 Residential Users Traffic Modeling . . . . . . . . . . . . . . . . . . . . . . 23

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3.3.1 User profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.3.1.1 Voice profile per user . . . . . . . . . . . . . . . . . . . . 243.3.1.2 Data Profile per User . . . . . . . . . . . . . . . . . . . . 24

3.3.2 2G Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.3 3G Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.4 4G Capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.5 Additional Considerations . . . . . . . . . . . . . . . . . . . . . . 28

3.4 Macro Base Station Power Consumption Models . . . . . . . . . . . . . . 293.4.1 WCDMA Base Station Model . . . . . . . . . . . . . . . . . . . . 293.4.2 LTE Base Station Model . . . . . . . . . . . . . . . . . . . . . . . 29

Chapter 4 Measurement Strategy 314.1 Measurement Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.1 Spectrum Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.1.2 Energy Monitor Device . . . . . . . . . . . . . . . . . . . . . . . . 32

4.2 WCDMA Power Measurement . . . . . . . . . . . . . . . . . . . . . . . . 334.3 Case Study: Norra Tornen . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Chapter 5 Simulation 375.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.2 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375.3 Simulation Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 6 Results 396.1 WCDMA Power Consumption Measurements Variation at Different Loads 39

6.1.1 Master Unit Power Consumption at Different Loads . . . . . . . . 406.1.2 Remote Unit Power Consumption at Different Loads . . . . . . . . 41

6.2 Power Consumption Measured at Different Number of Transmitted Fre-quencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

6.3 Capacity Dimensioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446.3.1 LTE Capacity Dimensioning . . . . . . . . . . . . . . . . . . . . . 446.3.2 WCDMA Capacity Dimensioning . . . . . . . . . . . . . . . . . . 46

6.4 Power Consumption Comparison between a Passive and Active Solution . 476.4.1 Passive DAS Total Power Consumption . . . . . . . . . . . . . . . 476.4.2 Active DAS Total Power Consumption for 1 BS with 1 sector per

operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486.4.3 Active DAS Total Power Consumption for 2 BS with 1 sector per

operator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496.4.4 Passive DAS vs. Active DAs Power Consumption Summary . . . 49

6.5 CO2 Emissions Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 49

Chapter 7 Conclusions and Future Work 51

Abstract

Good signal in indoor environments has always been one of the mobile operator’s mainchallenges. The situation gets even more complex when dealing with new energy-efficientbuildings that insulate the heat inside the building but at the same time causes higherlosses from the macro base stations. In this scenario, indoor solutions are required toovercome this problem. Nowadays, there are two main indoor solutions: DistributedAntenna Systems (DAS) and small cells.

This thesis focuses on DAS solutions and investigates the power consumption differencebetween the two main architectures: Passive and Active/Hybrid DAS. The evaluation ismade by measuring the power consumption of the active components and adding themto the already existing Base Station power consumptions models. Power consumptionmeasurements were performed for four commercial bands: 900, 1800, 2100, 2600 MHz.Power consumption and system capacity trade-off between the passive and active DASsolutions is also presented. The capacity analysis is focused on LTE and applied to areal case study: Norra Tornen residential building. Final results show that up to 75% ofthe indoor power consumption can be saved when implementing an active DAS solutionwithout affecting the service quality.

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Acknowledgment

I would like to express my special thanks of gratitude to my supervisors Mats Nilson(KTH), Tord Sjolund (MIC Nordic) and Ben Slimane (KTH) for their continuous supportthroughout the thesis. I am also grateful to Leif Eriksson for his guidance by sharing hisexperiences and knowledge, and to Kuldeep Pareek for his support.

I would like to extend my gratitude to the whole MIC Nordic team who was alwayswilling to help me on any required task and for creating a wonderful environment. Finally,I would like to mention my family, and especially my parents who have always supportedme in every new challenge.

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List of Figures

1.1 Small Traditional Indoor Passive DAS System . . . . . . . . . . . . . . . 31.2 Passive DAS Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Passive DAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.4 Hybrid DAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Hybrid DAS example from Mic Nordic . . . . . . . . . . . . . . . . . . . 16

3.1 PLS at different distances for a PLS constant of 38.1, adapted from [11] . 233.2 LTE Traffic Distribution (mE/user) during the busy-hour, adapted from [12] 253.3 WCDMA Macro Power Consumption, from [6] . . . . . . . . . . . . . . . 293.4 Matlab Simulation for WCDMA Macro Power Consumption, adapted from

[6] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5 LTE Macro Power Consumption, from [9] . . . . . . . . . . . . . . . . . . 303.6 Matlab Simulation for LTE Macro Power Consumption, adapted from [9] 30

4.1 WCDMA in Spectrum Analyzer . . . . . . . . . . . . . . . . . . . . . . . . 314.2 LTE in Spectrum Analyzer . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3 Spectrum Analyzer Remote Control Program . . . . . . . . . . . . . . . 324.4 SCPI commands for Spectrum Analyzer Remote Control . . . . . . . . . 324.5 Energy Sensor and Transmitter . . . . . . . . . . . . . . . . . . . . . . . 334.6 Engage Sensor (In charge of receiving the data from the Transmitter and

sending it to the cloud) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.7 WCDMA Power Consumption Measurement Setup . . . . . . . . . . . . 344.8 2.1 GHz Band Measurement. The expected WCDMA spectrums are not vis-

ible due to 3 MHz RBW, a resolution of 30 KHz was required to distinguishthe 4 different carriers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.9 Norra Tornen Base Stations Distribution . . . . . . . . . . . . . . . . . . 36

5.1 Norra Tornen 2nd Floor RSRP Simulation . . . . . . . . . . . . . . . . . 385.2 Norra Tornen 2nd Floor SNR Simulation . . . . . . . . . . . . . . . . . . 38

6.1 Master Unit Power Consumption at different WCDMA System Load . . 406.2 Remote Unit Power Consumption at different WCDMA System Load . . . 416.3 Power Consumption at Different Number of Transmitted Frequencies . . 426.4 Average Active DAS Power Consumption per Number of Tx Frequencies 436.5 SNR Mapping with Throughput per Resource Block . . . . . . . . . . . . 44

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6.6 Estimated Number of Supported Users per Sector for different LTE UserProfiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

6.7 Passive DAS Total Power Consumption . . . . . . . . . . . . . . . . . . . 486.8 Active DAS Total Power Consumption for 1 sector per operator . . . . . 486.9 Active DAS Total Power Consumption for 2 sector per operator . . . . . 486.10 DAS Power Consumption Summary . . . . . . . . . . . . . . . . . . . . . 496.11 CO2 emission from electricity generation (g/kWh), adapted from [14] . . 506.12 CO2 emission per DAS architecture (CO2 g/kWh) . . . . . . . . . . . . 506.13 CO2 emission table per DAS architecture (CO2 g/kWh) . . . . . . . . . 50

List of Tables

2.1 Resource Blocks at different LTE Bandwidths from Eq.(2.1) . . . . . . . 102.2 LTE RSRP Requirements per Location Type, adapted from [11] . . . . . . 112.3 Coaxial Cable Typical Attenuation, adapted from [11] . . . . . . . . . . . 122.4 Tappers Typical Losses, adapted from [11] . . . . . . . . . . . . . . . . . 132.5 RF Design Levels per Technology at Different Environments, adapted from

[11] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3.1 Free Space Loss at 1 meter (rounded), adapted from [11] . . . . . . . . . 223.2 PLS Constants, adapted from [11] . . . . . . . . . . . . . . . . . . . . . . 233.3 Typical Voice Load in Erlang, adapted from [11] . . . . . . . . . . . . . . 243.4 LTE User Profiles at Busy-Hour, adapted from [12] . . . . . . . . . . . . 243.5 Erlang B Table, adapted from [11] . . . . . . . . . . . . . . . . . . . . . . 263.6 SINR Mapping, adapted from [12] and [15] . . . . . . . . . . . . . . . . . 27

5.1 LTE RSRP and SNR Matlab Simulation Parameters . . . . . . . . . . . 37

6.1 Active DAS Average Consumed Power per Number of Transmitted Frequencies 436.2 LTE capacity Dimensioning Parameters . . . . . . . . . . . . . . . . . . . 456.3 WCDMA Voice Capacity per Sector . . . . . . . . . . . . . . . . . . . . . 47

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Acronyms and Abbreviations

ACIR Adjacent Channel Interference Ratio

BB Base Band

BS Base Station

BDA Bidirectional Amplifier

BER Bit Error Rate

CPICH Common Pilot Channel

CO Cooling

DC Direct Current

DAS Distributed Antenna Systems

EMR Electromagnetic Radiation

EDGE Enhanced Data GSM environment

EU Expansion Unit

FDD Frequency Division Duplex

GSM Global System for Mobile Communications

GoS Grade of Service

GHG Greenhouse Gases

HO Handover

HSPA High-Speed Packet Access

HRU Hybrid Remote Unit

LTE Long Term Evolution

MS Main Supply

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MU Master Unit

MMF Multimode Fiber

NOC Network and Operating Centers

NF Noise Figure

ORU Optical Remote Unit

OFDM Orthogonal Frequency Division Multiplexing

PIM Passive Intermodulation

PLS Path Loss Slope

PBR Physical Resource Block

PA Power Amplifier

RF Radio Frequency

RSSI Received Signal Strength Indicator

RSRP Reference Signal Received Power

RSRQ reference Signal Received Quality

RU Remote Unit

SNR Signal-To-Noise

SMF Single Mode Fiber

SCPI Standard Commands for Programmable Instruments

TDD Time Division Duplex

TA Timing Advance

UL Up-link

WCDMA Wide-band Code Division Multiple Access

VISA Virtual Instrument Software Architecture

Chapter 1

Introduction

Within ICT technologies, Nokia estimates that there is a yearly increase of 10-30% in themobile network energy consumption, where nearly 80% comes from mobile base stations(BS) [1]. This energy is generated from different sources, going from harmful environmentaltechnologies such as coal-based thermal power plants, up to more climate-friendly sourceslike solar and wind power generators. In order to deal with the greenhouse gas emissionsmitigation, the Paris Agreement was signed between 196 parties in 2015. In this agreement,ICT technologies carbon emission targets for 2030 have been defined, and the EU hascommitted to 40% reduction in greenhouse emissions, 27% renewable energy usage and27% improvement in energy efficiency [2]. This target can only be achieved by engagingall ICT technology vendors and consumers into new best practices that contribute to theoverall energy consumption cut down.

