UPTEC F 19056

Examensarbete 30 hpOktober 2019

Energy Consumption Optimizations for 5G networks

Martina Tran

Teknisk- naturvetenskaplig fakultet UTH-enheten Besöksadress: Ångströmlaboratoriet Lägerhyddsvägen 1 Hus 4, Plan 0 Postadress: Box 536 751 21 Uppsala Telefon: 018 – 471 30 03 Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student

Abstract

Energy Consumption Optimizations for 5G networks

Martina Tran

The importance of energy efficiency has grown alongside awareness of climate changedue to the rapid increase of greenhouse gases. With the increasing trend regardingmobile subscribers, it is necessary to prevent an expansion of energy consumption viamobile networks.

In this thesis, the energy optimization of the new radio access technology called 5GNR utilizing different sleep states to put base stations to sleep when they are nottransmitting data is discussed. Energy savings and file latency with heterogeneous andsuper dense urban scenarios was evaluated through simulations with differentnetwork deployments.

An updated power model has been proposed and the sensitivity of the new powermodel was analyzed by adjusting wake-up time and sleep factors. This showed thatcareful implementation is necessary when adjusting these parameter settings, althoughin most cases it did not change the end results by much.

Since 5G NR has more potential in energy optimization compared to the previousgeneration mobile network 4G LTE, up to 4 sleep states was implemented on the NRbase stations and one idle mode on LTE base stations. To mitigate unnecessary sleep,deactivation timers are used which decides when to put base stations to sleep.Without deactivation timers, the delay could increase significantly, while withdeactivation timers the delay increase would only be a few percent.

Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and72.7% energy with NR standalone deployment compared to LTE standalonedeployment, while minimally impacting the delay on file by 1%.

ISSN: 1401-5757, UPTEC F 19056Examinator: Tomas Nyberg, Uppsala UniversityÄmnesgranskare: Mikael Sternad, Uppsala UniversityHandledare: Richard Tano, Ericsson

Sammanfattning

Med tanke pa den okande medvetenheten om klimatet och den stigande koldioxidhalten,vill man minska energianvandandet. Ju mer energi som anvands, desto samre ar det forklimatet da skapandet av energi via forbranning av fossil leder till okad koldioxidhalt.Dessutom okar antalet mobilanvandare for varje ar och darfor ar det viktigt att inteenergianvandandet pa grund av det fortsatter oka.

Genom att anvanda sig av ”heterogeneous deployment” och en sovalgorithm kan energisparas och samtidigt paverka anvandare minimalt. Tidigare arbeten har gjorts pa detta,da hur mycket energi man kan spara in som mest undersoktes, utan att ta hansyn tillhur anvandare paverkas. I detta arbete har energioptimisering av NR med olika djupasomntillstand implementerats pa basstationer. Basstationer kan sattas i somn nar detinte i finns nagra aktiva anvandare i.e. anvandare som vill ladda ner filer. Utvarderingargjordes pa energianvandandet och filfordrojningar via simulationer i stadsmiljoer medolika mobilnat.

Tack vare ”the ultra-lean design” kan 5G NR sova djupare och langre jamfort meddet tidigare mobilnatet 4G LTE. Den implementerade sovalgoritmen anvander sig av4 somnstadier mojliga pa NR basstationer och ett vilolage pa LTE basstationer. Enviktig faktor nar man anvander sig av sovalgoritmen ar att veta nar det ar som mestoptimalt att satta basstationer i somn. For tidig somn paverkar anvandare negativtoch for sen somn paverkar energibesparingar negativt. For att minska onnodig somnanvandes deaktiverinstider som bestammer nar basstationer satts i somn och gor detgradvis. Deaktiveringstiderna ar viktiga att anvanda och optimera beroende pa vad detar for trafik. De har visats minska effekten pa filfordojningen och samtidigt fortfarandemaximerat energibesparingen.

En uppdaterad energimodell har redovisats eftersom den tidigare energimodellen antasvara for optimistisk. Sensitiviteten av den nya modellen har analyserats genom att justeraaktiveringstiden och energianvandandet for varje somnstadie. Analysen visar att for deflesta fall forandras inte resultatet mycket, men det ar fortfarande viktigt att tanka pahur parametrarna bestams.

Med den uppdaterade energimodellen ar det mojligt att spara upp till 42.5% energi medLTE-NR utplacering och upp till 72.7% energibesparing med endast NR utplacering, omman jamfor med endast LTE utplacering. I dessa fall paverkas filfordrojningen minimaltmed ungefar 1%.

ii

Acknowledgements

The master thesis job was done at Ericsson R&D in Kista, Stockholm.

Firstly, I want to give my biggest gratitude to my supervisor Richard Tano at Ericsson.Thank you for your continuous support and guidance working with this project. I learnta lot and I could not have asked for a better supervisor. Your patience allowed me to dothe best I could with the time I had at Ericsson.

A special thanks to Gustav Wikstrom and everyone else at the section who I had thepleasure to meet, and for making me feel welcomed. Thank you for the great fikas andthe fun after work activities.

Additionally, I would like to thank Pal Fregner for providing important knowledge aboutenergy optimizations and for the comments during my presentation.

Finally, I would like to thank my supervisor at Uppsala University Mikael Sternad forthe initial advices and for reviewing my work.

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Contents

List of Figures vi

List of Tables viii

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 Approaches to Energy Efficiency . . . . . . . . . . . . . . . . . . . 31.1.2 5G NR Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.1.3 Thesis Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.1.4 Delimitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.1.5 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Theory 82.1 Base station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Heterogeneous deployments . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Macro cells and Small cells . . . . . . . . . . . . . . . . . . . . . . 92.3 LTE and NR basics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3.1 Orthogonal frequency division multiplexing, OFDM . . . . . . . . 92.3.2 Time-domain and Frequency-domain structure . . . . . . . . . . . 102.3.3 Reference signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.4 Dual Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.5 Duplex Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.6 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.7 File transfer protocol, FTP . . . . . . . . . . . . . . . . . . . . . . . . . . 142.8 Power models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.8.1 Idle mode power consumption . . . . . . . . . . . . . . . . . . . . . 152.8.2 LTE base station power model . . . . . . . . . . . . . . . . . . . . 162.8.3 NR base station power model . . . . . . . . . . . . . . . . . . . . . 17

2.9 Energy and performance metrics . . . . . . . . . . . . . . . . . . . . . . . 18

3 Methodology 193.1 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.1 Round Trip Time, RTT . . . . . . . . . . . . . . . . . . . . . . . . 193.1.2 Downlink user throughput . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Energy evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.1 Daily traffic load . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.2 Power per unit area . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2.3 Average daily power consumption . . . . . . . . . . . . . . . . . . 20

3.3 Sleep mode algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.3.1 Configurations of sleep states . . . . . . . . . . . . . . . . . . . . . 24

3.4 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

iv

3.5 Scenario setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.6 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Simulation Results 274.1 Throughput vs Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2 Different deactivation timers . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Performance Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.3.1 LTE-NR dual connectivity deployment . . . . . . . . . . . . . . . . 294.3.2 NR standalone deployment . . . . . . . . . . . . . . . . . . . . . . 304.3.3 10th and 50th percentiles . . . . . . . . . . . . . . . . . . . . . . . 324.3.4 90th percentile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.4 Energy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.4.1 LTE-NR dual connectivity deployment . . . . . . . . . . . . . . . . 374.4.2 NR standalone deployment . . . . . . . . . . . . . . . . . . . . . . 404.4.3 Percentage spent in sleep states . . . . . . . . . . . . . . . . . . . . 41

4.5 Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5 Discussion 475.1 Deactivation timers’ impact . . . . . . . . . . . . . . . . . . . . . . . . . . 475.2 Performance evaluation discussion . . . . . . . . . . . . . . . . . . . . . . 47

5.2.1 Best case users and the median discussion . . . . . . . . . . . . . . 485.2.2 Worst case users discussion . . . . . . . . . . . . . . . . . . . . . . 49

5.3 Energy evaluation discussion . . . . . . . . . . . . . . . . . . . . . . . . . 505.4 Sensitivity analysis discussions . . . . . . . . . . . . . . . . . . . . . . . . 51

6 Conclusion 53

7 Future work 55

References 56

v

List of Figures

1.1 The increase of global mobile connections (excluding IoT) between year2014 and 2024 [3]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 A timeline of the mobile network evolution [5]. . . . . . . . . . . . . . . . 21.3 The increase of base stations between the years 2007-2012 [8], [9]. . . . . . 31.4 Figure shows from left to right, the power consumption in mobile networks,

and the power consumption distribution in a base station [8]. . . . . . . . 4

2.1 A heterogeneous network deployment with hexagonal cells, macro basestations, micro base stations and users. . . . . . . . . . . . . . . . . . . . . 9

2.2 OFDM subcarrier spacing ∆f [15]. . . . . . . . . . . . . . . . . . . . . . . 102.3 Time-domain structure of LTE and NR [15], [11]. . . . . . . . . . . . . . . 102.4 Frequency-domain structure of LTE and NR [15], [11]. . . . . . . . . . . . 112.5 Example of dual connectivity. . . . . . . . . . . . . . . . . . . . . . . . . . 122.6 Bearer concept of dual connectivity with LTE and NR [16]. . . . . . . . . 132.7 Duplex schemes in time-frequency structure for uplink and downlink [15]. 132.8 The congestion avoidance [18]. . . . . . . . . . . . . . . . . . . . . . . . . 152.9 The idle mode power consumption of LTE and NR [13]. . . . . . . . . . . 162.10 Power consumption dependency on relative RF output power in base sta-

tion [19]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.11 The four defined sleep state modes with the sleep factors and total power

consumption, P . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1 The average daily traffic profile in Europe [19]. . . . . . . . . . . . . . . . 203.2 Flowchart of the activation mode. . . . . . . . . . . . . . . . . . . . . . . . 223.3 Power model with a factor 2 decrease in power consumption for each 10-

fold increase in wake-up time. . . . . . . . . . . . . . . . . . . . . . . . . . 233.4 Flowchart of the sleep state algorithm with the four sleep modes. . . . . . 243.5 The process of obtaining the simulation results. . . . . . . . . . . . . . . . 26

4.1 The performance curves of 10 Mb file download without deactivation timers. 274.2 Average delay of 10 Mb file download with optimized deactivation times

and without deactivation times. . . . . . . . . . . . . . . . . . . . . . . . . 284.3 The average delay of multiple 1 Mb FTP file downloads. . . . . . . . . . . 294.4 The average delay of multiple small file transmissions with deactivation

timers from Table 4.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304.5 The average delay with fixed SS configuration on macro layer with varying

SS configurations on micro layer. . . . . . . . . . . . . . . . . . . . . . . . 314.6 The average delay where the micro layer cells have fixed SS3. . . . . . . . 324.7 The 10th and 50th percentile delay curves of LTE-NR deployment with 10

Mb file downloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

vi

4.8 The 10th and 50th percentile delay curves of LTE-NR deployment withmultiple 1 Mb file downloads. . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.9 The 10th and 50th percentile delay with fixed SS configuration on macrolayer with varying SS configurations on micro layer. . . . . . . . . . . . . 34

4.10 The 90th percentile delay curves of LTE-NR deployment with 10 Mb filedownloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.11 The 90th percentile delay curves of LTE-NR deployment with 1 Mb filedownloads. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.12 The 95th percentile delay of multiple 1 Mb file downloads with optimizeddeactivation timers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.13 The 90th percentile delay with fixed SS configuration on macro layer withvarying SS configurations on micro layer. . . . . . . . . . . . . . . . . . . 37

4.14 The total energy consumption of the scenarios in Figure 4.2. . . . . . . . . 384.15 The daily energy consumption curves of the scenarios in Figure 4.14. . . . 384.16 Zoomed in version of Figure 4.15 with the three scenarios with the lowest

energy usage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.17 Total energy consumption of multiple small file transmissions with DA1

and DA2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394.18 The total energy consumption with fixed SS3 on micro layer. . . . . . . . 404.19 The total energy consumption with varying SS configurations on micro layer. 404.20 10 Mb file transmission with LTE-NR deployment. . . . . . . . . . . . . . 414.21 NR standalone deployment with fix SS4 on macro layer cells. . . . . . . . 424.22 Average state percentage of cells for small file transmissions with baseline

