Energy efficient multi-connectivity for ultra-dense networks

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Erasmus Mundus Master’s Programme in Pervasive Computing & Communications for sustainable DevelopmentPERCCOM

Valentin Poirot

ENERGY EFFICIENT MULTI-CONNECTIVITY FOR ULTRA-DENSE NETWORKS

2017

Supervisors: Mårten Ericson(Ericsson Research) Mats Nordberg(Ericsson Research)

Associate Professor Karl Andersson(Luleå University of Technology) Examiners: Professor Eric Rondeau(University of Lorraine)

Professor Jari Porras(Lappeenranta University of Technology) Associate Professor Karl Andersson(Luleå University of Technology)

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This thesis has been accepted by partner institutions of the consortium (cf. UDL-DAJ, n^o1524, 2012 PERCCOM agreement).

Successful defense of this thesis is obligatory for graduation with the following national diplo- mas:

• Master in Complex Systems Engineering (University of Lorraine)

• Master of Science in Technology (Lappeenranta University of Technology)

• Master of Science (120 credits) - Major; Computer Science and Engineering, Speciali- sation; Pervasive Computing and Communications for Sustainable Development (Luleå University of Technology)

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Luleå University of Technology

Department of Computer Science, Electrical and Space Engineering PERCCOM Master Programme

Valentin Poirot

Energy Efficient Multi-Connectivity for Ultra-Dense Networks

Master’s Thesis 2017

87 pages, 23 figures, 9 tables

Examiners: Professor Eric Rondeau(University of Lorraine)

Professor Jari Porras(Lappeenranta University of Technology) Associate Professor Karl Andersson(Luleå University of Technology)

Keywords: energy efficiency; multi connectivity; ultra dense network; 5G; secondary cell association; reliability

In 5G systems, two radio air interfaces, evolved LTE and New Radio (NR), will coexist. By using millimeter waves, NR will provide high throughputs, but the higher frequencies will also lead to increased losses and a worse coverage. Multi-connectivity is therefore envisioned as a way to tackle these effects by connecting to multiple base stations simultaneously, allowing users to benefit from both air interfaces’ advantages. In this thesis, we investigate how multi- connectivity can be used efficiently in ultra-dense networks, a new paradigm in which the number of access nodes exceed the number of users within the network. A framework for secondary cell association is presented and an energy efficiency’s condition is proposed. Upper and lower bounds of the network’s energy efficiency are analytically expressed. Algorithms for secondary cell selection are designed and evaluated through simulations. Multi-connectivity showed an improvement of up to 50% in reliability and and an increase of up to 20% in energy efficiency.

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Luleå, May 31, 2017

I would like to express all my gratitude to the PERCCOM consortium [1] which allowed me to participate in this unique adventure for the past two years. It was a life-changing event that was only possible by the help of all the people involved in the project.

Special thanks to Eric Rondeau, Jari Porras, Karl Andersson and Jean-Phillipe Georges for everything they did for PERCCOM: all the activities organized, the classes given and their time.

I would also give a special mention to Karl for his assistance during the thesis.

I would also like to thanks Mats Nordberg and Mårten Ericson for allowing me to pursue this thesis with them and for their continuous support throughout the work. Thanks to all the other people working here at Ericsson, for their feedback and the coffee breaks.

And, of course, a special mention to all my classmates with whom I shared wonderful moments during those past two years: Victor, Tamara, Chandara, Joseph, Emil, Rafiul, Manish, Felipe, Atefe, Carlos, Mustaqim, Henrique, Olga, Giang, Nhi and Aigerim. To them, I want to say Bonzon!