To a great extent, there are two types of BS: macro and micro BS. On the one hand,macro BS are placed outdoors and normally located at high elevations to increase theircoverage range to multiple users. On the other hand, micro BS are placed indoors andare implemented to overcome coverage holes (i.e., places without macro BS signal) orenhance the voice/data service. Nowadays, nearly 80% of the traffic comes from theseindoor environments (e.g., malls, residences, offices, etc.) where micro BS are requiredand generally implemented with Distributed Antenna System (DAS) solutions [3].

The massive deployment of indoor solutions to overcome the expected sevenfold mobiledata traffic increase between 2016 and 2021 is a great challenge for mobile operators [4].The design of indoor solutions should be in accordance with the 2030 Paris Agreementtargets by implementing energy efficient indoor solutions that satisfy the traffic demandswhile using a proper amount of energy resources.

1.1 Background and Previous Work

In recent years there has been a boom in energy-efficient buildings, characterized by theirgood heat insulation. Nevertheless, this insulation also attenuates mobile signals fromthe outside, which dramatically affects the mobile indoor coverage. The spread of thesedesigns has increased the requirements for residential indoor solutions that, in contrast

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to commercial deployments, require less capacity due to their lower density of users. Inthis way, a trade-off between capacity, energy efficiency and quality of service can beintroduced to achieve more sustainable buildings.

In general, it is hard to find published results where DAS systems are included in thecalculations. Previous work for power consumption models has mainly been focused onmacro base stations. In [5], Forster presents a generic power consumption calculationfor GSM and WCDMA at maximum load with a fixed power amplifier efficiency pertechnology.

A different approach is taken by Lorincz in [6] by considering different voice loads.This paper focused on corroborating the instantaneous power consumption variation withthe traffic load (in Erlangs) by correlating them through a linear model for three macrobase station: GSM 900 MHz, GSM 1800 MHz and UMTS 2100 MHz. The results aretaken from real data in an indoor scenario.

Further elaborated model is presented by Jung in [7] for three different types ofBS: Macro-cell, Micro-cell and Remote Radio Head. The total power consumptionis disaggregated into six different components: climate control, power supply, signalprocessing, power amplifier, feeder loss and transmission power. Each component isfurther elaborated, and the relation between them is presented. The complexity ofthis model allows a better understanding of all the parameters involved in the powerconsumption, but it is not applicable to a more generic model due to the large number ofvariables, of which some of them are not specified by the equipment vendor.

In the case of LTE, a simple model is presented by Huawei in [8]. It splits the totalpower consumption into three parts: fixed, dynamic and backhaul power. The static poweris fixed and comes from the power amplifier, baseband transceiver units, feeder networkand cooling system; while the dynamic power depends on the percentage of PhysicalResource Blocks (PRB) used in the system. Finally, the backhaul power is calculatedfrom the microwave link power consumption and the rectifier loss.

A different approach is introduced in Auer’s papers [6] and [9], that presents the totalpower consumption for different base stations sizes and at different system loads. Thispapers does not take into account the number of PRB and emphasizes the influence ofpower amplifier in the macro BS power consumption and how this influence diminishes asthe BS becomes smaller.

Another LTE model with greater detail is presented by Desset in [10]. It follows asimilar approach as Auer’s, by disaggregating the total power into baseband, RF, poweramplifier and overhead; but investigates more in-depth each BS subcomponent while alsodefining how the CMOS generation influences in the dynamic and leakage power.

Regarding system capacity evaluation, a simple voice capacity calculation for 2G ispresented by Tolstrup in [11], where he makes a direct relationship between the totalErlang capacity and the number of RF transmitters. A 3G/4G data capacity evaluationis presented by Aragon-Zavalas in [12], where the total number of sectors for the indoorsolution is calculated by dividing the total required data traffic (depending on the user’sprofile) with the throughput offered by each sector (depending on the RF indoor coverage).

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1.2 Problem Formulation

Power consumption models are mainly based on either macro BS or small cells indoorsolutions without focusing on DAS system solutions. The goal of this thesis is to setup aresidential indoor DAS power consumption system and measure its power consumption atdifferent frequencies bands. It is important to mention that the definition of DAS systemsin this work refers to both, the base stations and the passive/active components requiredto offer the indoor solution.

A traditional small indoor passive DAS system is shown in Figure 1.1. As it can beseen, the RF signal from the base station (that can also be a repeater from a macro BS),is distributed among all the antennas located in different points and at strategic locations.The BS power and the passive equipment values are set in order to have a certain RFoutput power at the antennas. The total power consumption of the system comes justfrom the base station, as all the other devices are passive. The main limitation of a fullpassive DAS solutions comes in bigger buildings like the one shown in Figure 1.2 wherethe antennas located at the upper floors do not receive the required RF power even whenhaving the BS at maximum power.

Figure 1.1: Small Traditional Indoor Pas-sive DAS System Figure 1.2: Passive DAS Limitation

To overcome this problem, there are two traditional indoor DAS solutions: Passive andHybrid DAS. A traditional passive DAS is shown in Figure 1.3. It basically uses additionalbase stations to extend the RF coverage in the building. In this way, the RF signalreaches the upper floors and increases the system capacity at the cost of doubling thepower consumption. The other alternative is to use the Hybrid DAS solution representedin Figure 1.4. In this solution, just one BS is used and active DAS components areintroduced to extend the coverage. As the number of base stations remains in one, thecapacity is still the same and the total power consumption comes from the BS and allthe active DAS components. This thesis work will analyze both scenarios in terms ofpower consumption and capacity and will use the outcomes to evaluate an energy efficientresidential building in Sweden.

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Figure 1.3: Passive DAS Figure 1.4: Hybrid DAS

1.3 Methodology

The thesis focuses on making an energy consumption evaluation between two main indoorsolutions: Passive and Hybrid Distributed Antenna Systems (DAS). This evaluation willinclude two different technologies: WCDMA and LTE at four different frequencies: 900MHz, 1800 MHz, 2100 MHz, and 2600 MHz. The macro BS energy consumption modelsare used as a baseline, and the DAS system power consumption is calculated based onreal measurements.

Once the DAS power values have been obtained, they are used to evaluate a realresidential building. The RF propagation is simulated in Matlab by following the Path LossSlope Model. Additionally, the total capacity of the system is estimated, and an evaluationis made to verify if it fulfills the average residential users profile requirements. Finally,a CO2 emission evaluation is presented from the kWh required during the equipmentoperational time. This calculation is based on the Swedish power sector scenario.

The following steps were followed during the thesis:

• Literature review for macro base stations power models and system capacity estima-tion.

• Definition of a case study based on a ”state of art” status for a high rise residentialbuilding under construction in Stockholm (Norra Tornen, Oscar Properties).

• Active DAS power consumption measurements for four different bands: 900, 1800,2100 and 2600 MHz, and for two technologies: WCDMA/LTE. Real measurementwas performed by placing power consumption sensors in the active equipment.WCDMA/LTE power consumption was also evaluated under different systems load.The system load was obtained by creating a C# script to collect the data from thespectrum analyzer periodically.

• LTE capacity analysis and comparison between the passive and active DAS scenario.A Matlab radio-frequency propagation simulation was required to calculate the totalthroughput of the system and, subsequently, estimate the maximum number of usersthat can be supported at busy-hour and with a certain user profile.

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1.4 Limitations

Some limitations of this work are:

• The overall power consumption of both DAS system uses Base Stations powerconsumption models instead of real measurements as the access to measure the BSpower consumption was restricted.

• The RF propagation for the case study building is based on a Matlab simulationand not on real measurements as the building is currently under construction.

1.5 Environmental Impact in the Society

The environmental impact can be measured from the Greenhouse Gases (GHG) producedfor generating electricity. The most commonly measured GHG are: Carbon dioxide,Methane, Nitrous oxide and Fluorinated gases. From all of them, Carbon dioxide repre-sented 81% of the total emission [13]; thus, it will be the one evaluated on this work.

It is important to highlight the big difference in terms of CO2 grams generated perelectricity production (g/kWh) between countries. For example, in 2013, Iceland has beenthe country producing the cleanest energy, reaching 0.1741 grams of CO2 per kWh; whilethe least ”green” country was Estonia reaching 1016.1824 grams of CO2 per kWh. Thismeans that Estonia generates 5836 times more CO2 gases than Iceland for producing thesame amount of electricity [14]. For this reason, the country where the energy is generatedis also a relevant factor when calculating the overall GHG emissions reduction.

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Chapter 2

Technology Background

2.1 Mobile Technologies BackgroundApplied for Indoors

The information in this section is focused on indoor solutions and describe the generalconcepts required when implementing and analyzing a DAS solution.

2.1.1 Global System for Mobile communications (GSM)

GSM technology, also known as 2G, was launched in the early 90s. It is basically basedon two techniques to improve the spectral efficiency of the system: Frequency DivisionDuplex (FDD) and Time Division Duplex (TDD). On one side, FDD divides all the GSMspectrum into 200 kHz channels and, on the other side, TDD divides each 200 kHz bandinto eight different time slots, so that more than one user can access the same channel.Therefore, the total capacity of the system is determined by the total number of radiochannels (i.e., number of 200 kHz channels multiplied by the 8 time slots per channel). Itis important to notice that each of these channels can carry either user’s traffic (voiceor data) or signalling data from the network used for different purposes like mobilitymanagement, traffic control, paging, etc. [11].