DA and DA2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.23 Average state percentage of cells for small file transmissions with DA1 and

DA2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.24 Sensitivity analysis of delay when adjusting wake-up time. . . . . . . . . . 444.25 Sensitivity analysis of total energy consumption with A=5 and A=15. . . 444.26 Sensitivity analysis of the total energy consumption with different sleep

factors, B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.27 Sensitivity analysis of state percentage with A=5 and A=15. . . . . . . . 46

vii

List of Tables

2.1 Example of a 3-sector LTE macro 2x2 LTE power model parameters. . . . 172.2 Example of a 3-sector NR macro 2x2 NR power model parameters. . . . . 17

3.1 Power model configurations, with the different cell states (columns) indifferent configurations (rows). . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2 The summarized simulation setup. . . . . . . . . . . . . . . . . . . . . . . 26

4.1 The baseline deactivation timers and the corresponding sleep state. . . . . 284.2 Deactivation timers for 1 Mb file transmissions. . . . . . . . . . . . . . . . 284.3 10 Mb file transmission delay increase with deactivation timers. . . . . . . 294.4 1 Mb file transmissions delay increase with optimized deactivation timers. 294.5 10 Mb file transmission delay increase with NR deployment. . . . . . . . . 304.6 Energy savings in percentage for each configuration from Figure 4.14. . . 384.7 The increase of energy savings in percentage between using DA1 and DA2. 394.8 Energy savings in percentage for each configuration from Figure 4.19 and

Figure 4.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414.9 The updated wake-up times and sleep factor used in sensitivity analysis. . 434.10 The energy savings in percentage between configurations from adjusting

wake-up time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 454.11 The energy savings in percentage between configurations from Figure 4.11 46

6.1 Summarized delay and energy saving results. . . . . . . . . . . . . . . . . 536.2 Results from sensitivity analysis. . . . . . . . . . . . . . . . . . . . . . . . 53

viii

List of Abbreviations

3GPP Third Generation Partnership Project

CDMA Code-Division Multiple Access

CRS Cell Specific Reference Signal

DA Deactivation timers

EARTH Energy Aware Radio and neTwork tecHnologies

EDGE Enhanced Data Rates for GSM Evolution

FFT Fast Fourier Transform

FTP File Transfer Protocol

FWA Fixed Wireless Access

GSM Global System for Mobile Communications

HSPA High Speed Package Access

ICT Information and Communications Technology

IEEE Institute of Electrical and Electronics Engineers

IoT Internet of Things

ISD Inter-site Distance

LTE Long Term Evolution

MAC Media Access Control

MIMO Multiple Input Multiple Output

NR New Radio

OFDM Orthogonal Frequency Division Multiplexing

ix

PBCH Physical Broadcast Channel

PSS Primary Synchronization Signal

RF Radio Frequency

SDU Super Dense Urban

SI System Information

SSS Secondary Synchronization Signal

SS Sleep State

TD-SCDMA Time Division Synchronous Code Division Multiple Access

w/o without

w/ with

WCDMA Wideband Code Division Multiple Access

x

Chapter 1

Introduction

New Radio (NR) is the radio access network for the fifth generation (5G) mobile systemdefined by Third Generation Partnership Project (3GPP). 3GPP is the standard orga-nization that develops the protocols for mobile networks. The early implementation of5G is already upon us and released for commercial use in the US and South Korea. Bythe end of 2019, 16 more major countries will have launched 5G. However, it is expectedthat it will not be until after 2020 that the majority of countries will deploy 5G networks[1]. With the increasing usage of mobile networks and internet of things (IoT), there isalso an increase of energy consumption to satisfy the large data traffic volumes. This isa major concern when developing next generation mobile systems. A study about theglobal footprint of mobile communications [2] predict that between year 2007 and 2020the increase of CO2 emissions will increase by a factor of three.

By the end of the year 2024, 1.9 billion mobile connections are expected by Ericssonto be provided by 5G. This corresponds to over 20% of the global mobile connections(excluding IoT) at that time [3]. The increase of mobile connections between the yearsof 2014 and 2024 is illustrated in Figure 1.1. In terms of IoT connections, it is expectedto increase from 8.6 billion to 22.3 billion between the years of 2018 to 2024.

The impact on the climate due to the energy consumption depends on the source ofenergy. The most common energy source is fossil fuels. Burning fossil fuels releasesenergy which could be used for electrical power. The downside of using fossil fuels arethe release of CO2 which traps heat. As more CO2 is released in the atmosphere themore the global temperature rises and affects the climate. As there is a rise of attentionand awareness of climate change and the rising global temperature, it is vital to saveenergy by increasing energy efficiency. There are many ways to save energy, however inthis thesis the focus will solely be placed on energy efficiency enhancements using sleepcycle algorithms.

This thesis has laid the groundwork for a conference paper which will be presented in2019 IEEE 90th Vehicular Technology Conference in Hawaii [4].

1

Figure 1.1: The increase of global mobile connections (excluding IoT) betweenyear 2014 and 2024 [3].

1.1 Background

Mobile networks have gone through five generations from, 1G to 5G, shown in Figure1.2 below. 1G was the start of mobile networks where it was possible to make mobilevoice calls. With the introduction to 2G text messaging via SMS was made possible. 3Gwas the beginning of mobile web browsing and when 4G was released, data speed andcapacity increased significantly which then lead to video streaming via the mobile phone.Finally, with 5G comes a variety of new features like improved network connection andlower latency. It opens up new use case opportunities and allows for more connecteddevices which was previously not possible.

Figure 1.2: A timeline of the mobile network evolution [5].

2

One of the major points of concern is the increase of energy consumption of Informationand Communications Technology (ICT) as it is quickly changing and developing. Betweenthe years 2010 and 2015, the ICT energy consumption has increased by 31%. Additionallyduring that time the operational carbon emission grew by 17%. According to Ericsson,the total annual operational carbon emission in 2015 of ICT networks was estimatedto be 0.53% (32 Gtonnes) of the global CO2 emissions related to energy, or 0.34% (40Gtonnes) of all CO2 emissions.

The annual increase of energy consumption and operational CO2 emission is approxi-mately linear, and the reason for the increase is mainly due to the expansion of mobilenetworks [6]. By the end of 2024 it is estimated that 5G is going to provide coverage tomore than 40% of the world’s population. Between the years 2018 and 2024 the totalmobile traffic data is expected to grow by a factor of 5 [7].

The largest power draining part in mobile networks are the base stations, which takeup about 57% of the total energy consumption. Additionally, as seen in Figure 1.3 thetotal increase of base stations has been roughly 95%. Most of the power optimizationcan therefore be done in the base stations [8], [9]. The energy consumption of otherparts in mobile networks are presented in Figure 1.4. Apart from reducing the environ-mental footprint by reducing energy consumption, it also reduces the operational energycosts for the network operators. The reduced cost could help with providing mobilecommunications in developing countries by improving the communication infrastructurethere.

Figure 1.3: The increase of base stations between the years 2007-2012 [8], [9].

1.1.1 Approaches to Energy Efficiency

There are a few approaches to take when optimizing energy efficiency [9]:

• Improving energy efficiency of base station hardware components• Turning off components selectively e.g. sleep mode techniques• Optimizing energy efficiency of the radio transmission process• Planning and deploying heterogeneous cells

Improving the energy efficiency of the hardware components in the base station couldpotentially give the biggest energy savings gain since it represents the highest percentageof the power consumption of the wireless cellular network, as seen in Figure 1.4. The

3

Figure 1.4: Figure shows from left to right, the power consumption in mobilenetworks, and the power consumption distribution in a base station [8].

figure also shows that the power amplifier part in the base station is the biggest partof the energy consumption, most of it dissipated through heat. Improving this aspectcomes with a higher equipment cost as it needs to be replaced and upgraded. Betterpower amplifier could provide higher power efficiency. The cost of the hardware upgradeincreases as more power efficiency is desired.

Selectively turning off components e.g. by using sleep cycle algorithms depending on thetraffic load can be a good implementation. In dense areas, like super dense urban (SDU)areas, with many base stations or small cells this option could be especially good forenergy savings since there will be times of low load in which many base stations or smallcells will not be transmitting to any active users. Sleep cycle algorithms could also beapplied in less dense areas, like rural areas with less base stations. In those scenariosthere are lower loads leading to more sleep opportunities. This approach could come atthe cost of reduced performance.

Ways to optimize energy efficiency of the radio transmission process could be done byusing techniques like multiple-input and multiple-output (MIMO) technique, cognitiveradio transmissions, cooperative relaying, channel coding, and resource allocation forsignaling. These are all methods to increase efficiency of utilizing resources in time,frequency and spatial domains. This category of optimizing energy comes at the cost ofreduced performance.

Deploying small cells (low powered base station coverage area) in an efficient way couldalso reduce energy consumption. This is called heterogeneous deployment and is furtherdiscussed in Section 2.2. The small cells help with improving the coverage between theuser and base station by increasing pathgains, and increasing capacity. The drawbackis having too many ”unnecessarily” deployed small cells could instead increase energyconsumption and radio interference between cells when compared to homogeneous de-ployments. Heterogeneous deployments could be combined with sleep mode techniquesto mitigate the drawback of heterogeneous deployments.

1.1.2 5G NR Network

5G NR introduces many new possibilities for applications and new use cases compared tothe previous generations, like wearables, smart homes, smart cars, and ultra-high-speedmedia delivery. To support the increasing demand of mobile networks, NR has to supporta row of new capabilities like:

4

• Massive system capacity• High data rates• Low latency• Ultra-high reliability and availability• Low device cost and energy consumption• Energy-efficient networks• Interoperability with existing wireless networks

NR has the potential to allow for more energy savings compared to previous generationsof mobile networks as the base stations can sleep for longer and allows for deeper sleepthanks to the ultra-lean radio access design. Previous mobile networks transmit somesignals periodically even when there are no active users in the base station coverage areai.e. the base station cell. These signals are not data user related but are for channelestimations, base station detection, and broadcast of system information. Those signalscould also be called always-on signals. The always-on signals in the LTE case, doesnormally not add much to the total energy consumption as they are designed to onlyhave a minor impact. However, in dense deployments where not all network nodes willalways be utilized to its fullest potential, it results in a low average traffic load pernode which altogether adds up to more energy impact. The always-on signals will thenbe a bigger part of the overall transmissions. The ultra-lean design aims to minimizethe always-on signals [10], [11]. Instead of transmitting the always-on signals at regularintervals it should only be transmitted when necessary. More about the always-on signalimpact is further discussed in Section 2.8.

NR introduces a vast range of frequencies in which it could operate. As of 3GPP Release15 two new frequency bands has been introduced, high frequency band and low frequencyband, which are used for different purposes. The low bands include the range 0.45-6 GHzand the high bands include the range 24.25-52.6 GHz. The higher frequencies providehigher data rates over a smaller area while the lower frequencies provide lower data ratesbut higher coverage.

Since NR now can operate in the higher frequency bands, it also has a much smallerwavelengths compared to LTE. Although this allows the NR base stations to be smallerin size, the wavelengths are blocked more easily. One of the solutions to this problemcould be countered with using many more base station cells to not lose the area coverageof the signal, like when using heterogeneous deployment. However, the increase of cellswill also lead to an increase in energy consumption. To counter this problem an optionis to take advantage of the fact that there is lower traffic during some periods of the dayand areas. For example, in not very densely populated areas it is not necessary to alwayshave all the base stations active and ready to transmit as there is less activity in everycell [11].

1.1.3 Thesis Focus

The main goal of this thesis is to investigate and optimize the energy savings of using sleepcycle algorithms and investigate its impact on the users by looking at the performance indifferent scenarios with different network deployments. The new NR features allows formore energy savings by using sleep cycle algorithms with deeper sleep states comparedto the previous generations’ LTE.