Valentin Poirot

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List of Figures

1 Envisioned Traffic Growth, from [2] . . . 10

2 5G Radio Access Technologies . . . 17

3 Heterogeneous Network (a) and Ultra-Dense Network (b) . . . 19

4 Multi-Connectivity Architecture . . . 22

5 Inter-frequency MC (a) and intra-frequency MC (b) . . . 23

6 Power Consumption in Cellular Networks . . . 25

7 Power Model for LTE . . . 26

8 Sleep Mode Levels, adapted from [57] . . . 28

9 Handover Procedure . . . 33

10 Sleep Mode Model for 5G . . . 35

11 Deployment Strategy . . . 37

12 Association Procedure Flowchart . . . 40

13 Disassociation Procedure Flowchart . . . 41

14 Radio Link Failure Rate depending on the user velocity . . . 63

15 Linear Regression of the Simulated RLF Rate . . . 64

16 Standard Deviation of the RLF Rate . . . 65

17 Simulation results distribution depending on the velocity and number of connections . . . 65

18 System Power Consumption . . . 67

19 Percentage of Sleeping Cells . . . 68

20 System Energy Efficiency . . . 69

21 Results Distribution . . . 71

22 Simulated Results and Theoretical Lower Bound for Energy Efficiency . . . 72

23 10^th Percentile User Throughput . . . 74

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List of Tables

1 Handover Parameters . . . 33

2 Radio Link Failure Parameters . . . 34

3 Network Parameters . . . 36

4 Users Parameters . . . 38

5 Traffic Model . . . 38

6 Multi-Connectivity Schemes Parameters . . . 42

7 Scenario Parameters . . . 62

8 Deployment Parameters . . . 66

9 Probability of Multi-Connectivity . . . 73

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ABBREVIATIONS AND SYMBOLS

3GPP Third Generation Partnership Project

5G Fifth Generation of Mobile Communications 5GPPP 5G Public Private Partnership

BS Base Station

CoMP Coordinated MultiPoint DC Dual Connectivity

DL Downlink

EE Energy Efficiency

ETSI European Telecommunications Standards Institute FTP File Transfer Protocol

HetNets Heterogeneous Networks

ITU International Telecommunications Union LTE Long Term Evolution

LTE-A Long Term Evolution Advanced MC Multi-Connectivity

METIS Mobile and wireless Enablers for the Twenty-twenty Information Society MIMO Multiple Input Multiple Output

mmWave Millimeter Waves

NR (5G) New Radio

RAT Radio Access Technology RLF Radio Link Failure

SINR Signal over Interference plus Noise Ratio UDN Ultra-Dense Networks

UE User Equipment

UL Uplink

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1 Introduction

Throughout the world, mobile networks are being more and more used. It is expected that there will be a total of 8,900 million mobile subscriptions in 2022, smartphones accounting for 6,800 million of the total [2]. Moreover, the traffic growth is also following an exponential growth. In 2022, it is expected that the total mobile traffic will increase by a factor of 10, with video traffic making most of it.

Figure 1.Envisioned Traffic Growth, from [2]

Tackling this rise of traffic has become the priority of many actors in telecommunications. Mul- tiples projects and collaborations are carried out around the globe to investigate, discuss and prepare the next generation of mobile communications, 5G.

Moreover, 5G will have to fully support new use cases. Machine-type communications, coming from the Internet of Things, driverless cars or the industry will require very low latency and high reliability. Accurate positioning will be an important service for localized and personalised applications, but also for emergency responses. High reliability should also be ensured for high velocity vehicles, such as trains. Finally, 5G should provide acceptable quality of experience even in highly dense areas, like stadiums or shopping malls [3].

Current developments of 5G follow two tracks: an evolution of LTE, which will improve its capacity while ensuring backward compatibility, and a new radio access technology, called New radio (NR), which will work at higher frequencies to provide improved throughputs. [4]

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With higher frequencies also come low coverage, leading to a need for more serving points. It is expected that the network densification will be so important that the number of access nodes will exceed the number of active users within the network. This paradigm is called ultra-dense network, and will allow optimal throughput for the user, regardless of its location. Further- more, multi-connectivity will allow users to connect to multiple access points at the same time.

This will allow them to benefit from the coverage of LTE and the performance of New Radio simultaneously, at the cost of more active base stations for the network.

1.1 Problem Definition

Energy consumption is becoming more and more a problem for network operators. In 2011, base stations alone were representing 4.5 GW of power and 20 Mt of CO₂ per year [5]. Since that, the figures has been constantly increasing. In terms of costs, this equates to up to 18% of operational expenses in Europe, and up to 32% in India for that same year [6]. Energy efficiency has therefore been targeted at the international level as one of the key capabilities of 5G [3].

Ultra-dense networks, where tremendous number of base stations are deployed in a never seen manner, will probably cause a pike of energy consumption in specific areas. Energy-efficient hardware and efficient sleep procedures will be critical in urban areas. However, at the same time, it is envisioned that multi-connectivity could be used to provide better quality of experience (QoE). Users could then connect to multiple access nodes, thus aggregating additional bandwidth for high data rate and improving reliability. That feature could undermine energy reduction techniques put into place. A trade-off between QoE and power consumption is therefore critical.

The impacts of 5G in terms of sustainability are not yet known. On one hand, most predictions tend to believe the energy consumption of the network will increase worldwide, while a lot of research has been done recently towards more energy efficient solutions. Through the study of energy efficiency, the impacts of these new technologies should also be assessed with regard to sustainability.

1.2 Objectives and Research Questions

This thesis focuses on studying multi-connectivity. First, we investigate its effects on the network performance. We do it both theoretically and by simulation. The second part of our work

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consists in designing new algorithms for multi-connectivity association. Specifically, we give a particular attention to the system’s energy efficiency, and try to improve it.

We will answer the following research questions:

1. How does multi-connectivity affect the mobile network performance?

(a) What is the impact of multi-connectivity on the power consumption?

(b) How multi-connectivity affects the energy efficiency of the system?

(c) How can multi-connectivity improve reliability for the user?

2. How can we design Multi-Connectivity schemes to optimize the system performance?

(a) What common characteristics define multi-connectivity schemes?

(b) How can we compare different schemes?