When making an indoor design for a GSM solution, the following parameters have tobe taken into account during the Radio Frequency (RF) design [11]:

• Rx-Quality: It is defined as the link radio quality (for uplink and downlink) and isassociated with the Bit Error Rate (BER). It ranges from 0 to 7, where the bestquality is attained at 0. The indoor design should aim to provide an Rx-Qual higherthan 3 in all the solution to avoid quality degradation. There are two main reasonsfor low Rx-Qual: high interference from another cell using the same channel andlow signal level at the receptor (e.g., the closest transmitter is located very far fromthe receptor, or there is an obstacle with high attenuation between them).

• Rx-Level: It measures the Radio Signal Strength with respect to -110 dBm, and itgoes from 0 to 64. In this way, 0 represents a received signal of -110 dBm, whereas

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64 represents −110 + 64 = −46 dBm. A good Rx-Level does not guarantee a goodRx-Quality as there can be another transmitter nearby using the same channel,causing a huge interference.

Additionally, some considerations should be taken into account from the operationaland maintenance perspective [11]:

• When planning 2G voice, it is required a margin of 2 to 10 dB margin betweenco-channel. For 2G Data (EDGE), channel isolation of more than 17 dB is needed.

• Normally a ”two-way” neighbor list is defined between the indoor and outdoor cell(i.e., a handover can be done in both ways). Nevertheless, some indoor deploymentsrequire a ”one-way” neighbor, meaning that the handover only happens in onedirection (from the outdoor to the indoor cell). This configuration will assurethat the UE will stay connected to the indoor solution and to avoid the so-calledping-pong effect (i.e., making many unnecessary handovers between the indoor andmacro cell). This situation usually happens on high floors, where the UE receivesmany signals from different macros.

• Handover considerations: Usually, handovers are configured to be triggered when theRx-Qual goes below 3, and there is another cell offering better quality. Nevertheless,some additional triggers should also be considered: distance to the cell trigger,mobile moving speed trigger, traffic trigger (to offload the cell) and maintenancetrigger (to force users to empty the cell for maintenance).

• Timing Advance (TA) offset: As explained in the previous item, one way to ensurethat UEs will stay connected to the indoor solution is by setting a maximum distanceto the cell value. This configuration is done by a parameter called Timing Advance(TA) offset that goes from 0 to 63 with a 550 meters resolution, where 0 representsa maximum distance up to 550 m., and 63 up to 34650 m. This parameter is basedon the TA value that synchronizes the base station with the UEs to avoid channeloverlapping, and from which the distance can be calculated indirectly from the delay.However, this parameter is not directly applicable to indoor solutions as it does nottake into account the delay introduced by the cables and active systems. A propertiming offset has to be calculated and verified by a mobile field test.

2.1.2 Wideband Code Division Multiple Access (WCDMA) andHigh-Speed Packet Access (HSPA)

The main difference with GSM is that WCDMA uses the entire 5 MHz spectrum andassigns a unique code sequence to each user for identification. By using the whole 5 Mhzspectrum, narrow-band interference is minimized as the whole band is used. However, thetrade-off, of increasing the spectral efficiency by using the whole bandwidth, is that thereis always interference between all base stations.

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The concept of soft-handover is introduced to overcome the interference problem. Soft-handover enables the UE to connect to a certain number of neighbor cells simultaneouslywhen they have similar signal levels, thus reducing the interference. Nevertheless, thenumber of multiple connections is limited (defined by the mobile operator and usuallyset to 3) and all the other cells will be considered noise. Furthermore, when the UE isconnected to multiple cells, there is additional signaling over the network as each celllink requires extra resources. For this reason, careful RF planning is required on indoorsolutions regarding overlapping regions between cells, either indoor or outdoors.

As years passed, data demand increased, and an upgrade of the WCDMA technologywas introduced with the name of High-Speed Packet Access (HSPA). This technologyachieved higher spectral efficiency regarding data throughput by introducing highermodulation orders and additional features. It is divided in HSDPA for downlink andHSUPA for uplink. Further detail regarding WCDMA/HSPA technologies is in [11].

Regarding RF, the following parameters should be taken into account [11]:

• Common Pilot Channel (CPICH): It is on the DL channel, and its main purposeis to broadcast the cell so that UEs can perform cell selection evaluation. In thisway, the UE will connect to the strongest CPICH and will use this informationwhen performing a handover decision. The CPICH Power is normally 10% of thetransmission power of the BS.

• Ec/Io: It is basically the comparison between the serving cell CPICH with allthe CPICH from all the other cells, measured in energy per chip over interference.Sufficient quality of Ec/Io is between -10 to -15 dB.

• Pilot Pollution: It happens when there is no clear dominance of one serving cell.Pilot pollution happens when there are overshooting cells (i.e., cells with good signallevels in areas outside their designed coverage range) that are not defined in theneighbor list of the serving cell. These cells will act as pure interference as theydo not allow soft handover. However, it is also not recommendable to add them tothe neighbor list as normally these cells are located far away and will increase theuplink noise interference as UE will require high UL transmission power.

There are some Operational and Maintenance considerations for WCDMA/HSPAdeployments [11]:

• In case of external interference from the macro BS, a margin of 10 to 15 dB has tobe considered to avoid unnecessary soft handovers. An indoor signal dominance isdesired.

• In indoor scenarios, an Ec/Io between -6 to -8 dB is desired as long as there is goodisolation from the macro BS.

• External Equipment impact in the Noise Power: When implementing a DAS system,each equipment (e.g., repeater or active component) will generate additional noisepower in the Uplink (UL) which will require an update in the base station noise

10

reference level for no traffic. Increasing the spectral efficiency allows the transmissionof more data by using the same portion of spectrum.

2.1.3 Long Term Evolution (LTE)

LTE technology was introduced to satisfy the increasing demands on data consumption.It increases three to four times the downlink spectral efficiency of HSDPA and two tothree times the efficiency of HSUPA. It also allows dynamic use of the frequency spectrumby allowing adaptable bandwidth of 1.4, 3, 5, 10, 15 and 20 MHz. Additionally, it canoperate in two different modes: Frequency Division Duplex (FDD) and Time DivisionDuplex (TDD). FDD requires a fixed frequency allocation for the DL and UL while TDDuses the whole spectrum and offers a dynamic resource allocation. Nonetheless, TDDrequires high synchronization between UL/DL and between different cells which is not aconcern for FDD scenarios.

In LTE, each user has assigned at least two to more Resource Blocks (RBs) with acertain modulation scheme that provides a certain throughput for the required service.Each resource block is composed of 12 sub-carriers which are shared among different users,and the total transmitted power is also split among them. The power per sub-carrier iscalled RSRP, and its calculation is shown in Eq.(2.1). It is important to mention that LTErelies on Orthogonal Frequency Division Multiplexing (OFDM) which is a modulationtechnique that allows perfect isolation between sub-carriers and the break down of usersdata into different transmission streams that are transmitted at different sub-carriers.

PtxRSRP =Ptx

# sub-carriers=

Ptx

# resource-blocks × # sub-carriers per RB(2.1)

Where Ptx represents the transmitted power, the # of sub-carriers depends on thenumber of Resource Blocks (RBs) which is associated with the LTE bandwidth (each RBhas 180KHz, and its calculation is the division of the total BW by 200K) and the # ofsub-carriers per RB is 12. A resume for different LTE BW is shown in Table 2.1.

Further information regarding LTE is in [11].

Table 2.1: Resource Blocks at different LTE Bandwidths from Eq.(2.1)

LTE BW(MHz)

# RBs # Sub-carriersPower Drop per

sub-carrierfrom the total Tx Power (dB)

1.4 6 72 18.63 15 180 22.65 25 300 24.810 50 600 27.815 75 900 29.520 100 1200 30.8

11

Table 2.2: LTE RSRP Requirements per Location Type, adapted from [11]

LocationRSRP (dBm)Requirement

Basement and low-interference areas -100 to -95Office and high-use environment, with limited interference -90High rises, high-use critical areas with some interference -85High-interference areas more than -85VIP areas -75 to -70

The following RF parameters have to be taken into account for LTE [11]:

• Reference Signal Received Power (RSRP): Which is the LTE parameter for receivedpower and it is the linear-averaged received signal over six reference signals in eachresource block. It is used as a reference when selecting the cell with the strongestsignal.

• Received Signal Strength Indicator (RSSI): Is the sum of the power of all active sub-carriers. In LTE, all the sub-carriers that are not used, do not transmit; therefore,there is a decrease in the overall interference. In this way, RSSI will increase as theLTE system load increases as well.

• Reference Signal Received Quality (RSRQ): It is the relation between the RSRP,RSSI and the number of RBs.

As in previous technologies there are some Operation and Maintenance considerationsthat have to be considered in LTE [11]:

• LTE RSRP targets are shown in Table 2.2. The RSRP power should not exceed -25dBm to avoid saturation at the receivers.

• As 4G has a frequency reuse of 1 (i.e., all the cells share the same frequency whichcauses interference between each other), the dominant cell (i.e.,, the one with thestrongest signal that will potentially serve the UE) has to be between 10 to 15 dBstronger than all the other cells. A careful consideration has to be taken into accountin the so called ”transition areas” where there are two or more strong signals. Thosescenarios will have a limited capacity load.

2.2 Distributed Antenna System (DAS)

2.2.1 Base Station Solution (Passive DAS)

A passive DAS is shown in Figure 1.3 and is basically a BS connected to a system ofpassive components with the function of distributing its coverage among the building.

12

Table 2.3: Coaxial Cable Typical Attenuation, adapted from [11]

Coaxial Cable Type Typical Loss per 100 m (dB)

Cable

Diameter (inch)

Cable

Diameter (cm)900 MHz 1800 MHz 2100 MHz

14

0.64 13 19 20

12

1.27 7 10 11

78

2.22 4 6 6.5

114

3.18 3 4.4 4.6

158

4.13 2.4 3.7 3.8

The design strategy is mainly focused on having the same output at each indoor antennathat will satisfy the RF transmission requirements of the RF design.