There are two deployment options when transitioning from 4G to 5G. It will start off withdeploying 5G networks, which will in turn aid the existing 4G network and then transitioninto only deploying 5G. The two deployments are called LTE-NR non-standalone and NR

5

standalone deployment. The LTE-NR non-standalone means 4G and 5G will interworkwhile NR standalone only deploys 5G. There is also the third deployment option whichis LTE standalone where only 4G is deployed. This deployment is the one deployedcurrently before the transition. All three deployments are simulated in this thesis. LTEstandalone deployment is used in the baseline reference case.

Evaluations for energy savings and user impact when deploying LTE-NR dual connec-tivity is done by having NR cells added to the existing LTE cells. Simulations of thescenarios will be done with an internal java-based simulator which implements an algo-rithm that puts NR cells to different degrees of deep sleep.

The following subjects are investigated:

• How much energy could be saved by implementing sleep states to base station cellswhile minimally impacting the users• Comparisons between the different network deployments with an implemented sleep

cycle algorithm• How much latency and energy usage are changed by implementing different degrees

of deep sleep• How different traffics are affected by the sleep cycle algorithm• How the increase of traffic load affects the latency and energy usage with sleep

states• A sensitivity analysis of the power model that has been used in the evaluations to

analyze how the sleep factor and the activation time affects the model• User performance impact with sleep states from different perspectives, like worst

user case and best user case• What factors are important when optimizing and implementing the sleep state

algorithm

1.1.4 Delimitation

The scenario setup for the simulations has several different parameters and settings thatcould be changed to fit a desired scenario. For the thesis these parameters and settingshave been set according to what 3GPP or the supervisor has recommended. The SDUscenario has been used for the simulations, which is the scenario with the densest deploy-ments and highest traffic load. This scenario was chosen because it simulates the highesttraffic load allowing a better view of the impact of sleep states. Other scenarios have notbeen included in this thesis due to time limitations. More about the scenario setup aredescribed in Section 3.5.

The traffics investigated are large file transmission and multiple small file transmissions.Large files are transmitted in bursts with a set time interval in-between, this is explainedmore in Section 2.7. However, in the case of small file transmissions, the whole file issent in one instance without being split up. With this traffic, time between the small filetransmissions can be controlled and configured.

In this thesis it is assumed that when base stations enter sleep they do not lose connectionand thus there is no need to re-establish contact to the users again. The deactivatedcomponents in the base station when it is in sleep does not affect the connection betweenthe user and the base station.

The deepest sleep state that has been used is sleep state 4. More sleep state could beimplemented in the future but as of right now a maximum of 4 sleep states has been

6

simulated. Also [12] has defined 4 sleep modes as possible depending on the capabilitiesof hardware components at the base station.

When evaluating the user performance impact, the main impact that has been inves-tigated is the latency. Another subject that could be investigated is the downloadthroughput. However, when it comes to what scenario shows worse or better results,both latency and throughput show almost the same results, but inverted as shown inSection 4.1. Therefore, to delimit only the latency is investigated.

1.1.5 Previous Work

Earlier work at Ericsson has proved that despite the increase of small cells when usingheterogeneous deployments, it is still possible to save energy, especially in the low trafficscenarios [13]. The studies have only investigated how much energy could be saved in thepost processing part, and thus have not covered the user impact. Therefore, it is desiredto also look at the user impact and simulate different scenarios with a more detailedimplemented sleep cycle algorithm.

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Chapter 2

Theory

2.1 Base station

The base station communicates with the core network and the users. It is responsible fortransmitting and receiving radio signals and creating a cell area.

The parts that are essential for a base station are:

• Antenna elements, the part that receives and transmits data to and from the basestation. The more antenna elements used the more capacity could be increased.• A tall structure, i.e. a tower or something else tall that the antenna could be

mounted on as the signals need the height to not be blocked by objects like treesor buildings.• The hardware, the electronics that support the operation of the base station. The

power amplifier is housed in this and helps with amplifying the transmission signal.• A mobile switching station, that connects the wireless calls to the main telephone

system.

2.2 Heterogeneous deployments

As previously mentioned in Section 1.1.1, with the right deployment of base stations andsmall cells, energy could be saved and increase both capacity and data rates. How tobest deploy different deployments and the placement of the small cells has been in focusfor several cellular network releases. Especially for NR, heterogeneous deployments willbe important when it comes to coverage as NR utilizes higher-frequency bands, meaningthe wavelengths are easily blocked by e.g. walls and objects. The small wavelength isespecially a problem when it comes to indoor coverage. One of the solutions to thisproblem could be using many more small cells to not lose the area coverage of the signal.

Heterogeneous network deployment consists of macro cells with additional small cellslike micro cells, which are low-power nodes. The different network nodes use differenttransmit power and the small cells have overlapping coverage with the macro cells i.e.micro cells are placed within the macro cells as illustrated in Figure 2.1. Macro basestations have three hexagonal cells that make up one macro section. For every macrocell there can be several small cell base stations within it.

8

Figure 2.1: A heterogeneous network deployment with hexagonal cells, macrobase stations, micro base stations and users.

2.2.1 Macro cells and Small cells

The macro cells are cells with the widest coverage area and use higher transmission powerthan small cells. Therefore, macro cells are mainly responsible for the baseline coverageand small cells are placed within the macro cell to increase capacity, coverage, and datarates.

Since NR must fulfill high data rates, reliability, and low latency, it must either utilizedenser macro deployment or take advantage of small cells. There are different types ofsmall cells that could be used with heterogeneous deployments. They are all categorizedby the size of the coverage area and the number of users that these cells can service.There are micro cells, pico cells, and femto cells, that vary in heights from tallest toshortest respectfully. In comparison to the others which are suited for indoor use, macrocells are suitable for the outdoors because of it’s higher coverage area [14]. The size ofthe coverage area from the cells varies depending on the frequency and bandwidth of thesignals. Higher frequency and more bandwidth available the bigger the coverage area.The coverage area that the cells can provide is also related to its output power. Thewider the coverage area, the higher the output transmission power is from the cell. Sincemacro cells are taller it means that the antenna is at a higher height. The higher theantenna is placed, the less likely it will be that the signals will be hindered by obstaclessuch as trees and buildings thus coverage area is not limited by the obstacles.

2.3 LTE and NR basics

2.3.1 Orthogonal frequency division multiplexing, OFDM

In wireless communications, transmitted signals that reach the receiver could be affectedby several factors and could cause the signals to be corrupted. Frequency selectivity isa radio propagation anomaly where the radio signal is partially cancelled by itself. Away to increase overall transmission bandwidth, without suffering from increased signalcorruption due to radio-channel frequency selectivity, is utilizing multi-carrier transmis-sion. Multi-carrier transmission is a transmission scheme where instead of transmittinga single wideband signal, subcarriers are used to transport information. Subcarriers aremultiple narrowband signals. The subcarriers are frequency multiplexed, and transmittedtogether over the same radio link to the same receiver. This also ensures a more efficientuse of the bandwidth as the subcarriers are packed closely which is possible thanks tothe orthogonality.

9

Orthogonal frequency division multiplexing (OFDM) is a kind of multicarrier transmis-sion where subcarriers are closely packed with a subcarrier spacing of ∆f , as seen inFigure 2.2. On the transmission side, subcarriers are modulated with data during onesymbol duration which is one OFDM symbol duration. To produce the OFDM symbolan Inverse FFT transform was performed on the subcarriers. Guard intervals are thenintroduced between each OFDM symbol to prevent interference between them. They arethen concatenated to create radio frames of data to be transmitted. The receiver canthen execute a FFT on the OFDM symbols to retrieve the data [15], [11].

Figure 2.2: OFDM subcarrier spacing ∆f [15].

2.3.2 Time-domain and Frequency-domain structure

In the time-domain, both LTE and NR transmissions are organized into frames that are10 ms. Each frame consists then of 10 subframes that are 1 ms each. For LTE, onesubframe then consists of two slots. For NR, one subframe consists of one or more slotsdepending on the subcarrier spacing, as seen in Figure 2.3. When NR subcarrier spacingis 15 kHz, one NR slot has the same structure as an LTE subframe. The slots then aredivided into several OFDM symbols. Each LTE slot either consists of 6 or 7 OFDMsymbols and each NR slot consists of 14 OFDM symbols [15], [11].

Figure 2.3: Time-domain structure of LTE and NR [15], [11].

In the frequency-domain, a resource element consists of one subcarrier during one sub-frame. The resource element is the smallest physical resource in both LTE and NR that

10

a user can be scheduled. The resource elements are then grouped together to createresource blocks. There is a difference between NR and LTE resource blocks. An NRresource block is one-dimensional, spanning the frequency-domain only, while the LTEresource block is two-dimensional with 12 subcarriers in frequency-domain and one slotin time-domain. NR resource blocks are more flexible in time-domain thanks to this.The frequency-domain structure is shown in Figure 2.4.

Figure 2.4: Frequency-domain structure of LTE and NR [15], [11].

2.3.3 Reference signals

Reference signals in the physical layer are predefined signals, which have different pur-poses and occupies specific resource elements. Some of those signals and its purposes are[15], [11]:

• The primary synchronization signal (PSS): used for radio frame synchroniza-tion by the user in cell search. It is the first signal the user device will look forwhen it enters the cell and can also find the cell identity of the cell identity group.• The secondary synchronization signal (SSS): used for radio frame synchro-

nization by the user and is the second signal the user look for after PSS. It can findthe cell identity group of the detected cell.• The system information (SI): system information is the non-user-specific in-

formation that is necessary to properly operate in the network. In LTE case, SI istransmitted periodically over the entire cell. In NR case, SI is split into two parts.The first part are transmitted with longer periodicity (160 ms) and the second partcan be transmitted when necessary.• The physical broadcast channel (PBCH): necessary for the users to access

the network and carries some of the system information• The cell-specific reference signal (CRS): used for channel quality estimation,

downlink demodulation, and time-frequency tracking. NR does not use CRS anduse user-specific reference signal for channel estimation, which means that thesesignals are not transmitted unless there is data to transmit.

2.4 Dual Connectivity

To increase the data rates, mobility robustness and support load balancing, dual con-nectivity could be used. With dual connectivity a user device can simultaneously beconnected to two cells, see Figure 2.5. Dual connectivity was first introduced to be usedbetween LTE base stations. However, for the first time ever, as NR will be deployed, adual connectivity scenario will be enabled for two different generations of radio accesstechnology.

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Figure 2.5: Example of dual connectivity.

The primary cell and secondary cell operates on different carrier frequencies and are con-nected via a non-ideal backhaul, meaning there is a certain latency and limited capacityin the backhaul. In LTE-NR non-standalone mode, the primary cell uses an LTE basestation and an NR base station for secondary cell. The connection to the core networkin dual connectivity is split between the user plane and control plane. Both primary celland secondary cell have direct contact with the core network in the user plane, but inthe control plane only the primary cell has direct contact with the core network. Theuser plane carries the user data, while the control plane is responsible for transmittingsystem information and controlling the user connectivity [16].

The user plane and control plane protocol stack consists of the physical (PHY), mediumaccess control (MAC), radio link control (RLC), and the packet data convergence protocol(PDCP) layers. The control plane additionally consists of the radio resource control(RRC). The key functionalities of the different layers are described as follows [17]:

• The PHY layer transmits information from MAC transport channels and handlesfunctions like power control, link adaptation, and cell search.• The MAC layer handles the mapping between logical channels and transport chan-

nels, scheduling information reporting, error correction, priority handling betweenusers, ets.• The RLC layer transfers protocol data units (PDU) to upper layer, error correction,

segmentation and re-segmentation, etc.• The PDCP layer is responsible for transferring user data, header compression, du-

plication detection, packet duplication, etc. In the control plane this layer performsciphering and integrity protection.• The RRC layer handles the establishment, configuration, maintenance, and release

of data radio and signaling radio bearers, broadcast of system information, mobilityhandling, etc.