(c) How can we estimate energy efficiency when choosing a secondary cell?

3. How do these results translate in terms of sustainability?

We will answer these questions in four steps. After a survey of the state of the art, we will design algorithms for secondary cell association aimed at focusing the energy efficiency of multi- connectivity. We will then perform a numerical analysis of energy efficiency and evaluate our proposals through simulations. Finally, we will look at the power consumption and the energy efficiency of networks implementing our solutions to establish the impact of multi-connectivity in terms of sustainability.

As 5G networks are only deployed in laboratories at the time of this study, actual measurements are not possible and we therefore decided to use a simulation tool in the evaluation of the work.

Models used by our simulator are explained later in this thesis and are based on others’ work from empirical measurements made on real life equipment.

As one focus of this work is to design energy efficient solutions, we adopt a Green IT approach of sustainability. The energy consumption and energy efficiency of the system are our most important metrics related to the problem, and we deduce from them direct impacts on carbon emissions and costs. Other indirect impacts, or rebound effects, are discussed when possible.

Therefore, we consider only two of the three main aspects, or pillars, of sustainability, related to economical and ecological challenges.

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1.3 Scope

In this thesis, we focus on the usage of multi-connectivity within ultra-dense networks. This means that this work takes place in dense, urban areas. We use a combination of macro cells and small cells, which are explained further in this thesis. Moreover, a FTP traffic model is used to represent Internet-related user traffic within the network.

Furthermore, we propose several algorithms that are evaluated in this work. These algorithms treat the cell selection for secondary cell association and disassociation. We do not propose here new procedures (i.e. communications between BSs and UEs) for cell association and resource scheduling, as these parts will be defined during the standardization process.

1.4 Thesis Structure

The thesis is structured as follows:

Chapter 2 - Background and Related Work

All important aspects necessary for the good understanding of the thesis are presented here.

After a brief introduction of the history of mobile communications, a brief tour of 5G and its most promising technologies is given. The key terms of the title are then defined, and a survey of the literature is carried out.

Chapter 3 - Models and Multi-Connectivity Algorithms

This chapter first introduces our simulator. The different models used and implemented throughout our work are explained. The second part presents our framework for multi-connectivity.

Multiple algorithms are introduced and their functionalities are detailed.

Chapter 4 - Analytical Study of Energy Efficiency

The analytical work is shown in this chapter. A general expression of multi-connectivity is given, and its evaluation for energy efficiency is carried out. An approach to power savings is also expressed in this chapter, and a study of several algorithms is performed.

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Chapter 5 - Results

The work is evaluated in chapter 5. After presenting the methodology, results are presented for each scenario.

Chapter 6 - Summary and Discussions

A summary of the main findings is done in chapter 6. A discussion of the results is also done.

Chapter 7 - Conclusion and Future Work

Finally, we conclude this thesis in chapter 7. Our contributions are highlighted and potential tracks are given for future studies.

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2 Background and Related Work

This chapter encompasses fundamentals notions that are needed for the good understanding of this thesis as well as the state of the art of the field. 5G is presented in 2.1 through its technical solutions. Sections 2.2 and 2.3 define ultra-dense networks and multi-connectivity respectively.

A review of the literature is given for these two concepts. Previous works related to energy efficiency are then listed in section 2.4. Finally, an overview of the different metrics used in the literature is given in section 2.5.

2.1 5

^th

Generation of Mobile Communications

During the past ten years, multiple projects spawned worldwide to study and develop the next generation of mobile communications. In America, Europe or Asia, there is a push for national or continental efforts to lead its development. At the international level, the International Telecommunications Union (ITU) started planning the IMT-2020 specification in 2014, for a release in 2020.

2.1.1 The European Vision

The European Union decided to invest a lot in the development of the next generation of mobile communications. The METIS project (Mobile and wireless Enablers for the Twenty-twenty (2020) Information Society) is a FP7 funded collaboration between industry and academia started in 2012 and finished in 2015. Its main objective was to lay the foundation for the future of mobile communications, by bringing a consensus between multiple European actors in prevision of the international standardization. With 29 partners including manufacturers, telecommunications operators, automotive industries and academia, they successfully managed to propose a unified vision of what 5G should look like by stating challenges and requirements that must be met by the new technology in order to be considered a success.

In February 2014, The European Commission and the European ICT industry jointly launched The 5G Public Private Partnership (5G-PPP) under the Horizon 2020 funding framework. Its objective is to investigate and produce solutions, architectures and standards for 5G.

A few defining attributes were stated by 5G-PPP [7]:

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• Amazingly fast

• Great service in a crowd

• Best experience follows you

• Super real-time and reliable connections

• Ubiquitous things communicating

5G is thus not only envisioned as an improvement of LTE, with higher capacity and increased quality of service, but it should also cover new use cases that were not present before. Pervasive computing, IoT, connected vehicles and the industry 4.0 are a few of these new applications that must be served in 5G.