The passive DAS is implemented by using passive components that are also used inactive DAS solution [11]:

• Coaxial Cable: The typical sizes are shown in Table 2.3, and the total cable losscalculation is described in Eq.(2.2). The trade-off is between cable losses andinstallation costs. The thicker the cable, the less attenuation per meter but higherinstallation cost and vice-versa.

Total Loss = Distance (m) × Attenuation per meter (2.2)

• Splitters: Used to evenly split the power of a coaxial cable into two or more outputs.It is important to consider the insertion loss that is typically around 0.1 dB. In thisway, the splitter attenuation is shown in Eq.(2.3)

Splitter Loss = 10 log(# Ports) + Insertion Loss (2.3)

• Tappers: Also known as uneven splitters, and the only difference is the unevenlysplit power. Tappers are mainly used in the ”vertical” thick cable that goes betweenfloors where the lower loss is going through the different floors, and the higher lossoutput goes to the thinner cable installed on each floor. Typical tapper losses areshown in Table 2.4.

• Attenuators: Design to attenuate the signal with a certain value. Typical fixedvalues are: 1, 2, 3, 6, 10, 12, 18, 20, 30 and 40 dB and variable attenuators can befound for low power signals. Their main application is to bring the signal to thedesigned operation power.

13

Table 2.4: Tappers Typical Losses, adapted from [11]

Tapper Type Loss Port 1-2 (dB) Loss Port 1-3 (dB)1/7 1 7

0.5/10 0.5 10.50.1/15 0.1 15.1

Variable 0.1-1.2 6-15

• Circulators: As its name suggests, is a circulator splitter that has low attenuation inone direction and high attenuation in the opposite. The low attenuation is typically0.5 dB and the high attenuation 23 dB but can go up to 40 dB when needed. Thereare used in multi-operator systems to isolate each vendor’s transmitters.

• 3dB Coupler: Similar use to the circulator with the difference that it has 2 inputsand 2 outputs. For that reason is used when combining two signal from differentsources.

• Filters: There are two types: duplexers and diplexers/triplexers. Duplexers, sep-arate a combined Tx/Rx signal into two separate outputs. On the other side,diplexers/triplexers separate or combine signals at different bands; and, as it can bedistinguished from their names, the diplexer manages 2 frequencies and the triplexerup to 3.

Main advantages of a Passive DAS [11]:

• Designs can be done easily. Just the desired output power at the indoor antennashas to be taken into account.

• Both cables and components are reliable and resistant.

• Compatibility between different manufacturers.

Main problems in the Passive DAS are [11]:

• Higher losses at the antennas due to the cable attenuation from the BS. Thishas an impact in current an future mobile technologies, as the higher modulationschemes (for higher throughput) are more sensitive to cable losses in the downlink.Additionally, there is a degradation in the uplink due to the noise figure increase.

• Not flexible for updates as it will require a complete redesign of the solution.

A Passive DAS solution planning is based on [11]:

• Calculation of the maximum loss in the system and verify is the power in the antennawill satisfy the RF requirements for each technology.

• When the attenuation of the system gets too high, the solution is to split the DASsystem into different sections that are served by different BSs. The main problem isthe high cost of the BS.

14

The following Operational and Maintenance considerations have to be taken intoaccount in an indoor passive design [11]:

• The RF signal propagation speed at a passive coaxial cable is 88% of its normalradio propagation speed (i.e., approximately 300 000 km/s). As an example, a 100meter cable will introduce a propagation delay equivalent to 100/0.88 = 114 metersin the air.

• Verify that the power received by the components close to the BS (e.g., splitters,antennas, etc.) does not exceed the maximum power they can handle. This situationappears in high capacity and multi-operator passive DAS where a high power in theBS is required.

2.2.2 Fiber DAS Solution (Active DAS)

Active DAS solutions are characterized by the use of active components. These componentsare in charge of compensating the cable losses; thus, allowing the use of thinner cablesthat can be used for longer distances. There are difference types of active DAS, some are100% analog signals, others make RF to digital conversion and sometimes they also havean IP transportation layer. The usual active components are [11]:

• Master Unit (MU): Is in charge of managing the signals in all the solution and sendthem to the Expansion Units (EUs). It is also in charge of making internal calibra-tions to compensate for the cable losses to each EU. Additionally, it monitors theperformance of each equipment and supports remote IP support for troubleshooting.

• Expansion Unit (EU): It is in charge of the optical to electrical signal conversion tothe Remote Units (RUs) that are connected to the antennas. In some medium-sizedbuilding, the EU can be avoided, and a connection is made directly between theMU and the RUs by coaxial or Ethernet cables.

• Optical Fiber: Both systems, Single Mode Fiber (SMF) and Multimode Fiber(MMF), are supported and are required to be installed between the MU and the EUs.In Mic Nordic, it is considered as a rule of thumb that MMF supports distances upto 500 meters while SMF can reach up to 6 km.

• Remote Unit (RU): Are commonly located next to the antennas and are in chargeof the DL and UL RF to electrical conversion. They require a DC feed that isnormally provided by the EU. In pure fiber DAS installations, an Optical RemoteUnit (ORU) is used instead and, in this case, is in charge of optical to RF signalconversion between the MU and the antennas.

Nowadays, active DAS are not commonly seen in deployments due to their high costand complexity. Most indoor solutions using active components are a combination ofa passive and active DAS, therefore receiving the name of ”Hybrid DAS.” The mostcommonly deployed Hybrid architecture has a direct connection between the MU and RU

15

(avoiding the EU) through an optical cable, where the RU is in charge of the RF/opticalconversion [11]. An example is shown in Figure 1.4, where the MU is connected directlyto different RUs and these are connected to the antennas. Further information is in thenext section.

The main advantage of using active DAS are [11]:

• Transparency: The distances does not matter at all, as active components are usedto compensate cable losses.

• Flexibility: It can be easily updated as modifications in the design will not influencethe whole solution.

• Monitoring: Alarms can be configured for potential errors, and an end-to-endperformance can be monitored.

• Less Power Consumption: A typical active DAS is fed by a mini-BS with an inputpower of roughly +10 dBm. Additionally, this type of BS does not require ventilationand, because of their size, can be installed on a shelf instead of requiring a wholeroom for the equipment.

The main disadvantage of active DAS is [11]:

• Additional active components required, meaning an increase in the system complexityand cost.

In the case of Active DAS solutions there are some changes in the design and from theoperation and maintenance perspective [11]:

• Compared to free space propagations, there are three additional delays considera-tions:

1. Active DAS delay: it comes from the digital processing devices and its specifiedin the equipment datasheet.

2. Passive cable delay: same as in the passive solution, additional 13.64% delay.

3. Fiber cable delay: Signal propagates at 0.681 of the speed in the free spacewhich leads to a 46.84% of increased delay.

• One of the main problems in active DAS solutions is in the UL noise power. One wayto overcome this problem is the use of attenuators in the uplink port. Furthermore,digital processing can also be added to decrease the UL noise gain in the device.

16

2.2.3 Hybrid Active DAS Solutions

These are more commonly deployed than pure active DAS solution, as they balance theadvantages and disadvantages of passive and active architectures. They basically usethe passive DAS solution for the close by antennas and the active solution for furtherlocations.

A Hybrid DAS example from Mic Nordic is shown in Figure 2.1. The Mobile Operatorssignal comes from two different sources: an off-air repeater (i.e., donor antenna) or a BaseStation. All these signals are the input of the Multi-Operator Combiner that feeds twodifferent networks: a Passive Network and an Active network (comprised of the MasterUnit and 3 Remote Units). It is interesting to observe that the output of the ActiveNetwork can be connected to a Passive or an Active Network or even to both, dependingon the requirements in the solution.

Figure 2.1: Hybrid DAS example from Mic Nordic

It has the following characteristics [11]:

• Instead of RU, a Hybrid Remote Unit (HRU) is required. It has a fiber connec-tion towards the MU, and a passive connection to the antennas. Normally, somecomplexity in the design is added due to the local power supply requirement at theHRUs which might also need active cooling.

17

Table 2.5: RF Design Levels per Technology at Different Environments, adapted from [11]

Zone Type 2G RxLev (dBm) 3G RSCP (dBm) 4G RSRP (dBm)Zone A: Basement andlow-interference areasfrom the Macro

-85 -90 -100 to -95

Zone B: Office andhigh-use environments,with limited interferencefrom Macro

-70 -80 -90 to -85

Zone C: High interferencefrom Macro

-60 -70 more than -85

Some operational and maintenance considerations in a Hybrid DAS are [11]:

• The monitoring is only possible in the active part of the solution (i.e., until theHRU).

• Another type of hybrid solutions is when having an outdoor macro network locatedon the roof connected to an indoor DAS solution in the same building. This situationnormally happens in high-rise buildings where the macro antenna does not providecoverage to the building where it is installed, as the main lobes are not pointingtowards its own building.

2.2.4 General Considerations in a DAS RF Design

Some requirements are applied to both passive and active DAS solutions [11]:

• The indoor solution RF target levels will depend on the amount of interference fromthe neighbor macro BS. The level required per technology is shown in Table 2.5.

• The indoor RF design should focus to provide a good service in VIP areas andlocations with a huge amount of active users.

• Design according to the lowest dominator: Meaning that the design should focuson the technology that requires the smallest coverage radius, that will at the sametime, over-dimension the other technologies. In other words, if an indoor solution isrequired for 2G/3G/4G technologies, the design will be based on the technology thatrequires the largest amount of antennas in order to satisfy the capacity constraints.

• In some scenarios, an in-line Bidirectional Amplifier (BDA) is used in the passivepart of the solution to boost the UL and DL performance. The downside of thiscomponent is that the overall noise figure of the system is increased as well due tothe passive attenuations.

18

• In hybrid DAS solutions, normally the passive DAS is located close to the BS (wherethe cable loss is minimum), and the active DAS is used for other further places withhigher cable loss and also higher power requirements at the antennas.

• The 1dB compression point (P1dB) has to be taken into account. It indicates thepower level where the amplifier enters into saturation and the gain is dropped by1dB while losing its linearity gain. Its normal value goes around 10dB.