There are two ways data could be transported between the network and the users whendual connectivity is enabled: via direct bearers or split bearers. Direct bearers are usedwhen only one lower layer is configured. If two lower layers are configured, then splitbearers are used. If only LTE lower layer is configured, then the direct bearer useseither the LTE or NR higher layer. If only NR lower layer is configured, then the directbearer uses the NR higher layer. However, when both the LTE and NR lower layer areconfigured, then the split bearer will always use the NR higher layers. The splitting ofthe data stream when utilizing split bearers, is done in the PDCP layer [16]. Figure 2.6illustrates the bearer concept.

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Figure 2.6: Bearer concept of dual connectivity with LTE and NR [16].

2.5 Duplex Schemes

There are two different duplex schemes that could be used for both LTE and NR, thefrequency division duplex (FDD), and the time division duplex (TDD) [11], [15]. Theduplex schemes provide spectrum flexibility and allow the base station to transmit onboth uplink (from user to base station) and downlink (from base station to user) simul-taneously. Both LTE and NR support separation of uplink and downlink transmissions.Both duplex schemes are illustrated in Figure 2.7.

FDD allows the uplink/downlink transmissions to use different frequencies and can trans-mit simultaneously. The downside of this duplex scheme is that it requires more frequencybands compared to TDD.

TDD allows the uplink/downlink transmissions to use the same frequency but are sepa-rated by time. The downside of this is that it is more susceptible to interference.

Figure 2.7: Duplex schemes in time-frequency structure for uplink anddownlink [15].

2.6 Scheduling

Scheduling is performed in the Media Access Control (MAC) layer, which handles thetransmission of data packets between channels in the base station. It is in the schedulerthat the sleep cycle algorithm has been implemented, explained more in Section 3.3.

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The scheduler decides how and when the data is transmitted between the users and basestation depending on the channel variations. Best time to transmit is when the conditionsare advantageous to not risk losing packets, increase retransmissions, and overall to utilizeradio resources effectively. The operation of the scheduler used in the simulations is calleddynamic scheduling, this is where the scheduler makes a decision for every subframe. Thescheduler decides the resource allocation for users before it transmits the data dependingon conditions like the channel quality, and buffer status and priorities of different dataflows.

The scheduling strategy is implementation specific. This means that there is no standardbut it is chosen depending on what is desired. The scheduling strategy used in simulationsis round-robin scheduling. In round-robin scheduling users take turns to utilize the sharedresources, without taking into account the channel condition. The radio resources arethen equally distributed.

Link adaptation is closely related to scheduling. Link adaptation tries to adjust trans-mission parameters to compensate for variations in the instantaneous channel conditions.When channel quality is too poor, the error probability increases when transmitting data.Either power at the transmitter could be controlled to keep a constant data rate or thedata rate could be adjusted to compensate for the varying channel conditions. The latteris better for packet-data traffic where a constant data rate is not necessary.

Before transmission, both the scheduling, and link adaptation try to best adapt to thechannel variations. However, since the radio-link quality varies quite a lot, it is impossibleto have perfect adapation to it and could cause lost packets during transmission. Thevariations could be caused by several reasons like shadow fading, which is when obstaclesaffect the signal, or distance-dependent path loss. This is where hybrid automatic repeatrequest (HARQ) comes in. It requests retransmissions of erroneous data packets andhandles random errors like noise in the receiver [15].

2.7 File transfer protocol, FTP

FTP use TCP as its transport layer protocol and allow a client to download files froma server. When a connection is created between an ftp client and an ftp server, a TCPconnection is established and provides reliable transmission. Another characteristic ofFTP files is that they are packaged into smaller packets before being transmitted, they arethen reassembled after the transmission, resulting in transmissions with a small intervalin-between them.

When a client or a server wants to send large amounts of data over TCP, the datais limited by a window. The window is the amount of data that can be sent withoutreceiving a confirmation that the data has been received on the other side. For example,when the client begins transmitting data and the server starts processing the data, if theclient starts transmitting more data when the server has yet to finish the processing, thenthe window closes for the client. When the window is closed, the client cannot continuetransmitting. After the server has finished processing the data, it opens the window andthe transmissions continue. The window gradually moves as data is acknowledged untileverything has been sent.

To prevent congestion and define how much data can be transmitted before networkcongestion occurs, a congestion window is utilized. The congestion window is on thesender side (the source side) of the transmissions. An example of the congestion windowin action: if the source is on a fast network and the transmission destination is on a

14

slow network, then the source side will want to send more data than the destination sidecan handle, thus risking data loss. The congestion window gradually increases until athreshold is reached where the network congestion would most likely occur. Thus, thesource side must send data that does not exceed both the window on the destinationside, and the congestion window.

Another characteristic of TCP is its’ slow start. The slow start is what defines themaximum congestion window. It starts off as one data segment transmission and waitsfor confirmation. If it receives the confirmation then two segments of data is sent. Thisprocess repeats itself by increasing the segment size with a factor of two until either thesource has reached the window size, or congestion occurs. If congestion is reached, thenthe congestion window will be halved, and the value is saved as the congestion threshold.After the congestion window has been halved the slow start repeats itself. During thesecond time the slow start process starts, if the congestion window is less than or equalthe threshold, then it is possible to send double the data. However, if the congestionwindow is larger than the threshold, then doubling the data would most likely causecongestion. In that case the congestion window is only increased by a small amount [18].This is called congestion avoidance and is also shown in Figure 2.8.

Figure 2.8: The congestion avoidance [18].

2.8 Power models

2.8.1 Idle mode power consumption

The idle mode is the mode in which base stations have no active users in cell and doesnot transmit data. The power consumption in idle mode is different between using eitherLTE base stations or NR base stations [13]. Thanks to the ultra-lean design, as describedin Section 1.1.2, NR base stations can have several deep sleep states while, LTE can onlyuse micro sleep. The LTE micro sleep is a sleep state that is even shallower in sleepcompared to NR sleep states, meaning that less energy can be saved in micro sleep.

The power consumption when LTE base stations and NR base stations are in idle modecan be seen in Figure 2.9, which shows that LTE cells has to ”wake up” many more timesin a shorter time period compared to NR cells. This also shows that LTE cells in idlemode consumes more energy overall compared to NR cells.

When the base stations are in idle mode only mandatory reference signals are transmittedand there is no user plane traffic, meaning that no user data is being transmitted. In the

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LTE case, all subframes transmit the CRSs. From Figure 2.9, at subframe 0 there is anincrease in the power consumption and it contains PSS, SSS, and PBCH. At subframe5 there is a period of time of about 1 ms where the energy consumption increases andit contains SI, PSS, and SSS. In between the peaks of power consumption the LTE basestation can sleep for a few OFDM symbols i.e. micro sleep. Before each CRS transmissionwhich is one OFDM symbol (71.4µs), the LTE base station has to start waking up, thusthere is a small increase of energy consumption right before the peak.

In the NR case, because of the ultra-lean design the signals are not transmitted asfrequently as in the LTE case, and signals could also be transmitted simultaneously.Thus, minimizing the number of transmissions. The power consumption in idle modefor NR base stations depends on the number of synchronization signal blocks (SSB) ina cell, the subcarrier spacing, and more. For comparison sake, the subcarrier chosenis the same as LTE case. In NR, SSB contains PSS, SSS, and PBCH. It is assumedthat transmission of both the SSB and SI could take place in the same symbol andconsumes 25% of the total base station power. The SSB transmission periodicity is 20ms. In between the SSB and SI transmissions, the NR cell can sleep longer and deeper.Although the peaks are higher in NR case, it consumes less energy overall because of theless frequent transmissions in the idle mode.

Figure 2.9: The idle mode power consumption of LTE and NR [13].

2.8.2 LTE base station power model

Power consumption at variable load of an LTE base station cell configuration was basedon the EARTH Model [19]. The relation between radio frequency (RF) output power, andthe base station power consumption are almost linear, therefore a linear approximationwas made. The power model is defined in Eq.(2.1), where Ptx is the relative RF outputpower, δ is the sleep factor, Pcell is the base station power consumption, P0 is the powerconsumption at no load RF output power, and ∆P is the slope of the load dependentpower consumption from the linear model parameter. The ∆P slope can be seen inFigure 2.10. Power model parameters for LTE base station cells are presented in Table

16

2.1, which was used with the LTE idle mode power consumption.

Pcell = Ntransmitter ×

∆pPtx + P0, if Ptx > 0 (BS transmitting)

P0, if Ptx = 0 (BS no data to transmit)

δP0, if Ptx = 0 (BS in a sleep state)

(2.1)

Figure 2.10: Power consumption dependency on relative RF output power inbase station [19].

BS type Ntransmitter Ptx [W] ∆p P0 [W] δ

Macro cell 2 20 4.7 130 0.84

Table 2.1: Example of a 3-sector LTE macro 2x2 LTE power model parameters.

2.8.3 NR base station power model

For NR base station, the power model that is used is the power consumption model oflarge scale antenna systems with N antenna elements [20]. Eq.(2.2) shows the NR powermodel which uses four sleep states, shown in Figure 2.11. The parameters in Eq.(2.2) areN , which is the number of antenna elements, ε is the efficiency of the power amplifiers,P0 is the no load power consumption when not transmitting, Pc is the circuit power perantenna branch, δ is the sleep factor, and Ptx is the radiated power per cell. Table 2.2is an example of the power model parameters, which are also the parameters used withthe NR idle mode power consumption.

It should be noted that the power model proposed in this thesis are more conservativecompared to the power model in [12]. It is believed that there are further limitations onthe base station which could impact the activation time and power usage. The limitationsare taken into consideration in the updated model, therefore the power model presentedin this thesis is thought to be more realistic.

Pcell =

Ptxε +NPc + P0, if Ptx > 0 (BS transmitting)

NPc + P0, if Ptx = 0 (BS no data to transmit)

δP0, if Ptx = 0 (BS in a sleep state)

(2.2)

Ptx [W] ε P0 [W] Pc Sleep factor, δ

BS cell 40 0.25 260 1 0.50, 0.25, 0.125, 0.0625

Table 2.2: Example of a 3-sector NR macro 2x2 NR power model parameters.

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Figure 2.11: The four defined sleep state modes with the sleep factors and totalpower consumption, P .

2.9 Energy and performance metrics

To quantify the network performance, some performance metrics are necessary to define.The performance metrics are used to study the benefits of different technology solutions,like sleep states.

For the energy evaluations, the metric needed to relate the energy consumption to thecoverage area to quantify the energy consumption. The EARTH model defines the powerper area unit metric, as described in Section 3.2. When designing an energy efficientnetwork, it is important to consider both the energy consumption metric and the userperformance metric. The goal is to minimize the energy consumption, while not decreas-ing the performance of the network [19].

When evaluating the user performance in radio networks, some of the performance metricsthat are important to investigate, are the perceived average user throughput and thepercentiles of the user throughput, as recommended by 3GPP [21]. With the perceivedaverage user throughput, the average from all perceived throughput for all transmitteddata bursts are intended for the user.

To evaluate the user performance in this thesis, latency has been used instead of through-put. More explained in Section 4.1.

When evaluating the percentiles of the file latency, it is the 10th, 50th, and 90th per-centiles that is of interest. The 10th percentiles of the latency represents the best casefile latency where the peak rate occurs. The 50th percentiles is the median of the filelatency. The 90th percentiles represents the worst case file latency, which usually occurswhen users are at the cell edge, this is when the user is the furthest away from the basestation and the data rate is lower. It is especially important to study the worst caseusers as the average may show a smaller performance impact, but there may be a smallpercentage of users with very bad performance that needs to be considered.

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Chapter 3

Methodology

3.1 Performance evaluation

3.1.1 Round Trip Time, RTT

The Round Trip Time (RTT) has been calculated according to Eq.(3.1), where tfr is thetime of file reception, and trq is the time when the file request was sent.

tRTT = tfr − trq, [s] (3.1)

This is the latency that is being investigated in the simulations. The RTT delay includesamongst others the delay that comes from queuing, activating, transmitting, processing,and propagating. It is necessary to include all these delays from file request until the endof the file reception as it will otherwise be difficult to compare the latency of differentscenarios with different degrees of deep sleep.