In order to evaluate potential technologies, these scenarios were also transposed into technical requirements. 5G must not only deliver good results during in first deployment, around 2020, but must also be able to evolve as the traffic grows in the following years.

The METIS 5G requirements were :

• “1000 times higher mobile data volume per area,

• 10 to 100 times higher number of connected devices,

• 10 to 100 times higher user data rate,

• 10 times longer battery life for low power massive machine communication (MMC), and finally, and

• 5 times reduced end-to-end latency” [8]

These requirements might not need to be satisfied all at once. Indeed, with the appearance of the scenarios listed before, only a few of them will be required at once. These led to the division of 5G into three main services: (i) extreme mobile broadband, (ii)massive machine- type communications and(iii)ultra-reliable machine-type communications.

These services, as well as the attributes defined by 5G-PPP were refined in twelve test-cases, each of them aiming at representing one possible deployment and utilisation of 5G. The entire list can be found at [9]. We can, as an example, citeShopping mall,Emergency Communications orMassive deployments of sensors and actuators.

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At the international level, the International Telecommunications Union (ITU) published a draft for the technical requirements for IMT-2020, the technological family for 5G [10].

2.1.2 Technical Solutions

As explained in the introductory chapter, 5G will be composed of two radio access technologies (RATs): an evolution of LTE, andNew Radio(NR) [11]. We list here few of the most promising tracks to achieve the technical requirements fixed by the ITU. Figure 2 represents 5G RATs.

Figure 2. 5G Radio Access Technologies

The main concept of NR is to use higher frequency bands. Most of the studies consider the millimeter waves as the best candidate. mm-waves refer to the frequencies in the 3-300 GHz [12], and is composed of the Super High Frequency (SHF) and Extremely High Frequency (EHF) bands, with wavelength between 1 to 100 mm. Numerous frequencies were also proposed within this range, and theoretical and experimental studies have been carried out to determine which is the most suitable. Amongst them, we can cite the 15Ghz band [13], the 28 and 38 GHz bands [14, 15] and the 60 GHz band [16, 17]. A late consensus seems to indicate the 28 GHz as the most suitable candidate in the beginning. However, the ITU might select multiple bands during the IMT-2020 standardisation procedure.

It can also be worth noting that unlicensed spectrum might be available in 5G, as it was with LTE-Unlicensed [18]. It might however only be complementary, as opposed to the new frequency band which will play an important role.

The second most promising technology is the usage of massive MIMO (multiple input multiple

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output) [19, 20, 21]. In conventional point-to-point MIMO, base stations and user equipment are equipped of more than one receiver and transmitter. MIMO can be used for precoding, where all the transmitters emit the same signal in order to increase the received signal power at the receptor. MIMO is also used for spatial multiplexing, where each transmitter emits a part of the message at the same time in order to increase the bitrate. However, MIMO technology was mainly used for one user at a time [22]. Massive MIMO is a scaled-up MIMO, with hundreds of antennas elements in one base station, with the capacity to serve hundreds of users at the same time.

Massive MIMO offers numerous advantages. The usage of cheap low-power transceivers makes it energy-efficient, secure, robust and offer a great spectral efficiency. Different deployment scenarios are possible:distributed, where the antennas are spread across some area with a common radio cloud for processing, cylindar, to improve covered area, linear and rectangular [23].

Massive MIMO is expected to increase the capacity by 10 or more and the energy efficiency by 1000 or more. High mobility is however a challenge for this system as localisation-aware spatial beamforming and channel estimation might require a lot of signalling.

Software Defined Networking (SDN), Network Function Virtualisation (NFV) and Network Slicing are also envisioned [24, 25]. Virtual evolved packet core (vEPC) can be used to lower the costs for operators and improve the network efficiency.

Finally, device-to-device (D2D) communications tackle the traditional view on architecture, where users and providers are two different entities. New nodes might serve as relay between a user equipment and a base station, or two equipment might want to communicate together without using the serving base station [26, 27]. With connected vehicles, both vehicle-to-vehicle and vehicle-to-infrastructure communications will be important, either to serve users within the vehicle or for new services such as collision avoidance for unmanned cars. Internet of Things can also benefit from D2D communications, especially for reliability, low latency and energy efficiency [28, 29].

2.2 Ultra-Dense Networks

As stated before, ultra-dense networks (UDN) are seen as one of the most important way of tackling the new requirements. We can give a first and simple definition of UDN as:

A deployment in which the number of access nodes exceeds the number of users.

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While this paradigm is seen as a cornerstone of 5G, densified networks were already present in the past. We can see Heterogeneous Networks (HetNets), a concept designed for LTE, as a first step in this direction. However, HetNets were only used locally to increase the performance, whereas UDN is seen as a cornerstone of future deployments.

Figure 3.Heterogeneous Network (a) and Ultra-Dense Network (b)

Another possible definition of UDN, which can be extracted of the quantitative work of Ding et al. [30], is related to the density of cells. In their work, 10³ cells/km² appears as the lower bound for an ultra-dense deployment. This definition is linked to the first one in [31]. However, their density of users might not reflect the one in 2020 and onward. It is solely included in this review to provide another view on how UDN can be defined.