• Noise figure impact in the uplink sensitivity: It is the relationship between theinput and output Signal-To-Noise (SNR) which leads to the noise introduced by theamplifier. Its calculation is as follows:

Noise Figure (NF) = 10 log

(SNRinput

SNRoutput

)(2.4)

• Dynamic Range of the Active Equipment: It is important to check the supportedminimum and maximum signal levels supported by each equipment.

• Passive cable delays: The RF signal has 12% less speed when its propagated througha passive cable in comparison to free space (i.e., 264 000 km/s) which is equivalentto a delay increase of 13.64%. This extra delay has to be taken into account in largedeployments.

• Adjacent Channel Interference Ratio (ACIR) has to be added to the BS noise floor.Sometimes the adjacent channel interference is the one which determines the totalnoise floor of the system.

• Electromagnetic Radiation (EMR): The indoor design must fulfill all the EMRregulations from the region where it is going to be installed. These standards andregulations are constantly changing.

• The outdoor signal level should be considered to determine the required RF targetlevels and the handover (HO) zone between the macro and the indoor solution.

• Around 95% of the indoor solutions are implemented with omnidirectional antennas.Nevertheless, there are some scenarios where directive antennas are more useful:

1. In narrow corridors: A directive antenna will allow the signal to reach, withalmost the same power level, the end of the corridor and the performance couldbe almost the same as by locating an omnidirectional antenna in the center.This implementation can be used in scenarios where cable length can be saved.

2. In high-rise buildings: To overcome the high interference from the macrosat the floor borders by creating an indoor cell dominance. This can also beachieved by placing omnidirectional antennas close to the windows.

3. For a clear definition of HO zones: This can be done in large places where thegoal is to minimize the number of HO zones.

19

• By doing an appropriate RF design, the UL power from the UEs will also beminimized, increasing the battery life of the mobiles. Moreover, when a UE useshigh power (especially in scenarios when the HO Zone is not well defined, and someUEs are connected to the macro), the noise level in the UL increases and the capacityof the indoor cell decreases.

• Interleaving Antenna Technique Between Floors: Basically, this technique focuseson taking advantage of the floor leakage between different floors (when having asimilar floor plan in all the building). The idea is to reduce the number of antennasto the half by letting the leakage signal to serve the adjacent floors. However, thistechnique will only work when using the same logical cell in all the building, asdifferent cells would create unnecessary handovers and decrease the cells dominance.

Additionally, there are some considerations to be taken into account when designing amulti-operator DAS [11]:

• A fading margin around 16-18dB is usually considered in indoor environments witha target coverage of 95%.

• The main unit should make isolation between UL and DL signals which goes around30dB.

• Typically the reflected power of a DAS should not exceed 10-15 dB between theforward and reverse power.

• As a rule of thumb, the link budget between the UE and the Base Station providingthe service should not exceed 25 dB.

• The inter-band isolation between different mobile technologies should be better than50dB.

• One of the main problems in combined systems is the Passive Intermodulation(PIM) minimization. It is generated by the passive components when there is acombination of two or more signals, as their result goes into the non-linear behaviorof the components. This non-linearity is mainly caused by the junction betweentwo components with different materials. PIM effect is the generation of unwantedharmonic signals at different frequencies which will end up decreasing the overallcell throughput and, in some congested cases, it can even generate drops. As a ruleof thumb, the maximum PIM requirement calculation is shown in Eq.(2.5)

MaxPIM = −110dBm −BSTxPow (2.5)

In this way, the maximum PIM depends on the Base Station transmission powerand a -110 dBm reference value. For example, by following Eq.(2.5), a -153 dBmPIM will be required for a Base Station transmitting at 43 dBm and a -140 dBmPIM would be required for a 30 dBm transmission power.

20

Chapter 3

Theory

3.1 Thermal Noise

It is also called white noise and its characterized by been constant over all the frequencyspectrum. In this way, the noise power calculation is shown in Eq.(3.1).

P = 10 log(KTB) (3.1)

Where K is the Boltzmann’s constant (i.e., 1.38×10−23J/K), T is the temperature inKelvins and B is to total bandwidth in Hertz. By following Eq.(3.1), the noise power for2G and 3G can be calculated as follows:

P2G = −174dBm/Hz + 10 log(200 × 103Hz) = −121dBm (3.2)

P3G = −174dBm/Hz + 10 log(3.84 × 106Hz) = −108dBm (3.3)

Where P2G is calculated from the 200kHz channels and P3G from the whole 3.84MHz 3Gspectrum.

The relationship between the input and output noise is calculated by the followingequation:

Noise Figure (NF) = 10 log

(SNRinput

SNRoutput

)(3.4)

Where NF represents the relation between the input and output SNRs in dBs. From thisvalue, the receiver noise floor is calculated as follows:

Rx Noise Floor = KT0B + NF (3.5)

Where a typical value of T0 is 290 Kelvin (i.e., 17 Celsius or 62 Fahrenheit). This valuerepresents the lowest signal that can be received by the receiver and it is equivalent tothe noise level generated by itself. Finally, the receiver sensitivity calculations are:

Rx Sensitivity = Rx Noise Floor + Service SNR Requirement (3.6)

Where the Service SNR Requirements represent the SNR needed for a certain service (i.e.,voice or data).

21

22

Table 3.1: Free Space Loss at 1 meter (rounded), adapted from [11]

Free Space Loss 1m 2m 4m 8m 16m800 MHz 31 dB 37 dB 43 dB 49 dB 55 dB950 MHz 32 dB 38 dB 44 dB 50 dB 56 dB1850 MHz 38 dB 44 dB 50 dB 56 dB 62 dB2150 MHz 39 dB 45 dB 51 dB 57 dB 63 dB2600 MHz 41 dB 47 dB 53 dB 59 dB 65 dB

One of the major problems for 3G/4G is in the UL noise increase. The best solution isto place Bi-Directional amplifiers as close as possible to the antennas to reduce the totalNoise Floor of the system to the minimum. More detail information regarding noise isexplained in [11].

3.2 Indoor Propagation Models

In order to calculate the indoor antennas output power, an indoor propagation model hasto be taken into account.

3.2.1 Free Space Loss

It is the simplest model and it is valid up to 50m in a line-of-sight scenario. It is calledfree space as neither reflections nor refractions are taken into account. It is described inEq.(3.7)

Free Space Loss (dB) = 32.44 + 20(logF ) + 20(logD) (3.7)

Where F represents the frequency in MHz, D the distance in km and 32.44 is defined asthe loss at 1m. The typical mobile frequencies free space losses are shown in Table 3.1. Asit can be seen from Eq.(3.7), there is a 6dB loss every time the distance is doubled [11].

3.2.2 Path Loss Slope (PLS) Model

It is a widely used model that takes into account the frequency and the indoor environmentby following Eq. (3.8) [11].

PLS Path Loss (dB) = Free Space Path Loss at 1m (dB) + PLS × log(distance in m)(3.8)

PLS values depend on the indoor environment as shown in Table 3.2. The approach issimilar to the Free Space Loss, but the propagation losses vary with the environmentalconditions. An example of the PLS in a dense environment at 1800/2100 MHz is shownin:

23

Table 3.2: PLS Constants, adapted from [11]

Type of environment 900 MHz 1800/2100 MHzOpen environment: few RF obstacles(e.g., factories, convention centers)

30.1

Moderately open environment: low to mediumRF obstacles (e.g., airports, factories)

35 32

Slightly dense environment: medium to largeRF obstacles (e.g., malls, offices)

36.1 33.12

Moderately dense environment: medium tolarge RF obstacles (e.g.,Office that is 50%cubicle and 50% hard wall)

37.6 34.8

Dense environment: large number of RF obstacles 39.4 38.1

0 5 10 15 20 25 30 35 40 45 50

Distance from DAS antenna [m]

30

40

50

60

70

80

90

100

110

Fre

e S

pa

ce

Lo

ss [

dB

]

Free Space Loss

900

1800

2100

Figure 3.1: PLS at different distances for a PLS constant of 38.1, adapted from [11]

3.3 Residential Users Traffic Modeling

This section will focus on determining the user profile for voice and data, and the capacityper technology.

24

Table 3.3: Typical Voice Load in Erlang, adapted from [11]

User Type Traffic Load per UserExtreme User 200mEHeavy User 100mE

Normal Office User 50mEPrivate User 20mE

Table 3.4: LTE User Profiles at Busy-Hour, adapted from [12]

Service TypeMall

(mE/user)Venue

(mE/user)Stadium

(mE/user)Avg Throughput

(kbps)Blocking

ProbabilityEmail 50 50 50 100 0.03Browsing 100 100 100 200 0.04Video Conferencing 25 50 5 600 0.08Data Download 150 150 150 1000 0.1Video Streaming 5 10 2 2000 0.2

3.3.1 User profile

3.3.1.1 Voice profile per user

Users can be classified into four categories to determine their capacity load in Erlang asshown in Table 3.3.

3.3.1.2 Data Profile per User

The user data profile is based on [12]. Different user profiles at busy-hour are shown inFigure 3.2 where three different environments are described: stadium, venue and mall.Each service is associated with a required throughput and a certain blocking probability.The resume is shown on Table 3.4. It is important to notice that the definition in [12]for Erlang is different from the traditional voice Erlang definition. Definition of Erlangin data is presented as the percentage of users using a specific service. For example, avalue of 100 mE, is interpreted as 10% of the users using that specific service; and themaximum theoretical value would be at 1 E which would represent that 100% of the usersare using that specific service at busy-hour.

25

Figure 3.2: LTE Traffic Distribution (mE/user) during the busy-hour, adapted from [12]

3.3.2 2G Capacity

In 2G, each TRX can handle up to 7 voice traffic channels (some TRX uses less than 7voice channels as channels are also required for signaling). The total number of users tobe served can be calculated from the Erlang B formula and the desired Grade of Service(GoS) as shown in Table 3.5. The total number of erlangs depends on the number ofusers and the users’ type while the Blocking Probability depends on the mobile operator’starget. With both values, it is possible to determine the total number of traffic channelsand, hence, the total number of TRX.