3.1.2 Downlink user throughput

The downlink user throughput is calculated using RTT, as seen in Eq.(3.2). NrOfBytesrefers to the number of bytes downloaded by the user.

Throughput =NrOfBytes

1 · 106 · tRTT, [Mb/s] (3.2)

The throughput is the transfer rate between the user and base station. It is the actualdata transmitted and successfully received during a specific amount of time.

3.2 Energy evaluation

3.2.1 Daily traffic load

The daily traffic profile, which is seen in Figure 3.1, was proposed by EARTH and is usedfor the energy evaluations [19], also explained more in Section 3.2.3. It also shows thatthere are times during the day in which the base station will be less active e.g. duringnight time. It gives an idea of how the power consumption is changed over 24h. By using

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the given daily traffic profile, an estimation can be done of the traffic for every hour asa fraction of the maximum traffic load.

Figure 3.1: The average daily traffic profile in Europe [19].

The total traffic load is calculated by taking the downlink data rate divided by thecoverage area, A, as described in Eq.(3.3). This is used for when plotting the performanceevaluation figures in Section 4.3 as it is used in the x-axis of the figures.

Xtraffic =DataRate

A, [Mb/s/km2] (3.3)

3.2.2 Power per unit area

The power per unit area is defined as in Eq.(3.4), where P is the average power con-sumption and A is the coverage area. This metric is closely related to the CO2 emission[19].

Parea =P

A, [W/m2] (3.4)

3.2.3 Average daily power consumption

Energy performance is defined as the daily average area power consumption [19]. Thedaily power consumption is defined by Eq.(3.5), where Pactive and Pidle are the powerconsumption of either the LTE or NR power model depending on what base station wasused, η is the daily traffic profile point, and Ncells are the number of cells.

EP =124

∑24t=1

∑Ncellsi=1 [(Pactive)η

ti + Pidle(1− ηti)]

A, [kW/km2] (3.5)

3.3 Sleep mode algorithm

When cells are in a sleep state it means that there are hardware components like antennasin the base station that have been deactivated. The more parts being deactivated in thebase station, the deeper the sleep the base station enters, leading to more possible energy

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savings. Depending on the number of deactivated parts, the longer the time to activatesaid parts. While it may be possible to quickly shut down the components in the nodes togo into deeper sleep, it will however take time to activate them. Some will take longer toactivate depending on factors such as, if there are components which needs to be warmedup before operating, or if shuffling of data between memory parts in the base station isnecessary.

Problematic for most sleep cycle algorithms is knowing when to put base station cellsto sleep and when to activate them. If the base station cells wake up too late, the userperformance will be impacted negatively. If they wake up too early or they start sleepingtoo late, the energy efficiency will not be as optimal as it could be. Therefore, in thepresented algorithm, deactivation timers are introduced. The deactivation timers putsthe cells gradually to sleep to mitigate unnecessary sleep.

The implemented sleep cycle algorithm used in this thesis has six states, with four sleepstates (SS) used with NR base station cells, which is the number being used in theevaluations:

• Awake mode: The base station cell is active and transmitting data to users.• Sleep State 1 (SS1): The base station cell is in a very light sleep, but still ready to

wake up and transmit when active users arrive in the cell. It has the lowest sleepfactor and shortest activation time. This is the first sleep state the cell enters afterawake mode.• Sleep State 2 (SS2): The base station cell is in a deeper sleep than SS1 with a little

longer activation time and higher sleep factor compared to the standby mode. Thecell enters this sleep state after SS1.• Sleep State 3 (SS3): The base station cell is sleeping with an even higher sleep

factor and activation time compared to SS2. The cell enters this sleep state afterSS2.• Sleep State 4 (SS4): The base station cell is in deep sleep and takes the longest to

activate but has the lowest sleep factor and uses the least energy. The cell entersthis sleep state after SS3.• Activating mode: The base station cell is in the process of waking up and cannot

transmit data yet. This mode is activated if cell is in one of the sleep states andsuddenly needs to wake up. After waking up and leaving this mode the BS cellenters an awake mode.

LTE base station cells use only two states. Those states being:

• Awake mode: Cell is awake and active. Consumes more power compared to theawake mode of NR cell.• Micro sleep (SS0): This is the only sleep state of LTE cells. It is actually not really

a sleep state as it does not require activation that causes an additional delay.

The wake-up times or activation times, and power consumption that belongs to each ofthese sleep states are summarized in Table 3.1. The power consumption is presentedin percentage of overall power usage. The sleep factor of the four sleep states are alsopresented in Table 2.2. LTE0 is the configuration for SS0 and NRX are the configurationsfor the different deep sleeps SS1-SS4.

It is clear from Table 3.1, what power usage and activation time should be associatedwith which sleep state. However, when a cell is in activation mode, the activation timedepends on what sleep mode the cell was in when it enters that mode.

The cell in activation mode gradually steps into lighter sleep as the cell is successively

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Mode configuration LTE0 NR1 NR2 NR3 NR4 Awake Activation

Wake-up time (ms) 0 1 10 100 1000 0 1-1000

Power usage (%) 84 50 25 12.5 6 100 6-50

SS0 X

SS1 X X

SS2 X X X

SS3 X X X X

SS4 X X X X X

Table 3.1: Power model configurations, with the different cell states (columns)in different configurations (rows).

activated. For example, if cell was in SS4 and then is triggered to wake up, it will startwith setting the activation time to the corresponding 1000 ms and the energy usage inthat state is 6% of the full energy usage. The cell though will only spend 900 ms (1000-100 ms) in SS4 before it enters SS3 where the activation time left will be 100 ms and theenergy usage is 12% of the full energy usage. For every subframe the activation timeris decreased, and the cell enters lighter sleep until it is fully awake. This process is alsodescribed in the flowchart in Figure 3.2. Additionally, illustrated in Figure 3.3 is thegradual activation process of cell with wake-up times.

Figure 3.2: Flowchart of the activation mode.

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Figure 3.3: Power model with a factor 2 decrease in power consumption foreach 10-fold increase in wake-up time.

The baseline sleep cycle algorithm utilizes the data buffer of the scheduler. Wheneverthere are active users in the cell, and data arrive at the buffer, it triggers the base stationto start waking up. If the base station is already sleeping, then it enters activation modewhere an ”activation timer” starts. The base station does not start transmitting datauntil the activation timer expires and the base station enters an awake mode. If no activeusers enter the base station cell then the base station will successively enter each sleepmode until it reaches the deepest sleep state. The algorithm will be executed for each cellfor every subframe as the scheduler allocates the resources. If the base station is alreadyin a sleep state, or there are no active users, the ”sleep timer” increments for everysubframe. If the base station is in an activation mode, the activation timer decrementsfor every subframe until it expires and starts transmitting. The flowchart for the sleepmode algorithm implemented is shown in Figure 3.4.

The sleep timer is associated with the deactivation timers, sleep1-sleep3 from Figure 3.4.The deactivation timers decide when the cell should step down into deeper sleep. Forexample, the deactivation timer sleep1 decides when the cell should step into SS1, sleep2decides when the cell enters SS2, and so on. If the sleep timer is less than the deactivationtime, it means it has entered the corresponding sleep state. This causes the cell to enterdeeper sleep as the sleep timer increases.

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Figure 3.4: Flowchart of the sleep state algorithm with the four sleep modes.

If the deactivation timers are set to zero it means no deactivation timer is used. Nodeactivation timers mean that the cell will enter the next sleep state without waiting forthe sleep timer to increase. For example, the deactivation time for SS1 is 0, which meansthe cell enters SS1 as soon as there are no data to transmit. If the deactivation time is 0for both SS1 and SS2, it would mean that the cell enters SS2 as soon as there is no datato transmit.

The first algorithm, which is the baseline sleep mode algorithm used in simulations,utilizes macro cells for coverage, while small cells are mainly used for increasing datarates. In the simulations, the small cells are on the high frequency band which providesmore bandwidth, thus providing higher capacity. Deeper sleep states are implementedon micro cells while the deepest sleep on macro cells is micro sleep. This algorithm isimplemented for LTE-NR non-standalone deployments.

The second algorithm used in simulations, assumes that both the macro layer cells andthe micro layer cells can utilize deep sleep, this means that both layers use NR cells.Utilizing deeper sleep on both macro layer and micro layer would possibly allow for moreenergy savings. This algorithm is implemented for NR standalone deployments.

3.3.1 Configurations of sleep states

The configurations when investigating the LTE-NR deployment is on the macro layerLTE0 (SS0) while for the micro layer it is NRX (SSX ), from Table 3.1. Legends inresulting figures in Section 4 only show one SS, that refers to the SS on micro layer.If two SS are described in the form SSX+SSY, that refers to the (macro layer cell

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SS ) + (micro layer cell SS ). Those mainly refer to the NR-NR deployment. SS0 orSS0+SS0 (LTE standalone) is the baseline configurations where no sleep states has beenimplemented.

3.4 Simulator

For the simulations a java-based simulator provided by Ericsson was used and post-processing of the data obtained by the simulator was done in MATLAB. In the simulatorthere are many different possibilities for parameters and traffic models that could be usedto fit the simulation to the desired scenarios.

The event-driven simulator is a large scale radio system, which can model radio networksand users. It models deployments, propagation, protocols and applications for severaldifferent radio access technologies, including LTE and NR. The simulator additionallyallows for logging of data, like delay and throughput.

3.5 Scenario setup

The scenario setup used for the simulations are similar to those used in [13]. Hexagonalmulti-user and multi-cell scenarios with macro cell and micro cell deployments are usedfor the simulations. The SDU scenario is simulated, where the peak hour traffic isexpected to reach 750Mbps/km2 in 2020 according to [22]. The macro layer consists of3-sector sites using 3GPP antenna models at height 25 m, with 2 antenna elements. Thisantenna configuration is selected to make a fair comparison with LTE. The micro layerconsists of 2 additional nodes located inside each macro-site sector using single sectoromnidirectional antennas, with 64 antenna elements at height of 10 meters. The macrolayers transmit power is 40 W per sector while the micro layer is 20 W per micro cell.The propagation model used was the 3GPP spatial channel model [23]. Two frequencybands, 0.9 GHz with 10 MHz bandwidth and 3.5 GHz with 40 MHz bandwidth, are used.The lower band is configured on the macro layer and the higher band is configured onthe micro layer. Both the macro layer and the micro layer operate in FDD with 15 kHzsubcarrier spacing. Users are stationary with an antenna height of 1.5 m, and randomlydistributed across simulation area with an indoor probability of 80%. All users downloadfiles using the network protocol FTP. Two different traffic scenarios are simulated either,one larger 10 Mb file, or multiple smaller 1 Mb files with an interval of 0.5 s betweenthe files. The inter-site distance, which is the distance between two adjacent macro basestations, is 380 m with cell radius 127 m. The scenario setup is summarized in Table 3.2.

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Simulation parameters Values

Scenario SDU

Peak hour traffic 720 Mbps/km2

Macro cell layer Hexagonal 3-sector

Micro cell layer Omnidirectional single sector

Macro antenna elements 2 at 25 m height

Micro antenna elements 64 at 10 m height

Transmit power macro/micro 40 W / 20 W

Propagation model 3GPP spatial channel model

Frequency bandMicro cells, 3.5 GHzMacro cells, 0.9 GHz

BandwidthMicro cells, 40 MHzMacro cells, 10 MHz

Duplex scheme FDD, subcarrier spacing 15 kHz

User antenna height 1.5 m

Indoor probability 80%

File downloads 10 Mb FTP file & 10 of 1 Mb FTP files

ISD 380 m

Table 3.2: The summarized simulation setup.

3.6 Method

The whole process of obtaining the simulation results has been an iterative process start-ing with defining the algorithm. After that updating the simulator and testing it follows,see Figure 3.5. The process has been split into two parts, the system simulation part andthe post processing part. The system simulation part simulates the scenarios with thealgorithm. Following the system simulation is the post processing part, where simulationresults are obtained. After evaluating the results, parameters and settings are changedto optimize the results and investigate other setups.