2.2.1 HetNet and UDN Characteristics

HetNets are based on the principle of using cells of different size or coverage within the same network [32]. Typically, a HetNet is composed of two layers: a macro-layer, with traditional base stations, and a micro-layer, where small cells, either micro, femto or pico-cells, are used in strategic locations. Such scenario is often referred as “multi-tier deployment”.

Small cells have two main objectives: provide coverage and/or performance. At the edge of macro-cells, where downlink throughput is usually low and uplink suffers even more, users can connect directly to that cell and maintain a good quality of service. Another possibility is using Coordinated Multi-Point (CoMP), where multiple base stations cooperate by transmitting or listening simultaneously and in the same subframe to decrease the probability of erroneous transmissions [33].

But small cells can also be deployed within macro-coverage to serve small areas with high traffic. Solutions like Carrier Aggregation or Dual Connectivity can be set up to boost performance.

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From this concept, the main idea was to deploy even more small cells, as it has been shown that densifying the deployment does increase the system throughput [34].

Access nodes will not be exclusively deployed by network operators in 5G. It is expected that indoor deployment can be done by users within their homes. Moreover, in device to device communications, some devices might play the role of user, relay or access node depending of the situation. These possibilities make management quite complex in some cases.

From these definitions and observations, we can list some crucial characteristics of UDN. (i) Firstly, one user can be within the vicinity of multiple cells. That was already true with multi- tier deployments, but users can be within coverage of more than two access nodes in UDN.

(ii) Secondly, Interferences will be highly critical. With low inter-site distance, transmissions might interfere between each other, leading to a decrease of the signal quality. (iii)Some cells might have no users within their coverage. This characteristic is crucial, as sleep mode can be used to save energy. (iv)In high frequency bands, Line Of Sight deployment will be of great importance. The propagation losses will be so important that user losing a clear view towards the access node might lose its connection.

2.2.2 Ultra-Dense Networks in the Literature

Numerous papers have investigated UDN. We review here some of them by highlighting the different approaches used. A recent survey on ultra-dense networks can be found at [31].

The basic foundation of UDN is laid down in [35]. A list of objectives and challenges are presented, from scaling laws to coordination problems. Some basic impacts of the density of base stations are evaluated, such as the guaranteed rate. The authors conclude by stating that more performance modelling and realistic scenarios are needed to push UDN to their full potential. Theoretical work has been carried out to model UDN. Two main mathematical tools were used: stochastic geometry, and game theory.

Stochastic geometry is often used to represent wireless networks, with users and base stations usually represented by Poisson processes, as it offers the possibility to evaluate probabilistic deployments. Indicators such as outage or coverage probability or the bitrate can be expressed as a lot of mathematical background is available. Stochastic geometry is used in [36] to express spectral and energy efficiency in relation to the density of base stations. The authors show that densifying up to a certain value is not efficient. Also, this optimal density depends of the transmitted power per base station. An optimal density can be found. Similarly, energy

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efficiency is at its minimum (as EE is defined as the power used to transmit one bit here) for the same optimal density.

Game Theory, the second mathematical tool, is used to study behaviour and decision making in cooperation or conflict between rational agents. In [37], Sunet al. formulates a non-cooperative game for cell selection in association. Centralized and decentralized cluster control are tested, and CoMP is used as a way to achieve better performance. A new SINR measurement is also proposed, and their results show a higher connectivity towards small cells and higher throughput for low throughput users. [38] uses mean-field game theory, a sub-field of that mathematical tools, where the number of players is significantly higher. In their work, a two-level interference management framework is proposed. Especially, co-layer interferences are tackled with MFG, and the tested algorithms show an increase in energy efficiency compared to traditional frequency reuse solutions.

Another study shows that densification has its limits. In [39], a deployment with constant area is densified and tested with different propagation model. Results showed that a theoretical limit to densification exists, and it is related to the baseline (constant) power consumption of access nodes.

Simulations are also largely used in the literature. Yunaset al. [40] also studied spectrum and energy efficiency. Their work focused on three deployment strategies: dense indoor femtocells, densified macrocells and dynamic distributed antenna system, thus making the comparison with [36] impossible.

2.3 Multi-Connectivity

The concept of multi-connectivity is not new with 5G. Indeed, some similar mechanisms were defined and standardized for LTE. Dual Connectivity (DC) is defined as an “operation where a given UE consumes radio resources provided by at least two different network point connected with non-ideal backhaul” [41]. Although the official definition allows more than two connections, the scenarios covered contain only one macro cell (master eNodeB) and one small cell (secondary eNodeB).

As for multi-connectivity (MC), Its standardization is only a draft yet and no final definition has been given [42]. However, most authors agree and use a similar concept, derived from the definition of DC. In this work, we define multi-connectivity as:

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The ability for a user equipment to connect to multiple access nodes at the same time.