Additionally, it should also be considered that the capacity is doubled when Half-rate(i.e., UEs only transmit or receive at their assigned time slot) is enabled. A typical 2Gindoor will use a maximum of 12 channels, but in theory, it could go up to 16-24 channelon the same indoor cell [11].

3.3.3 3G Capacity

In 3G, one carrier can handle up to 39 voice channels (12.2 Kbps) of which 1 data service at384 Kbps requires 12 of these channels [11]. The 3G analysis will be focused on voice callsas most of the data is currently handled by 4G. Thus, the 3G voice capacity calculationfollows the same approach as in 2G.

3.3.4 4G Capacity

It is more difficult to define a fixed way to calculate the supported capacity of a system,as the number of PRBs that should be allocated to each user’s service depends on thesignal quality (i.e., SINR) [12]. One approach is presented in [12]. The steps are:

26

Table 3.5: Erlang B Table, adapted from [11]

Blocking Probability or GoS (%)Number ofchannels

0.10% 0.50% 1% 2% 5%

1 0.001 0.005 0.01 0.02 0.0522 0.045 0.105 0.152 0.223 0.3813 0.193 0.349 0.455 0.602 0.8994 0.439 0.701 0.869 1.092 1.5245 0.762 1.132 1.36 1.657 2.2186 1.145 1.621 1.909 2.275 2.967 1.578 2.157 2.5 2.935 3.7378 2.051 2.729 3.127 3.627 4.5439 2.557 3.332 3.782 4.344 5.3710 3.092 3.96 4.461 5.084 6.21511 3.651 4.61 5.159 5.841 7.07612 4.231 5.278 5.876 6.614 7.9513 4.83 5.963 6.607 7.401 8.83414 5.446 6.663 7.351 8.2 9.72915 6.077 7.375 8.108 9.009 10.63316 6.721 8.099 8.875 9.828 11.54417 7.378 8.834 9.651 10.656 12.46118 8.045 9.578 10.437 11.491 13.38519 8.723 10.331 11.23 12.333 14.31520 9.411 11.092 12.031 13.182 15.24921 10.108 11.86 12.838 14.036 16.18922 10.812 12.635 13.651 14.896 17.13223 11.524 13.416 14.47 15.761 18.0824 12.243 14.204 15.295 16.631 19.03125 12.969 14.997 16.125 17.505 19.98526 13.701 15.795 16.959 18.383 20.94327 14.439 16.598 17.797 19.265 21.90428 15.182 17.406 18.64 20.15 22.86729 15.93 18.218 19.487 21.039 23.83330 16.684 19.034 20.337 21.932 24.80231 17.442 19.854 21.191 22.827 25.77332 18.205 20.678 22.048 23.725 26.74633 18.972 21.505 22.909 24.626 27.72134 19.743 22.336 23.772 25.529 28.69835 20.517 23.169 24.638 26.435 29.67736 21.296 24.006 25.507 27.343 30.65737 22.078 24.846 26.378 28.254 31.6438 22.864 25.689 27.252 29.166 32.62439 23.652 26.534 28.129 30.081 33.60940 24.444 27.382 29.007 30.997 34.596

27

Table 3.6: SINR Mapping, adapted from [12] and [15]

CQIIndex

ModulationCode Rate

x 1024Spectral Efficiency

(bps/Hz)DL TP perRB (Kbps)

Estimated DLSINR (dB)

1 QPSK 78 0.1523 19.1898 -7.282 QPSK 193 0.377 47.502 -4.783 QPSK 449 0.877 110.502 -2.044 16QAM 378 1.4766 186.0516 0.665 16QAM 490 1.9141 241.1766 2.846 16QAM 616 2.4063 303.1938 4.737 64QAM 466 2.7305 344.043 6.388 64QAM 567 3.3223 418.6098 8.789 64QAM 666 3.9023 491.6898 11.4910 64QAM 772 4.5234 569.9484 13.2711 64QAM 873 5.1152 644.5152 16.5212 256QAM 711 5.5547 699.8922 19.7113 256QAM 797 6.2266 784.5516 23.1214 256QAM 885 6.9141 871.1766 26.3715 256QAM 948 7.4063 933.1938 28.79

1. Service area SINR calculation: The SINR represents the relation between thereceived signal power and the noise plus interference. The received signal power isthe difference between the transmitted RSCP from Eq.(2.1) and the path-loss fromthe PLS model in Eq.(3.8). The Noise calculation is based on Eq.(3.1), where thebandwidth to consider is equal to the 180 kHz from each physical resource block.In this way, the thermal noise is approximate -121 dBm. The PIM should also beincluded in the noise, and it can be estimated as one additional dB as long as thePIM signal is lower than -127 dBm; resulting in a total noise of -120 dBm. Finally,the interference calculation comes from all the other cells transmitting at the samecarrier.

2. Mapping between SINR and throughput: The mapping between SINR and achievedthroughput at the PRB is presented in Table 3.6. By having the SINR value rangesfrom the RF coverage simulation, it is possible to map them with their throughputand calculate the total throughput of the sector.

3. Capacity calculation per User: The throughput required per user is shown in Eq(3.9).

28

Total Data Rateper User[Mbps]

=

[Email Usageper User

[Erlang/User]·Email Data Rate

per User[Mbps]

· (1 −Email BlockingProbability

[%])

+Browsing Usage

per User[Erlang/User]

·Browsing Data Rate

per User[Mbps]

· (1 −Browsing Blocking

Probability[%]

)

+Video Conferencing

Usage per User[Erlang/User]

·Video ConferencingData Rate per User

[Mbps]· (1 −

Video ConferencingBlocking Probability

[%])

+Data DownloadUsage per User[Erlang/User]

·Data Download Data

Rate per User[Mbps]

· (1 −Data Download

Blocking Probability[%]

)

+Video StreamingUsage per User[Erlang/User]

·Video Streaming Data

Rate per User[Mbps]

· (1 −Video Streaming

Blocking Probability[%]

)

](3.9)

The services Usage per User, Data Rate per User and Blocking probability valuesshould be defined for a peak hour scenario. By following the values in Table 3.4,the average Total Data Rate per User are 193 Kbps, 171 Kbps and 156 Kbps for auser in a venue, mall and stadium respectively.

4. Number of supported LTE users: Once the throughput per user is determined, thetotal number of LTE users that can be supported is calculated from Eq.(3.10).

Number of supported LTE users =Total Data Rate per Sector [Mbps] · Number of LTE sectors

Total Data Rate per User [Mbps] ·[

Transmission Duration [ms]Delay Between

Consecutive Data Tx [ms]·Duty Cycle

](3.10)

This formula is based on the Total Data Rate per Sector calculated on step 2 andthe Total Data Rate per user defined in step 3. The additional variables are thenumber of LTE sectors, which is the one with the biggest influence on the systemcapacity, and the following LTE parameters: Transmission Duration, Delay BetweenConsecutive Data Tx and Duty Cycle. These parameters are also defined at thebusiest hour traffic, where most users will demand these services.

3.3.5 Additional Considerations

There are some additional considerations to be taken into account when making thecapacity dimensioning [11]:

• Capacity based calculation of the number of traffic channels that are needed in thesystem is based on the busiest hours.

• Trunking gain concept: For voice, when two or more resources are combined into asingle one, its capacity is increased in comparison to the sum of their independentcapacities.

29

3.4 Macro Base Station PowerConsumption Models

3.4.1 WCDMA Base Station Model

The WCDMA BS Model is based on [6]. In this paper, tests were performed for a 3 sectorUMTS BS with an output power of 25 Watts per sector. The frequency operation wasset to 2100 MHz, and the data was collected for 5 days. Figure 3.3 shows the measuredresults and the linear model approximation is shown in Eq.(3.11). Figure 3.4 shows thelinear approximation simulation in Matlab.

PowerWCDMA = 1.46 · Traffic [Er] + 551 (3.11)

Figure 3.3: WCDMA Macro Power Con-sumption, from [6]

0 2 4 6 8 10 12 14 16 18 20

Average Traffic (Erl)

550

555

560

565

570

575

580

585

Po

we

r (W

)

UMTS (1/1/1 TxPerSector=25W)

Figure 3.4: Matlab Simulation for WCDMAMacro Power Consumption, adapted from[6]

3.4.2 LTE Base Station Model

The LTE Base Station Model is based on [9] and shown in Figure 3.5. In this paper, theLTE macro BS power consumption is disaggregated into 6 different sources:

• Power Amplifier (PA): As it can be seen from Figure 3.5, the PA power consumptionis not linear. The reason is that it normally works linearly when it is 6-12 dB belowsaturation point; where saturation point is normally achieved at the maximumoutput power as it reaches the highest efficiency.

• Radio Frequency (RF): Related to the receiver and transmitter power for uplinkand downlink communication respectively.

30

• Baseband (BB) Unit: It is related to the signal processing power required in the BS(e.g., filtering, up/down-conversion, channel coding/decoding, etc.).

• DC-DC Power Supply: It is the power required to convert the source Direct Current(DC) power into the required one for the Base Station. It represents around 7.5% ofthe overall power consumption.

• Cooling (CO): Represents the cooling system power required for the Base Station.It is important to notice that cooling systems are normally required on high powerBase Station and that smaller BS can be cooled just with natural air circulation. Itrepresents around 10% of the overall power consumption.

• Main Supply (MS): Represents the electrical power required for the BS and normallyis around 9% of the total BS power consumption.

The scenario analyzed in the paper is a LTE BS with 10 MHz bandwidth and 2 x 2MIMO configuration. The linear approximation from Figure 3.5 is presented in [16] andshown in Eq. (3.12).

Pin = NTRX · (P0 + ∆pPout), 0 ≤ Pout ≤ Pmax (3.12)

The NTRX is considered to be 6, as there are 2 antennas per sector to support MIMO.The P0 value for a macro BS with a Pmax of 40 W is 118.7 W with a ∆p of 2.66. Finally,the Pout value goes from 0 to 40 W. The linear approximation simulation in Matlab isshown in Figure 3.6.