Figure 3.5: The process of obtaining the simulation results.

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Chapter 4

Simulation Results

4.1 Throughput vs Latency

As seen from Eq.(3.2), the downlink throughput has an inverted correlation to the delay.The inverted correlation means if throughput decreases, the delay increases and viceversa. Therefore, for performance evaluations the throughput show about the sameresults as when evaluating latency but inverted.

Figure 4.1 shows the downlink throughput and average delay of the same scenario withincreasing traffic load. Comparing them shows that they both have decreasing perfor-mance as the traffic load and number of used deep sleep states possible on cell increases.Therefore, to not increase redundancy and delimit, only the latency is evaluated when itcomes to performance.

4.2 Different deactivation timers

(a) The average downlink throughput curves. (b) The average delay curves.

Figure 4.1: The performance curves of 10 Mb file download withoutdeactivation timers.

Figure 4.1a depicts the throughput impact with increasing traffic load with the deactiva-tion timers removed. It can be noted that the configuration with SS4 without deactivationtimers (w/o DA) does not reach the expected throughput for year 2020.

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The baseline deactivation timers used with the algorithm is presented in Table 4.1, andhas been optimized for 10 Mb FTP file downloads.

Sleep States Deactivation timers (ms)

SS1 0

SS2 100

SS3 300

SS4 600

Table 4.1: The baseline deactivation timers and the corresponding sleep state.

Figure 4.2 shows the average delay impact with increasing traffic load, where both sce-narios with using the defined baseline deactivation timers and not using them has beensimulated. It is clear that using sleep states on cells without deactivation timers increasethe delay. The overall delay also increases with increasing traffic load, especially for theconfiguration of using SS4.

Figure 4.2: Average delay of 10 Mb file download with optimized deactivationtimes and without deactivation times.

Simulations of multiple small file transmissions were done with the baseline deactivationtimers, and with optimized deactivation timers for the specific traffic scenario (DA2),seen in Figure 4.3. When comparing using and not using optimized deactivation timers,the delay is smaller with the deactivation timers. The optimized deactivation timers(DA2) used in Figure 4.3b are presented in Table 4.2.

Configurations DA1 (ms) DA2 (ms)

NR1 0 0

NR2 200 500

NR3 700 700

NR4 1000 1000

Table 4.2: Deactivation timers for 1 Mb file transmissions.

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(a) Deactivation timers not optimized. (b) Deactivation timers optimized.

Figure 4.3: The average delay of multiple 1 Mb FTP file downloads.

4.3 Performance Evaluations

4.3.1 LTE-NR dual connectivity deployment

Large file transmission

Figure 4.2 shows the performance result from large file transmission. The resulting delayincrease has been summarized in Table 4.3 below. The increase in delay for SS1-SS3was consistent despite the increase in traffic load as the baseline delay increases with theload. Generally, increasing the number of sleep states also increases the delay, and byextension, decreases the throughput.

Sleep configuration Average delay increase

SS1 1%

SS2 1%

SS3 2%

SS4 31% - 90%

Table 4.3: 10 Mb file transmission delay increase with deactivation timers.

Multiple small file transmissions

Figure 4.3b shows the performance result from multiple small file transmissions and issummarized in Table 4.4.

Sleep configuration Average delay increase

SS1 1%

SS2 1.6%

SS3 1.6%

SS4 6.6% - 14%

Table 4.4: 1 Mb file transmissions delay increase with optimized deactivation timers.

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Figure 4.4 shows the comparison of the performance results of small file transmissionsbetween using DA1 and DA2 from Table 4.2. Both DA1 and DA2 are optimized deacti-vation timers for this traffic but have different times before entering SS2. This is used toinvestigate how different optimized deactivation timers change performance and energy.Energy impact is further discussed in Section 4.4. The increase of delay is between ∼1-2%when using DA1 compared to the sleep configurations using DA2.

Figure 4.4: The average delay of multiple small file transmissions withdeactivation timers from Table 4.2.

4.3.2 NR standalone deployment

Large file transmissions

Figures 4.5 shows the average delay results from 10 Mb file transmission with differentcombinations of number of sleep states on both the macro and micro layer. The resultshave been summarized in Table 4.5. Throughout all the scenarios one can conclude thatusing the 4th sleep state gives a rather large delay increase.

Sleep configurations(macro+micro)

Average delay increase

SS1+SS1/SS2/SS3 1%

SS2+SS1/SS2/SS3 1%

SS3+SS1/SS2/SS3 5%

SS1/SS2/SS3+SS4 29% - 102%

SS4+SS1/SS2 12%

SS4+SS3 180%

SS4+SS4 290%

Table 4.5: 10 Mb file transmission delay increase with NR deployment.

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(a) SS1 on macro layer (b) SS2 on macro layer

(c) SS3 on macro layer (d) SS4 on macro layer

Figure 4.5: The average delay with fixed SS configuration on macro layer withvarying SS configurations on micro layer.

Since it is shown in Figures 4.5 that using up to 3 sleep states on the micro layer (purplecurve) give a somewhat acceptable amount of delay, except for when using SS4, thereforeit was utilized to further study the NR standalone deployment. The resulting averagedelay in those scenarios are presented in Figure 4.6. Utilizing more than SS3 on themacro cells resulted in a big delay increase, while with SS0+SS3, SS1+SS3, and SS2+SS3impacts the delay marginally in comparison. SS3+SS3 did give a noticeable delay increaseof about 5%. The delay increase when using SS4 was more than double the baseline(SS0+SS0). Utilizing SS4+SS4 could cause up to 389% delay increase.

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Figure 4.6: The average delay where the micro layer cells have fixed SS3.

4.3.3 10th and 50th percentiles

10th and 50th percentiles show the best case user performance and the median. The10th percentile showed that 90% of the users have either the delay shown in the curve orhigher. The 50th percentile curves are dashed, while the 10th percentile curves are solid.

Figure 4.7 depicts the 10th and 50th percentile delay of 10 Mb file download of LTE-NR deployment with deactivation timers and without. It can be observed that the 10thand the 50th percentile delay curves are rather close to its corresponding baseline whenutilizing SS1 to SS3 on the cells. The difference between the 10th and 50th percentilecurves is about 8% at high loads when using deactivation timers, and up to about 12%when not using deactivation timers, not including SS4. SS4 on cells show a bigger increasein delay for both 10th percentiles and 50th percentiles. Without deactivation timers, theperformance is worse when observing the 10th and 50th percentiles compared to usingthem.

Figure 4.8 depicts the 10th and 50th percentile delay of multiple 1 Mb file downloads withoptimized and non-optimized deactivation timers. Figure 4.8a shows that there is abouta 10% delay increase between the 10th percentile and 50th percentile during low load, andabout 15% delay increase during high load. Both 10th and 50th percentiles do not havebig delay increase compared to its corresponding baseline. Figure 4.8b however, show anincreasing delay compared to the baseline for both the 10th and 50th percentile for allsleep states. Especially SS4 and SS3 of the 50th percentile have higher delay comparedto the other sleep configurations. When observing the 10th percentile, the increase indelay compared to the baseline is about 15% when SS4 is used on cells, while for 50thpercentile the delay increase is about 60%. Without optimized deactivation timers, theperformance is worse when observing the 10th and 50th percentiles compared to usingoptimized deactivation timers.

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(a) With deactivation timers (b) Without deactivation timers

Figure 4.7: The 10th and 50th percentile delay curves of LTE-NR deploymentwith 10 Mb file downloads.

(a) With optimized deactivation timers (b) Without optimized deactivation timers(DA2)

Figure 4.8: The 10th and 50th percentile delay curves of LTE-NR deploymentwith multiple 1 Mb file downloads.

Figure 4.9 shows the 10th and 50th percentile of NR standalone deployment with 10Mb file download. Figure 4.9a and Figure 4.9b show similar results. Overall, the moresleep states used on either macro or micro layer increases even the best user case delay.The 10th percentile curves display that increasing SS on micro layer increases the delay,at most, about 33% for all cell configurations on the macro layer, with the most delayincrease when utilizing SS4 on both macro and micro layer. The median delay resultsshow similar results to the average delay.

The 10th percentile curves when observing Figure 4.9a, Figure 4.9b, and Figure 4.9c,prove the 10th percentile curves and 50th percentile curves do not have a bigger delaydifference than about 17%. Figure 4.9d indicates the biggest delay difference between10th percentile delay and 50th percentile delay is between SS4+SS4 (∼317%). Otherwise,the difference is up to about 11% when utilizing SS1 or SS2 on the micro layer.

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(a) SS1 on macro layer (b) SS2 on macro layer

(c) SS3 on macro layer (d) SS4 on macro layer

Figure 4.9: The 10th and 50th percentile delay with fixed SS configuration onmacro layer with varying SS configurations on micro layer.

4.3.4 90th percentile

Investigations of the 90th percentile was done to study the worst case user performance.The 90th percentile curves show that 10% of the users have either the delay as seen inthe curve or higher.

Figure 4.10 shows the 90th percentage delay of 10 Mb file download of LTE-NR deploy-ment with deactivation timers and without. The worst users do have a bigger delayincrease, but in the case of SS1 to SS3, the delay increase is not much bigger than thebaseline 90th percentage delay.

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(a) With deactivation timers (b) Without deactivation timers

Figure 4.10: The 90th percentile delay curves of LTE-NR deployment with 10Mb file downloads.

Figure 4.11 shows the 90th percentile delay of 1 Mb multiple small file download of LTE-NR deployment with optimized deactivation timers and without. The 95th percentile forthe optimized deactivation timers is shown in Figure 4.12. While Figure 4.11a depictsthat the worst users in all configurations have a small delay increase compared to thebaseline. Figure 4.11b show that with SS2 and SS3, the delay does not deviate too faraway from the baseline delay, but SS4 have a bigger delay increase.

(a) With optimized deactivation timers (b) Without optimized deactivation timers

Figure 4.11: The 90th percentile delay curves of LTE-NR deployment with 1Mb file downloads.

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Figure 4.12: The 95th percentile delay of multiple 1 Mb file downloads withoptimized deactivation timers.

Figure 4.13 simulates the 90th percentile delay of 10 Mb file download of NR standalonedeployment. In all scenarios, using SS4 on micro cell gives the worst delay increase.Figure 4.13a and Figure 4.13b show similar results. As the sleep states increase on themacro cells, so does the additional worst delay. In all scenarios using SS1 and SS2 onthe micro cells does not increase the worst user delay by much when comparing to thebaseline.

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(a) SS1 on macro layer (b) SS2 on macro layer

(c) SS3 on macro layer (d) SS4 on macro layer

Figure 4.13: The 90th percentile delay with fixed SS configuration on macrolayer with varying SS configurations on micro layer.

4.4 Energy Evaluation

4.4.1 LTE-NR dual connectivity deployment

Figure 4.14 shows the total energy consumption, with the red bar representing the macrolayer and the blue bar for micro layer. The corresponding percentage energy savingscompared to the baseline is summarized in Table 4.6. As SS increases, so does theobtainment energy savings.

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Figure 4.14: The total energy consumption of the scenarios in Figure 4.2.

Configurations Energy savings (%)

SS1 25

SS2 w/ DA 43

SS3 w/ DA 51

SS4 w/ DA 52

SS2 w/o DA 43

SS3 w/o DA 50

Table 4.6: Energy savings in percentage for each configuration from Figure 4.14.

Figure 4.15 and Figure 4.16 are the daily energy consumption.

Figure 4.15: The daily energy consumption curves of the scenarios in Figure4.14.

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Figure 4.16: Zoomed in version of Figure 4.15 with the three scenarios with thelowest energy usage.

The total energy consumption of multiple file transmissions using DA1 and DA2 can beseen in Figure 4.17, as well as the corresponding energy consumption of scenarios fromFigure 4.4. The corresponding energy savings between using DA1 and DA2 is found inTable 4.7.