We will develop more this definition further in this section.

In order to understand how DC and, by extension MC, works, we will briefly explain its architecture and related procedures. A deeper presentation of DC can be found at [43]. We will call master cellthe main base station, also called master eNodeB, MeNB in the literature, andsec- ondary cella base station used as an additional connection. It is also called secondary eNodeB, SeNB in the literature. Unlike the DC standard, we do not consider the master cell as a macro cell only and the secondary cell as a small cell only, but accepts scenarios where the master cell can be a small cell and a secondary cell can be a macro cell.

2.3.1 MC Scenarios and Procedures

In order to reduce signalling, the control plane is present at the master cell only. The UE RRC (Radio Resource Control) layer is terminated at the master cell, which is itself directly connected and communicating with the core network (Evolved Packet Core, EPC for LTE; Next Gen Core, NGC for NR) through the interfaces S1 or NG. The serving cells are communicating together via the X2 or Xn interfaces. The UE is then connected through the user plane to every serving cells. Figure 4 shows the control and user planes in MC.

Figure 4.Multi-Connectivity Architecture

For the user plane, multiple scenarios are possible. First, since DC was designed for LTE, both

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the master and the secondary cells were using the same frequency band. This scenario is called intra-frequency DC. Within this deployment, the split of the data transmission could be either at the bearer level, i.e. each cell use a different radio bearer, or at the packet level, i.e. the master and secondary cells share the same bearer, but packets are either send by the master or a secondary cell. In MC, since multiple frequency bands are available, another scenario is possible. We can have inter-frequency MC, where the master cell and secondary cell does not use the same frequency band. The architecture is, in this case, similar to the bearer level split.

We can further improve our definition of MC:

Multi-Connectivity is the ability for a user equipment to connect to multiple access nodes at the same time. These connections can happen either within the same frequency band, which we call intra-frequency MC, or in different frequency bands, which we call inter-frequency MC.

Figure 5.Inter-frequency MC (a) and intra-frequency MC (b)

In DC, the decision to connect a user to a secondary cell is made by the master cell, based on the UE measurements. Although the user might have more information at its level, especially concerning the radio signals, its battery usage, etc., having the decision at the network level allows a better control of resources, especially when it comes to energy efficiency.

Concerning the uplink (UL), different scenarios exist. UL split can be proven difficult as it increases the complexity of handling reports and prioritization (BSR, LCP, PHR, etc.). How- ever, another technique, named Coordinated Multi-Point (CoMP), can be used to increase UL performance. More information on CoMP can be found at [44].

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2.3.2 Multi-Connectivity in the Literature

In [45], MC in mmWave is investigated. Specifically, a procedure is defined for directional beam tracking, where both the user and small cells are composed of highly directional antenna array.

The work focused on how tracking can be done in order to maintain the multiple connections.

Since antenna arrays are another promising technology for 5G, this approach can be of great interest. However, it might induce an increase in signalling, which is the opposite of what 5G is trying to achieve.

Another work on MC tackles the problem of mobility [46]. Indeed, higher frequency bands are typically associated with lower reliability, as propagation losses are higher and the channel quality can quickly evolve. This work examines how MC can improve the reliability of the connection by looking at the radio link failure (RLF) rate for walking users at 3km/h and users at 60 km/h (in cars along a linear road). A few assumptions are made regarding the architecture, in particular a cloud radio is considered, in order to avoid handover latencies. Furthermore, a strong assumption is made by considering that the control plane is handled by “all of the coordinated cells.” A MC scheme is also proposed, and multiple parameters are tested to see which provide the highest reliability. Their results showed that the RLF can be completely resolved and the 5%ile-throughput can be improved by 43% with specific settings. However, it was also shown that too extreme settings will decrease the user throughput.

Da Silvaet al. proposes different approaches to define MC in order to provide a tight integration of LTE and 5G [47]. Different scenarios of common layers are presented to allow such integration, and procedures such as fast switching, user plane aggregation or control plane diversity are presented along with their advantages. However, these solutions are not tested or put into practice in any way. These can be seen as possible improvements and propositions towards a standardized solution.

In [48], spectrum aggregation in DC is studied for decoupled UL/DL in 5G. Previous studies showed that the best cell for downlink (DL) might not always be the best for uplink (UL), as the latter depends on the user equipment transmission capabilities. Moreover, spectrum aggregation was also shown at being not energy efficient in certain cases for UL. The work used the stochastic geometry framework to prove analytically how UL/DL decoupling can be used with DC in order to improve the user throughput. The results showed that interesting improvements can be made for DL, but it was rather limited for UL because of the power constraints.

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2.4 Energy Efficiency in Mobile Networks

Studies were carried out to decompose the power consumption at the network level [49] and at the component level in base stations [50]. It was discovered that base stations account for around 57% of the entire operative cost. Mobile switching and the network core account together for around 35%, and datacentres and retail represents the remainder. Figure 6 shows the consumption decomposition. The figure is adapted from the values of [49] and [50].