Figure 3.5: LTE Macro Power Consump-tion, from [9]

0 20 40 60 80 100

Load (%)

0

200

400

600

800

1000

1200

1400

Po

we

r (W

) PA

RF

BB

DC

CO

MS

Figure 3.6: Matlab Simulation for LTEMacro Power Consumption, adapted from[9]

Chapter 4

Measurement Strategy

4.1 Measurement Tools

4.1.1 Spectrum Analyzer

The Spectrum Analyzer is an equipment used to measure signals at certain frequencyranges. The measurements were performed using the model R&S Handheld SpectrumAnalyzer FSH4. The goal was to measure the system load at different timestamps. Inthe case of WCDMA, the variation of the load is correlated with the Channel Power:an increase in the WCDMA system load is reflected in an increase in the power, andvice-versa. The value was obtained directly (in dBm) from the Digital Modulation Modeas shown in Figure 4.1, where 1○ is the selected WCDMA Channel and 2○ its respectiveinstantaneous channel power.

In the case of LTE, the parameters are shown in Figure 4.2, where the frequency centeris in 3○ and the instantaneous traffic appears in 4○

Figure 4.1: WCDMA in Spectrum Analyzer Figure 4.2: LTE in Spectrum Analyzer

As there is a constant variation of the traffic in time, a C# script was created in

31

32

Visual Studio to make a periodic query to the spectrum analyzer and get the trafficload information as shown in Figure 4.3. The remote control was possible by usingStandard Commands for Programmable Instruments (SCPI) commands and the NationalInstrument Virtual Instrument Software Architecture (VISA) for developers. One sectionof the SCPI commands implementation in Visual Studio is shown in Figure 4.4. The querywas performed every 15 seconds and the channel/frequency measured by the spectrumanalyzer was cyclically switched after that. In this way, the frequency of the measurementper carrier was 15 seconds multiplied by the number of carriers.

Different SCPI commands are required depending on the technology. In the case ofWCDMA, it is required to specify the Frequency Band and the channel number that isgoing to be analyzed. In the case of LTE, only the center frequency is required.

Figure 4.3: Spectrum Analyzer RemoteControl Program

Figure 4.4: SCPI commands for SpectrumAnalyzer Remote Control

4.1.2 Energy Monitor Device

Efergy Online Energy Monitor 3-Phase kit engage hub (Model No. HH-2.0) sensors areused to measure the power consumption of any device. The information is displayedin real time through a web interface, and the product also provides cloud storage forhistorical data.

33

Figure 4.5: Energy Sensor and Transmitter

Figure 4.6: Engage Sensor (In charge ofreceiving the data from the Transmitterand sending it to the cloud)

The data collection happens in Figure 4.5 where the sensors in 1○ are clipped in thefeeding cable and connected to the Transmitters in 2○. The collected data is wirelesslytransmitted to the Engage hub shown in Figure 4.6 as 3○. These devices must have anInternet connection through an Ethernet cable and are in charge of sending the data tothe cloud. Finally the historical data can be downloaded from the cloud of each device ina .csv file.

4.2 WCDMA Power Measurement

The WCDMA power measurement follows the steps shown in Figure 4.7.

34

Figure 4.7: WCDMA Power Consumption Measurement Setup

35

The Active DAS power consumption is measured in its two active components: MasterUnit (MU) and Remote Unit (RU). The data collection was taken from the RU outputin the 2.1 GHz band as shown in Figure 4.8. As it can be observed from the figure, theUMTS Band Selective repeater amplifies noise between 2.1 GHz and 2.15 GHz, and 2.17and 2.18 GHz. The WCDMA signal only appears in the range between 2.15 GHz and 2.17GHz. This band corresponds to the Operator SUNAB (Telia/Tele2) and four WCDMAchannels: 10762, 10787, 10812 and 10836. These four bands are the ones set in the scriptto fetch the data from the spectrum analyzer.

Figure 4.8: 2.1 GHz Band Measurement. The expected WCDMA spectrums are notvisible due to 3 MHz RBW, a resolution of 30 KHz was required to distinguish the 4different carriers.

4.3 Case Study: Norra Tornen

Norra Tornen is an energy efficient residential building with 37 floors and 178 apartments.The total number of residents can be roughly estimated in 534 by considering 3 personsper apartment. The indoor solution to be implemented in the building is a Passive DASsolution with 24 BS located in 6 different rooms. Each room has 4 different base stationscorresponding to different operators and certain technologies as shown in Figure 4.9.

36

Figure 4.9: Norra Tornen Base Stations Distribution

Chapter 5

Simulation

5.1 Overview

The simulation was done in Matlab, with the goal to determine the expected throughputfor one LTE sector by considering the RF propagation conditions on the second floor ofNorra Tornen.

5.2 Simulation Parameters

The simulation parameters are resumed in Table 5.1. A Path Loss Slope (PLS) constantof 38.1 was chosen, as the goal of the simulation is to evaluate the worst case scenario (i.e.,dense environment with several RF obstacles in the 1800/2100 MHz band). An IndoorAntenna Tx Output Power of 0 dBm is considered for the simulation as it is the standardvalue in most practical RF indoor designs.

The SNR of the system is calculated by considering a total noise of -120 dBm. Thisnoise is composed by the thermal LTE noise of -121 dBm (by considering the LTE PhysicalResource Block bandwidth of 180 kHz) which increases up to -120 dBm when consideringthe PIM noise (when the PIM is lower than -127 dBm) [12].

Table 5.1: LTE RSRP and SNR Matlab Simulation Parameters

Technology LTEBand 1800 MHzPath Loss Slope constant 38.1Indoor Antennas Tx Power 0 dBmLTE Bandwidth 10 MHzNumber of PRB 50Number of Sub-Carriers per RB 12Noise -120 dBm

37

38

5.3 Simulation Output

RF propagation simulation was made by considering Norra Tornen’s real measures ofthe 2nd floor. Figure 5.1 shows the LTE RSRP values that were calculated from Eq.(2.1)with the parameters mentioned in the previous section. Once the RSRP is calculated,the SNR is obtained by dividing the RSRP with the Noise. In this simulation, the wallsattenuation are considered to be isolating the building from the macros; thus no macrointerference is considered.

RSRP (TxPower = 0dBm, PLS=38.1)

50 100 150 200 250 300 350

X (dm)

50

100

150

200

250

300

350

Y (

dm

)

-95

-90

-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

RS

RP

(dB

m)

Figure 5.1: Norra Tornen 2nd Floor RSRPSimulation

SNR (TxPower = 0dBm, PLS=38.1)

50 100 150 200 250 300 350

X (dm)

50

100

150

200

250

300

350

Y (

dm

)

20

30

40

50

60

70

80

90

SN

R (

dB

)

Figure 5.2: Norra Tornen 2nd Floor SNRSimulation

Chapter 6

Results

6.1 WCDMA Power ConsumptionMeasurements Variation at Different

Loads

The test was performed for 5 days by following the WCDMA Power MeasurementProcedure.

39

40

6.1.1 Master Unit Power Consumption at Different Loads

Figure 6.1 shows the WCDMA RF Channel Power variation and the MU power variationover time. As it can be seen from the graph, there is no correlation between these twomeasurements.

Figure 6.1: Master Unit Power Consumption at different WCDMA System Load

41

6.1.2 Remote Unit Power Consumption at Different Loads

In Figure 6.2, the WCDMA RF Channel Power variation, and the RU power variationare shown. As it can be seen from the red dotted lines, there is a correlation in someof the RU Power consumption peaks with an increase in the WCDMA RF channelpower. Nevertheless, the variation in the power is less than 5% of the average RU powerconsumption. As the variation is small, the power consumption of the RU at differentWCDMA loads could be considered almost constant.

Figure 6.2: Remote Unit Power Consumption at different WCDMA System Load

42

6.2 Power Consumption Measured atDifferent Number of Transmitted

Frequencies

In this setup, the power consumption of the Remote Unit was measured at a differentnumber of transmitted frequencies. The results are shown in Figure 6.3.

Figure 6.3: Power Consumption at Different Number of Transmitted Frequencies

It is important to notice that there were no variations of the power consumption atdifferent transmitted output powers. The reason is that the RU always transmits at thesame power and the output variation is due to the attenuation value configured in thedevice.

The average output power per number of transmitted frequencies is shown in Table6.1. It is observed that there is approximately a 30 W difference each time an additionalfrequency is added in the transmission. The MU power consumption remained at a constant68 W during the whole test. A resume of the total Active DAs power consumption perequipment is shown in Figure 6.4.

43

Table 6.1: Active DAS Average Consumed Power per Number of Transmitted Frequencies

# TxFrequencies

Avg RUPow (W)

MUPow (W)

Total ActiveDAS POW (W)

0 86 68 1541 121 68 1892 152 68 2203 185 68 2534 216 68 284

Figure 6.4: Average Active DAS Power Consumption per Number of Tx Frequencies

44

6.3 Capacity Dimensioning

6.3.1 LTE Capacity Dimensioning

The LTE capacity dimensioning is based on the SNR Matlab simulation and their cor-responding mapping to the Resource Block throughput in Table 3.6. The results areshown in Figure 6.5. From these values, it can be observed that most of the floor coverageachieved the maximum throughput of 933.2 kbps per RB. By considering the percentagesand the associated throughput for 50 RB in a 10 MHz band, the total throughput for 1sector is 45.3 Mbps. It is relevant to consider that this would be in the worst case scenarioand, if MIMO is considered, the throughput is almost doubled.

Figure 6.5: SNR Mapping with Throughput per Resource Block

With a total throughput of 45.3 Mbps, and by following the 4G Capacity calculationprocedure in Eq.(3.9) and Eq.(3.10) with the parameters summarized in Table 6.2, theestimated number of supported users per sector is shown in Figure 6.6.