Configuration Energy savings (%)

SS2 DA1-DA2 1.3

SS3 DA1-DA2 1.3

SS4 DA1-DA2 1.2

Table 4.7: The increase of energy savings in percentage between using DA1 and DA2.

Figure 4.17: Total energy consumption of multiple small file transmissions withDA1 and DA2.

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4.4.2 NR standalone deployment

Figure 4.18 depicts the total energy consumption of the scenarios from Figure 4.6, withthe red bar representing the macro layer and the blue bar for micro layer. The corre-sponding energy savings in percentage is summarized in Table 4.8. Figure 4.19 shows thetotal energy consumption of the scenarios from 4.5.

Figure 4.18: The total energy consumption with fixed SS3 on micro layer.

(a) SS1 on macro layer (b) SS2 on macro layer

(c) SS3 on macro layer (d) SS4 on macro layer

Figure 4.19: The total energy consumption with varying SS configurations onmicro layer.

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Configuration Energy savings (%) Configuration Energy savings (%)

SS1+SS1 39 SS3+SS1 51

SS1+SS2 55 SS3+SS2 66

SS1+SS3 59 SS3+SS3 76

SS1+SS4 59 SS3+SS4 76

SS2+SS1 46 SS4+SS1 54

SS2+SS2 64 SS4+SS2 73

SS2+SS3 73 SS4+SS3 81

SS2+SS4 73 SS4+SS4 81

Table 4.8: Energy savings in percentage for each configuration from Figure 4.19and Figure 4.18.

4.4.3 Percentage spent in sleep states

Figure 4.20 shows the average percentage that the micro cell has spent in the differentNR cell states for 10 Mb file download with LTE-NR deployment.

(a) The average total state percentages of thecells.

(b) Total time cells spent in activating state inpercentage.

Figure 4.20: 10 Mb file transmission with LTE-NR deployment.

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Figure 4.21 shows the average time the cells spend either in awake mode or in sleep modefor 10 Mb file download NR standalone as traffic load increases.

(a) The average time spent in awake state (b) The average time spent in a sleep state

Figure 4.21: NR standalone deployment with fix SS4 on macro layer cells.

Figure 4.22 shows the total average percentage the micro cells spends in different cellstates excluding the activation state. This shows the comparison between using opti-mized deactivation timers and not using optimized deactivation timers with LTE-NRdeployment.

Figure 4.22: Average state percentage of cells for small file transmissions withbaseline DA and DA2.

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Figure 4.23 shows the total average percentage the micro cell spends in different NR cellstates excluding the activation state. The figure displays the difference between usingDA1 and DA2 when downloading multiple small files with LTE-NR deployment.

Figure 4.23: Average state percentage of cells for small file transmissions withDA1 and DA2.

4.5 Sensitivity analysis

In this section results are presented from the sensitivity analysis. The adjusted wake-uptimes and sleep factors are presented in Table 4.9. The sleep factor is in percentage ofthe power usage. All simulations in this section are done with 10 Mb file downloads withLTE-NR deployment.

Sleep state NR1 NR2 NR3 NR4

Wake-up time (ms), A=5 1 5 25 125

Wake-up time (ms), A=15 1 15 225 3375

Wake-up time (ms), A=20 1 20 400 8000

Sleep factor, B=1.5 1/1.5 1/1.52 1/1.53 1/1.54

Sleep factor, B=3 1/3 1/32 1/33 1/34

Table 4.9: The updated wake-up times and sleep factor used in sensitivity analysis.

The transmission delay when adjusting the wake-up time with factor A can be observedin Figure 4.24. With an increasing factor A, there is also an increase in delay, especiallywith the deepest sleep state SS4. When A=20, the delay increase with SS2 is minimal,while using SS3 gives an increase in delay of about 10%. When A=15, the delay increasewith SS3 is about 2.5%, and SS2 is even smaller. When A=5, the increase in delay withall configurations are very small. The increase in delay is about 1%.

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(a) A=20 (b) A=5 and A=15

Figure 4.24: Sensitivity analysis of delay when adjusting wake-up time.

Figure 4.25 depicts the total energy consumption with the energy savings summarized inTable 4.10.

Figure 4.25: Sensitivity analysis of total energy consumption with A=5 andA=15.

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Configuration Energy savings (%)

SS2-SS3 (A=5) 15

SS3-SS4 (A=5) 9

SS2-SS3 (A=15) 15

SS3-SS4 (A=15) -17

SS2 (A=5) - SS2 (A=15) 0

SS3 (A=5) - SS3 (A=15) 0

SS4 (A=5) - SS4 (A=15) -28

Table 4.10: The energy savings in percentage between configurations fromadjusting wake-up time.

Figure 4.26 depicts the total energy consumption with the energy savings summarized inTable 4.11.

Figure 4.26: Sensitivity analysis of the total energy consumption with differentsleep factors, B.

Figure 4.27 show the total average percentage micro cells spend in different NR cell stateswhen performing the sensitivity analysis.

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Configuration Energy savings (%)

SS1-SS2 (B=1.5) 18

SS2-SS3 (B=1.5) 14

SS3-SS4 (B=1.5) 5

SS1-SS2 (B=3) 25

SS2-SS3 (B=3) 11

SS3-SS4 (B=3) -5

SS1 (B=1.5) - SS1 (B=3) 28

SS2 (B=1.5) - SS2 (B=3) 34

SS3 (B=1.5) - SS3 (B=3) 31

SS4 (B=1.5) - SS4 (B=3) 24

Table 4.11: The energy savings in percentage between configurations fromFigure 4.11

.

Figure 4.27: Sensitivity analysis of state percentage with A=5 and A=15.

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Chapter 5

Discussion

5.1 Deactivation timers’ impact

The FTP file transmission is transmitted in parts, meaning when downloading a 10 Mbfile there are intervals of about 100 ms between transmission bursts during the TCPslow-start. Choosing the deactivation timers depend on the time between bursts, howmuch delay is acceptable, and how much energy saving is desired. Without choosing theappropriate deactivation times the negative user impact will be more significant thannecessary. In Figure 4.1 and Figure 4.2 where no deactivation timers were used, it wasobserved that the cell take a longer time to activate, which in turn has a larger impacton the users.

The optimized deactivation timers make sure that the cells do not go to deeper sleepduring the intervals in-between the bursts since that is the major cause of additionaldelay. When deactivation timers have been optimized for the traffic, most of the delayinstead comes from when cells are sleeping after transmitting the whole file and then gettriggered by a new user to wake up i.e. the initial transmission delays.

The impact on the latency is evident in Figure 4.3 when both non-optimized deactivationtimers and optimized deactivation timers were used, with multiple 1 Mb file downloads.When comparing the two figures, the delay is longer for non-optimized deactivationtimers. Instead of using the baseline deactivation timers (non-optimized) which is op-timized for 10 Mb file download, DA2 (optimized) was used instead. DA2 is optimizedfor the 1 Mb file downloads. This means that the traffic does not reach such deep sleepin-between the file transmissions, this ensures that the additional activation delay is nottoo long. Another point to observe, is that the utilization of non-optimized deactivationtimers are better than not using any deactivation timers at all.

These deactivation timers can be optimized depending on what traffic model is used. It isstrongly recommended to implement the deactivation timers, and if possible to optimizethem. This could be difficult in real life scenarios as it is hard to know what traffic is tobe expected.

5.2 Performance evaluation discussion

Generally, the delay for all scenarios rise with an increasing load caused by a growingnumber of users that all want to use the limited resources. Transmissions are queued

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until they get transmitted, leading to a longer queuing delay.

When evaluating the performance using the deepest sleep state (SS4) on either the macroor micro layer, it can be seen that it has the most significant impact on the delay comparedto the other configurations. SS3 have less of an impact on users, but is still noticeablein some cases which could be acceptable depending on how much delay is allowed. Forexample, some traffic models can accept more delay since the user will not notice theadditional delay, like in the case with video streaming. Video streaming has the possibilityof transmitting a significant part of the data in advance to buffer up the video on theuser side, thus allowing for long time between transmissions. It is possible to accepteven the latency from utilizing SS4, if the traffic model is not delay sensitive. The lessdelay sensitive a traffic is, the more sleep could be utilized to save more energy. Othertraffics might not accept this additional delay though, like voice calls. In those cases, itmight still be possible to use sleep states that are lower than SS3. From the performanceresults, it can be observed that using SS1 and SS2 on both macro and micro layer inboth LTE-NR deployments and NR deployments give minor latency impact. The use ofcell sleep should be optimized according to what the requirements for the performanceis desired.

As previously mentioned, if the cell often reaches deep sleep in-between transmissions,then the cell will spend a lot of time in an activation state, leading to additional timeadded to the activation delay. This is also depicted in Figure 4.20a, where SS4 cell spendsless time in a sleep state, and more in an activating state compared to the other scenarios.The other scenarios also spend time in an activating state, but it is so marginal that itis not noticeable when looking at the averages. Observing Figure 4.20b, it shows moreclearly how much time in percentage the cells spend in an activating state. For growingload, the total average time spent in the activating state increases, especially for SS4.

Figure 4.20a also shows that the percentage of lighter sleep is much smaller than deepersleep. Despite this, it does make a significant difference in performance when utilizinglighter sleep than to not, as seen in Figure 4.2. This is because of the transmission burstswhere cells end up not reaching deep sleep between them, and only enter deep sleep whencell has finished transmitting the file. Therefore, the cell spends most of its time in thedeepest sleep it can reach.

Observing the average delay of NR deployment with SS4 on the macro layer in Figure4.5d, the delay decreases from the 200 Mb/s/km2 traffic load mark, which is the oppositeof what happens in the other NR standalone scenarios. This reason is after a certainpoint, a lot of congestion occurs caused by the built-up transmissions when using deepersleep states. The cell stops entering deep sleep and are forced to stay in the lighter sleepbecause of the many active users in the cell. Thus, the cells do not get a chance to enterdeeper sleep, and the total average delay decreases as the cells spends more time in anawake state. It can be seen in Figure 4.21, as the traffic load increases the time spentin an awake state grows and time spent in sleep lessens. Note that in the figure, theSS4+SS1 curve is right behind the SS4+SS2 curve.

5.2.1 Best case users and the median discussion

When investigating the best case users, observed is that the delay impact is small, evenin the case of SS4+SS4 configurations. Studying the NR standalone case which has beenshown to have more delay impact, even with additional SS on either macro or microlayer, it does not increase the best case user delay by much when comparing to the othersleep configurations, as seen from Figure 4.9. The best case delay curves for the NR

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deployment have about the same value for all of the four figures. Most delay impact tothe best case users occur when SS4 is used on the macro layer, albeit, it is smaller thanthe overall average. The increasing load does not seem to affect the user delay increasebetween the baseline and the cells with sleep states implemented by much either. Thebest case users probably triggered a scheduling of file transmission when the cell wasalready active or in very light sleep like SS1, therefore it does not suffer much additionalactivation delay.

Figure 4.7 and Figure 4.8, depicts not using deactivation timers give larger best case delayincrease, compared to the best case delay when using deactivation timers in addition withdeep sleep on both macro and micro layers. Even when investigating the best case users,using deactivation timers was better than not using them.

The median delay are similar to the results from its corresponding average delay, indicat-ing that the delay results are evenly distributed. When comparing how close the mediandelay and the best user delay of LTE-NR deployment is, they are not too different invalue, excluding when using SS4 on micro layer with 10 Mb download and the cases,where non-optimized deactivation timers were used. The same goes for NR deployment,but additionally excluding the SS4+SS3 configuration. This means that at least 50%of the users have small delay impact of implementing up to SS3 on cells when usingoptimized deactivation timers, excluding SS4+SS3.