Figure 6. Power Consumption in Cellular Networks

Therefore, most works are aimed at improving the energy efficiency of base stations. More energy efficient and adaptive hardware were designed to reduce their footprint. However, since only 10% of the BS consumption is caused by signal processing, against 50 to 80% in the power amplifier, most studies concentrate on techniques such as sleep mode to reduce the BS consumption to some lower level.

2.4.1 Power Consumption Models

Models for power consumptions are necessary in order to evaluate energy efficiency. Multiple models have been proposed in the literature, covering different aspects such as the differences in consumption between macro-cell and femto-cell, or between abstract or more realistic models.

Ismailet al. summarize some power models in their survey [51]. The most common and most widely used model for macro-cell consumption is defined in [52].

A few papers also focus on femtocell consumption. Femtocell is the main enabler for HetNets and UDN, and its consumption must be correctly modelled for their studies. Deruyck et al.

[53] proposed a power model independent of the load. This choice can be explained by the low

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radiated power of such base stations, but the power is likely going to evolve following the load, as for more traditional BS. Riggioet al.studied real femtocell consumption in order to abstract a model out of their measurements [54]. This model is this time load dependent, although it is only modelling 3G femtocells.

The EARTH project (Energy Aware Radio neTwork tecHnologies) is a FP7 financed European project aimed at investigating energy efficiency of mobile communication networks. In its E³ Framework (Energy Efficient Evaluation Framework), Aueret al. a power model for base stations in LTE as they are used nowadays [52]. Figure 7 shows the power decomposition for the different components of a base stations of different size.

Figure 7.Power Model for LTE

Mathematically, the power consumption is defined as:

P_BS,LTE =N_T ∗N_S∗N_C ∗n P₀+P_tx∗∆_P ,06P_tx 6P_max

P_sleep ,sleep mode (1)

WhereN_T is the number of transceivers (antennas),N_Sis the number of sectors,N_Cthe number of carriers,P₀the static power consumption caused by the cooling and signal processing,P_txthe radiated power,∆P the power consumption dependent of the radiated power, due to feeder losses and the power amplifier, P_max the maximum radiated power and P_sleep the power consumption when the base station is in sleep mode.

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Regarding 5G, most studies envision or assume that 5G base stations will follow a model similar to the one used for LTE. However, the development of more energy efficient hardware will most likely reduce the overall consumption. Moreover, METIS, for its 5G concept, envisioned a lean system control plane [55], sometimes also called ultra-lean design, as a way to reduce signalling.

Such an improvement will bring more possibility for more often and deeper sleep-mode usage.

In order to incorporate lean design to the power model of 5G, we define the power consumption as:

P_BS,5G =N_T ∗N_S∗N_C∗

( P₀+P_tx∗∆_P ,06P_tx 6P_max

P_sleep ,micro-sleep mode

δ_NR∗P_sleep ,deep-sleep mode

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where we reuse the notation in (1). δ_NRcorresponds here, in a similar manner to [56], to the improvement made by the ultra-lean design in sleep consumption. Thus, we denote the difference in LTE and 5G models by a second, deeper, sleep mode level, which corresponds to a fraction of the usual sleep mode consumption. The reason of this expression is detailed below.

2.4.2 Sleep Mode Model

In order to save energy, equipment vendors decided to develop more energy-efficient techniques for their products, especially for scenarios with low load. Sleep mode and cell breathing are two of these solutions.

Cell breathing consists of increasing or reducing the radiated power, thus allowing an increase or decrease in coverage. Even if increasing the transmitted power does also increase the energy consumption, it can be orchestrated in such a way that neighbours cells consumption can be decreased, either by reducing their coverage or even switching them off.

Sleep mode corresponds to a state for the base station when its functions are reduced to its strict minimum, allowing a reduction in energy consumption at the cost of not serving eventual users.

More complex sleep mode is also envisioned for new base stations. Unlike in LTE where one sleep mode is defined, 5G could see the apparition of multiple sleep mode levels, each with a different power consumption [57]. A base station could thus progress through different level, reducing at each step its consumption, and for longer periods of time, as depicted in figure 8.

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Figure 8.Sleep Mode Levels, adapted from [57]

2.4.3 Sleep Mode Techniques

Sleep mode usage has already been intensively investigated to improve energy efficiency in networks. Using stochastic geometry, Soh et al. showed analytically its importance in heterogeneous networks [58]. More extensive surveys of the different sleep mode techniques can be found in [59] for switch-off techniques and in [60] for cell zooming algorithms.

Coordinated Multi-Point (CoMP) is used in [61] in addition to the sleep mechanism. Neighbours of the sleeping cell coordinates together to provide coverage. It is shown that the SINR actively increased when using CoMP. However, the important signalling to coordinate neighbours can quickly lead to inefficient energy consumption. A threshold for CoMP is thus necessary and proposed. Channel estimation is also presented as a factor for CoMP configuration.