45

Table 6.2: LTE capacity Dimensioning Parameters

Item Mall Venue Stadium UnitEmail Users UsageConnection Duration 50 50 50 mE/userData Rate 100 kbpsCall Blocking 3%Web Browsing UsageConnection Duration 100 100 100 mE/userData Rate 100 kbpsCall Blocking 4%Video ConferencingConnection Duration 25 50 5 mE/userData Rate 600 kbpsCall Blocking 8%Data DownloadConnection Duration 150 150 150 mE/userData Rate 1000 kbpsCall Blocking 10%Video StreamingConnection Duration 5 10 2 mE/userData Rate 2000 kbpsCall Blocking 20%Additionalparameters at Busy HourDelay BetweenConsecutive Data Tx

10 ms

TransmissionDuration

1 ms

Duty Cycle 0.4

46

Figure 6.6: Estimated Number of Supported Users per Sector for different LTE UserProfiles

In Figure 6.6, the estimated number of residents in Norra Tornen building is 534residents. Nevertheless, the data market share of the operators providing the servicesshould also be considered [17]:

• Telenor: 28%

• Tele2: 27%

• Telia Company: 22%

• Hi3G: 21%

In this way, there are less than 150 expected users per operator. Thus, it can be determinedthat only 1 sector would be sufficient to satisfy all the data requirements for the wholebuilding.

6.3.2 WCDMA Capacity Dimensioning

WCDMA Capacity Dimensioning is based on the 39 voice channels that can be supportedby one sector. Erlang B Table is applied for this value, and the result is shown in Table6.3. In the case of Norra Tornen, private users are expected (20mE per user), and it canbe seen that even less than 0.01% of blocking probability can be achieved by using justone sector for less than 200 users. This would suggest that one WCDMA sector would bemore than enough for this deployment.

47

Table 6.3: WCDMA Voice Capacity per Sector

GoS (%)0.01 0.05 0.1 0.5 1 2 5 10 15 20 30 40

Erlangs supportedby 39 Voice Channels

20.64 22.64 23.65 26.53 28.13 30.08 33.61 37.72 41.32 44.91 52.82 62.69

# Extreme UsersSupported (200mE)

103 113 118 133 141 150 168 189 207 225 264 313

# Heavy UsersSupported (100mE)

206 226 237 265 281 301 336 377 413 449 528 627

# Normal Office UsersSupported (50mE)

413 453 473 531 563 602 672 754 826 898 1056 1254

# Private UsersSupported (20mE)

1032 1132 1183 1327 1407 1504 1681 1886 2066 2246 2641 3135

6.4 Power Consumption Comparisonbetween a Passive and Active Solution

The following considerations were made:

• GSM power consumption was not considered in the GSM/LTE base station; onlyLTE power consumption was considered (as no power model was found for a dualtechnology base station). In this way, there might be even a bigger energy savingdifference when implementing an Active DAS solution, as GSM Base Stations arecharacterized by being energy inefficient.

• WCDMA Power consumption values come from the model that was made fora 3 sector Base Station. A 1 sector Base Station would require less power andcould decrease the difference between the Active and Passive energy consumption.Nevertheless, this difference would be compensated from the previous item.

6.4.1 Passive DAS Total Power Consumption

The Passive DAS energy consumption is only based on the number of Base Stations peroperator. For Norra Tornen, there will be 18 LTE BS and 6 WCDMA BS, each basestation will have only one sector. The results are based on the power LTE and WCDMApower consumption models and shown in Figure 6.7.

48

Figure 6.7: Passive DAS Total Power Consumption

6.4.2 Active DAS Total Power Consumption for 1 BS with 1sector per operator

This subsection assumes that each operator uses only 1 Base Station with 1 sector. Allthe operator’s Base Stations outputs are combined and connected to a single Master Unitthat distributes the signal from all the operators to 6 Remote Units located at the sameplace as the current Base Stations from the passive solution. The power consumptionmodels for LTE and WCDMA were combined with the values obtained from the activecomponents tests leading to the outcome in Figure 6.8.

Figure 6.8: Active DAS Total Power Con-sumption for 1 sector per operator

Figure 6.9: Active DAS Total Power Con-sumption for 2 sector per operator

49

6.4.3 Active DAS Total Power Consumption for 2 BS with 1sector per operator

It follows a similar approach as the previous item, with the difference that now 2 BaseStations with 1 sector are considered for each operator. The reason to increase the numberof Base Stations would be to increase the system capacity. The results are shown in Figure6.9.

6.4.4 Passive DAS vs. Active DAs Power Consumption Sum-mary

Figure 6.10 summarizes the results in this section with the following observations:

• Active DAS solution with 1 sector represents 25% of the Passive DAS solution atmaximum load

• Active DAS solution with 2 sectors represents 41% of the Passive DAS solution atmaximum load

• There is a 60% total power increase when going from 1 Base Station to 2 BaseStations per operator on an Active DAS solution.

Figure 6.10: DAS Power Consumption Summary

6.5 CO2 Emissions Comparison

An evaluation has been made in terms of the CO2 grams emissions that can be savedby implementing a Hybrid solution. The amount of CO2 emissions per kWh at different

50

years depends on the country as shown in Figure 6.11. Three different scenarios wereanalyzed: Estonia, United Stated and Sweden; where Estonia is the least ”green” countryfrom all the ones analyzed by the OECD [14].

Figure 6.11: CO2 emission from electricity generation (g/kWh), adapted from [14]

By considering the Power consumption values of 11.598 kW, 2.978 kW and 4.778 kWrequired for a Passive, Hybrid with 1 BS per operator and Hybrid with 2 BS per operatorDAS solution respectively, the total amount of CO2 emissions are shown in Figure 6.12and Table 6.13.

Figure 6.12: CO2 emission per DAS architecture(CO2 g/kWh)

Figure 6.13: CO2 emission ta-ble per DAS architecture (CO2g/kWh)

As it can be observed, there is a considerable difference in the amount of CO2 emissionsreduction depending on the analyzed country. Electricity generation in Sweden produces1.3% of the CO2 emissions generated in Estonia and 2.7% of the amount generated in theUnited States.

Chapter 7

Conclusions and Future Work

The trade-off between energy consumption and system capacity has been analyzed in thisthesis. It has been concluded that a careful capacity dimensioning estimation is requiredbefore deciding either for a passive or active solution. For the capacity dimensioning it isimportant to have a clear definition of the environment and determine the user profile forboth voice and data. A proper RF coverage simulation is also required to determine thetotal achievable throughput per sector and the number of supported users.

Regarding WCDMA power consumption in indoor solutions, it has been determinedthat there is less than 5% variation in the Remote Unit; thus it can be considered asconstant. Furthermore, the Master Unit power consumption is always constant andindependent of the load or the number of transmitted frequencies.

Regarding the case study of Norra Tornen, it has been determined that only one sectorfor LTE and one sector for WCDMA would be more than enough for the expected trafficdemand in the building. With one LTE sector with the RF conditions in Norra Tornen,it will be possible to serve 939 users in a venue, 1058 users in a mall or 1166 users in astadium. These values are far beyond the less than 200 expected residential users peroperator in the building. Regarding WCDMA, one sector would be able to serve 1032private users with a GoS of 0.01% which also greatly exceeds the voice demand in NorraTornen.

Finally, it was observed that the Active Solution with 1 sector reduces the powerconsumption up to 75% when compared with the current passive DAS solution and byconsidering the Base Stations power. This power consumption decrease does not degradethe performance of the system as it is already over-dimensioned and only one sector isrequired per technology for each operator. In terms of CO2 emissions reduction, this valueis highly dependent on the country. As an example, in Sweden, CO2 emissions per kWhare 2.7% in comparison to the US; thus, the power reduction will have higher impact inthe carbon footprint reduction in countries were ”clean” energy policies are still in earlystages.

As an additional comment, when implementing an Active DAS solution, it is importantto realize the energy payment shift in the electricity bill from the mobile operator to thereal estate owner (as the BS power consumption is paid by the MO but the active DAS ispaid by the building owner). This is, however, normal, and would be part of the business

51

52

model within the connection fees that the real estate owner charge to the mobile operator.Another topic is the mobile operator’s willingness to have a shared active DAS infras-

tructure. Most mobile operators try to avoid sharing indoor solutions as it is complicatedto add separate O&M platform solutions in their Network and Operating Centers (NOC),and it also requires a maintenance contract with the company offering the active DASservice. Nevertheless, it is likely that shared indoor infrastructures will be required infuture big deployments due to their considerable energy saving and their scalability.

Regarding future Work to be continued from the thesis:

• Carbon Dioxin Emission calculation and comparison between the Passive and HybridDAS solution.

• LTE measurements to determine the Remote Unit power consumption variation atdifferent LTE system loads.

• Residential data traffic analysis to determine the traffic required per user for eachdata service. This is a complex analysis as it also requires an analysis of Wi-Fiservices in residential buildings to determine the percentage of Wi-Fi services comingfrom LTE or cable.

• VoLTE addition in the LTE data services when making the LTE capacity dimen-sioning. The throughput per service should also be updated as it is increasingcontinuously.

Bibliography

[1] Nokia (2015), ”Nokia Networks launches zero CO2 emissionbase station site offering”. Retrieved 09 March 2018 from:https://www.nokia.com/en˙int/news/releases/2015/02/24/nokia-networks-launches-zero-co2-emission-base-station-site-offering-mwc15

[2] British Telecom (2016), ”The role of ICT in reducing car-bon emissions in the EU.” Retrieved 09 March 2018 from:https://www.btplc.com/Purposefulbusiness/Ourapproach/Ourpolicies/ICT Carbon Reduction EU.pdf

[3] ABI Research (2016), ”ABI Research Anticipates In-Building Mobile DataTraffic to Grow by More Than 600% by 2020”. Retrieved 09 March 2018from: https://www.abiresearch.com/press/abi-research-anticipates-building-mobile-data-traf/

[4] Cisco (2017), ”The Zettabyte Era: Trends and Analysis.” Retrieved 31 March 2018from: https://www.cisco.com/c/en/us/solutions/collateral/ser vice-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html

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