5.2.2 Worst case users discussion

When investigating the worst case users, they do not differ much from the baselineworst case users in terms of delay, except for in some cell configurations. For the worstcase users, even with additional sleep states, it does not worsen the performance impactsignificantly. This can be seen for both LTE-NR deployments and NR deployments. Mostdelay increase for worst case users of LTE-NR deployment occur when SS4 is configuredon the micro layer with 10 Mb file download. For multiple 1 Mb downloads, the worst userdelay increase (with sleep) is not much bigger than the baseline worst user delay (withoutsleep) for all configurations. This means that the sleep algorithm with light sleep impactsthe worst case users minimally. The NR deployment show the cell configurations thatcause the biggest worst case delay, occurs when SS4 is configured on the micro layer forall configurations on macro layer, compared to the baseline. The SS4+SS3 configurationalso show increased worst case delay.

To really investigate how the worst case users affect the overall results, it should becompared to the average users. When comparing the 1 Mb file download delay results,one can see a difference in delay increase. This can be observed in Figure 4.11a and Figure4.3b, which depicts the LTE-NR deployment with 1 Mb file downloads. The worst caseusers have a lot higher delay increase. The cause for the higher delay is most probablefrom when users enter cells when they are in deep sleep, or there are a higher numberof users that manage to avoid cells in deep sleep, leading to better average delay results.Comparing the 10 Mb file download worst case users, the delay results does not deviatetoo much from the average users as seen from Figure 4.2 and Figure 4.10a. Meaningthe sleep states does not affect the worst case users too badly. The same goes for theNR deployment case. This means the majority of the users in those cases experiencesomewhat good performance with a small delay impact when using lighter sleep.

The delay difference with the 1 Mb file downloads between SS4 and the baseline sleepconfigurations in Figure 4.3, is smaller than other scenarios with 10 Mb file downloads,so for this traffic, less users have longer worst case delay. Increasing the percentile to the

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95th percentile shows a bigger increase in delay between SS4 and the other configurations,as seen in Figure 4.12.

5.3 Energy evaluation discussion

When it comes to a balanced sleep configuration for both dual connectivity and stan-dalone deployments, using SS3 on the micro layer is a good choice. It gives a balancedresult between energy savings and latency.

In the NR standalone cases, using SS1 on the macro layer to make sure coverage isensured, gives a smaller delay impact, while in most cases saving more energy than whenusing LTE-NR deployment. This is quite promising when considering energy efficiencyin the future with NR standalone deployment.

It is worth noting in the LTE-NR deployment case, the macro cells tend to use moreenergy as traffic load rises, as well as an growth seen in the number of sleep states. Thisis caused by the fact that the micro cells sleep more and longer when more SS are used,while the macro cells keep getting overloaded. With increasing traffic load the macrocells must use up more resources to transmit all data compared to if the micro cells wereactive and transmitting too. This could lead to more energy usage on the macro layer,which in turn leads to less energy decrease as SS4 is implemented. This could be seenin Figure 4.14, where using SS4 on the micro layer show that the macro layer cells usemore energy compared to the other configurations.

Since most energy could be saved when cells are sleeping, it is expected that if cell cansleep deeper it should also be able to save more energy. However, that is not entirelytrue for all sleep configurations. When comparing the total energy consumption of LTE-NR deployment between adding an additional sleep state to the configuration of thescenario, it does save additional energy, but between using SS3 and SS4 is where itusually stops giving additional energy savings. Of course more energy is saved whenusing SS4 compared to lighter sleep like SS1 and SS2. Investigating the energy savingsfrom Table 4.6, proves that there is a very small percentage energy savings betweenSS3 and SS4. This could be explained by investigating the daily energy consumption.The daily energy consumption curve from Figure 4.16 shows that for low traffic loads,SS4 w/DA consumes less energy compared to SS3 w/DA, and more for higher loads.Therefore, the total energy consumption for these configurations as seen in Figure 4.14are about the same since the total energy usage with SS4 levels itself out. Less energyis decreased with using SS4 when higher loads are reached, since the cells are forced tostay awake to be able to service all the users. There is a lot of accumulated data queuedso that when cells are active, it takes longer to transmit everything before resumingsleep. Although, having a lot of queued transmissions is not necessarily bad for energyefficiency. It is when the base station is overloaded and it can not handle all the queuedtransmissions simultaneously, where it could lead to bad energy efficiency. As long as thebase station does not get overloaded to the point of impacting the performance negativelyby too much, utilizing deep sleep is energy efficient. The same could be said about the NRstandalone case when the macro layer base stations start using more energy comparedto the other configurations as seen from the results in Figure 4.19.

Considering the daily energy consumption, it is pretty energy efficient to use higher SSat low loads. From Figure 4.15 where the daily energy consumption can be investigated,it can be observed that for all configurations at the 5th hour, they have the lowestenergy usage. Highest energy usage occurs at higher traffic loads, at the 22nd hour.

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Taking advantage of this it could be beneficial to use deeper sleep states for low loadsfor increased energy efficiency, if the traffic is not too latency sensitive.

The multiple small file transmission simulations also show that it is possible to controlhow much time is spent in each sleep state to some degree. This information can beused to know what state to optimize energy savings from. Comparing using optimized(DA2) and non-optimized (baseline) deactivation timers from Figure 4.22, the averagepercentage spent in SS1 is bigger for the optimized deactivation timers compared to nothaving optimized deactivation timers. This is because to enter the 2nd sleep state itonly requires 100 ms for the non-optimized deactivation timer, and for the optimizeddeactivation timer it would take 500 ms which is longer. Note that time in sleep andactivation state has been grouped together in the figures. Therefore, cells spend longertime in 1st sleep state when using optimized deactivation timers.

Adjusting the deactivation timers while still using optimized deactivation timers (DAfrom Table 4.2) also changes the delay as seen from Figure 4.4. Both DA1 and DA2 isoptimized for multiple small file transmissions but they have different timestamps beforeentering SS2 (NR2). Depending on how much performance degradation is allowed, thedeactivation timers could be chosen accordingly to save more energy. For example, inFigure 4.23 which shows the state percentage for 1 Mb file downloads utilizing DA1 andDA2, it can be seen that for configurations where DA1 is used, the cells spend more timein 2nd sleep state than when using DA2. Since both deactivation timers are optimized,it is guaranteed that the delay increase will not be too extreme. The configurationsusing DA1 should have less energy usage compared to DA2, and this is confirmed byTable 4.7. With the current power model, the increase in energy savings is rather small,but it should be noted that only one deactivation time was changed between DA1 andDA2. This proves that it is possible to further optimize energy efficiency by adjustingthe deactivation timers.

5.4 Sensitivity analysis discussions

A sensitivity analysis was done to see how the activation time and sleep factors in thepower model affects the latency and the energy consumption. As seen from Figure 4.24,adjusting the activation times show that the longer the activation time, the more thedelay grows. Also, adjusting the sleep factors show that with a smaller sleep factor whenincreasing B gives more energy efficient cells, as seen from Figure 4.26.

Shorter activation time gives the cells a chance to enter all sleep states without impactingthe latency significantly, even with SS4. However, increasing the activation time doesthe opposite. Using SS1 and SS2 even with a different factor A displays about the sameresults, but with a larger A, comes a big increase in delay when using SS3 and SS4.

The state percentages shown in Figure 4.27 proves that with an increased factor A thereis especially a rise in time spent in activation state when observing SS4. This is alsoreflected in the delay. Investigating the time spent in an awake state with SS4, it can beseen that the cells spend less time in that state compared to the other configurations.This would mean that the cells that reach deeper sleep are not able to reach an activestate when triggered to wake up. Both the additional activation delay from the longwake-up time, and the congestion that occurs because of the increased activation delay,leads to worse overall user performance.

In Figure 4.25 when using A=5 depicts a decrease in energy consumption for everyaddition of a sleep state. When using A=15, less energy is consumed between SS2 and

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SS3 but a pretty big rise in energy usage between SS3 and SS4. This is mainly caused bythe long activation delay from using longer activation times, this causes congestion anda lot of built-up transmissions, leading to the macro cells having to transmit a lot more,while the micro cells are still in the process of activating. This can be seen in the higherenergy usage of the macro cells (the orange bar) compared to the other configurations.So, utilizing a smaller activation time factor A is more energy efficient than using a higherfactor when using deeper sleep state. Between the other sleep configurations the factorA did not affect the energy savings. Thus, implementing the 4th sleep state should bedone with caution.

Investigating the results from adjusting the sleep factor shows that there is energy tobe saved independent of how the sleep states have been implemented. The sensitivityanalysis of a 10 Mb file download with LTE-NR deployment with different factor B isdepicted in Table 4.11, it shows that when B=3 energy usage is higher with SS4 comparedto SS3. Most likely the cause of this is the increase of energy usage in the macro layercells, which is the same as the reasoning explained previously. This means using SS3is more efficient than SS4 in that case. When changing the sleep factor to B=1.5, itis overall more energy efficient than B=3, increasing the energy savings by about 30%.How the sleep states are optimized is important as between SS1 and SS2 using B=1.5give smaller energy savings compared to B=3, but the opposite when comparing SS2 andSS3.

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Chapter 6

Conclusion

DeploymentEnergy saved(minimal delay impact)

Energy saved(high delay impact)

LTE-NR 43% with 1% delay increase 52% with up to 90% delay increase

NR-NR 73% with 1% delay increase 81% with up to 390% delay increase

Table 6.1: Summarized delay and energy saving results.

It has been shown that it is possible to save energy while keeping the impact on theusers small, even with a more conservative power model. If very minimal impact on theusers is desired, then lighter sleep should be utilized. As seen from Table 6.1 additionalenergy could be saved if more delay is acceptable. Overall, using NR standalone deploy-ment saves more energy compared to LTE-NR deployment while keeping the user impactminimal.

There is a trade-off between using additional sleep states. Deeper sleep leads to lessenergy usage, but at the risk of higher latency. When adding additional sleep states tosave more energy, it is essential that they are implemented in a smart way, which couldbe done with deactivation timers. Otherwise the congestion could cause both increasedenergy usage and latency. Without deactivation timers the increased delay could beas bad as a ∼35% delay increase, when it with deactivation timers would only be ∼2%(comparing SS3 w/o DA and SS3 w/DA).

Generally, as the traffic load increases, so does latency because of congestion. For hightraffic loads, cells use more energy to service all the users, and less during low loads asthere are more opportunities for cells to sleep. When utilizing higher sleep states it isless energy efficient during high loads, compared to using lower sleep states. In contrast,during low loads, it is more energy efficient with higher sleep states but it comes with ahigher delay impact.

Adjusted sensitivity factor Result

Shorter wake-up times Less delay increase

Longer wake-up times Higher delay increase

Higher sleep factor Higher energy usage

Lower sleep factor Less energy usage

Table 6.2: Results from sensitivity analysis.

The sensitivity analysis of the power model shows that even when changing the wake-uptimes it does not affect the energy usage except for when utilizing SS4 configuration,

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where the congestion causes an energy increase. Results are summarized in Table 6.2.Careful implementation is necessary when adjusting both the wake-up time and sleepfactor.

The sleep cycle algorithm does not increase the baseline worst user performance by muchexcept for in the case of using SS4, which has already been shown to give high delayimpact in almost all scenarios and deployments. Up to at least about 90% of the usersexperience a minimal impact on the delay when utilizing lighter sleep states on cells.Deactivation timers are necessary and should be optimized for the traffic, otherwisethere are worst case users that experience high negative delay impact. The best caseusers give minimal delay impact even when deepest sleep is utilized on either macro ormicro layer.

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Chapter 7

Future work

For future work, the sleep cycle algorithm needs to be applied to other scenarios likeurban and rural scenarios. Different types of traffic scenarios should also be simulatedtogether with the algorithm to see how that affects the users and the energy usage. Itwould also be possible to build upon the baseline algorithm to utilize traffic to knowwhen to put the cells to a deeper sleep state. The algorithm would then investigate whatperformance requirements the traffic needs, and from that, decide what sleep states isappropriate to use. This way cells can be put to a deeper sleep state almost immediatelyto save energy and wake up on time to not add additional activation delay.

For further improvements, a machine learning algorithm could be applied to anticipatefuture traffic and have less of an impact from the initial transmission delay. Also, in thefuture as the base station components could be grouped together in smaller numbers tocreate more sleep states, more investigations could be done using more than four sleepstates.

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