Dudnikova et al. proposed a multi-criteria decision algorithm to select cells that should be sleeping and redistribute spectral resources in [62]. Analytic Hierarchy Process and Grey Rela- tional Analysis are used to rank the different cells. Coverage rate indicator, cell load factor and the number of interfering neighbours are used as criterion. Results showed improved energy efficiency between 5 and 11% against other algorithms from the literature. In [63], the same authors also include Fuzzy Logic to their proposal.

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A low complexity algorithm is proposed in [64]. By ordering cells that could be switched off depending on the possible savings and the load steering, the authors managed to obtain results similar to the optimal, NP hard, optimization solution. An energy saving ratio of 0.14 is found, compared to 0.06 and 0.1 from two other algorithms of the literature (the higher means more savings).

In [65], cells are ordered based on its distance to its users. The average distance of the traffic load is computed and shared with neighbouring cells. The cell with the highest distance is switch off if it does not degrade quality of service. Results showed an improvement of 28% in energy consumption.

Cell breathing also received a lot of attention. In [66], antenna tilt is modified after the cell switch-off selection in order to modify the coverage. Results show a slight improvement of quality of service and energy efficiency after tilting optimization. However, we believe that this solution cannot be used in deployment where switch-on and switch-off are frequent. Moreover, the tilt-optimisation algorithm is based on knowledge on the antenna characteristics as well as other cells relative positions. This won’t be possible in user-deployed networks.

Finally, a centralized cell zooming algorithm and its procedure is described in [67]. Cell zooming can either be done by augmenting the transmitted power, CoMP or relaying in this case. This algorithm was tested against a distributed algorithm based on the same principle, and the cen- tralised one behaved better in terms of energy consumed, with savings of the order of 20-50%

compared to the baseline without algorithm.

2.4.4 Other Studies on Energy Efficiency

Discontinuous Transmission (DTX) impact on energy efficiency is studied in [68]. At the network level, cell DTX consists of putting to a lower power mode the base station during two broadcast or signalling when no transmission is needed. These periods are usually 2 or 3 OFDM symbols long. A 15% decrease in energy per bit is obtained with cell DTX. Combined with adaptive hardware, which can attain lower level of consumption in low power mode, a decrease of 57% can be achieved.

Multi-RAT deployments and their relation to energy efficiency are investigated in [69]. In co- sited deployments, it is shown that shared power amplifiers can save up to 42% of energy. Load balancing is also studied, where some traffic is balanced from one rat to another, here from LTE to HSDPA as LTE can use cell DTX, and results showed an improvement of 29%. However,

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shared power amplifiers could be used only for big sites, since femtocells probably won’t be co-sited with other technologies.

In [56], Tombazet al. investigated the new air interface in terms of energy performance. LTE and 5G NR, in that paper called5G-NX, have been tested at 2.6 and 15 GHz. A real life scenario inspired from Asian cities is used, along with beamforming for 5G. It is shown that there is by average a 65% decrease in power consumption for 5G NR at 15 GHz compared to LTE at 2.6 GHz. Moreover, even using LTE and NR together offers a reduction of power consumption compared to LTE alone (35% decrease) at high load. Furthermore, DTX is shown to offer a 66% percent decrease of power consumption at low load and 13% decrease at high load, which is similar to the previous studies.

2.5 Metrics

Metrics are needed in order to quantify power or energy efficiency. The most common one, simply call energy efficiency, corresponds to the quantity of information transmitted per unit of energy, usually Joule. It is expressed in [bit/J]. It can also be expressed as the bitrate offered per power consumed, in [bps/W]. Some work also quantifies energy efficiency in [J/bit], as the quantity of power for 1 bit transmitted. It is sometimes called Energy Consumption Ratio (ECR). Their usage mainly depends of the objective of the study: maximisation or minimisation.

At the system level, the energy efficiency is simply defined as the sum of data transmitted for a unit of time over the entire consumption of the system during that same period of time.

Extensions of these metrics can sometimes be found. The energy efficiency per area, expressed in [bit/J/km²], can be more precise for urban scenario, especially for ultra-dense network, where energy consumption can be non-homogeneous. In a similar fashion, the spectral efficiency is defined in [bit/J/Hz]. Some other means of quantifying efficiency can be used in specific scenarios, such as UDN. The Sleep Ratio is defined as the number of access nodes in sleep mode over the total number of nodes. Similarly, the active ratio can be expressed as 1-sleep ratio.

Many other metrics were also defined. Power Usage Efficiency (PUE) came from datacentre evaluation and can be used to express the efficient usage of energy at the base station level.

Daily consumed energy can assess the evolution of energy consumption throughout the day, whereas averaged daily consumed energy can differentiate two deployments.

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3 Models and Multi-Connectivity Algorithms

This chapter details the implementation done throughout this work. Section 3.1 lists all the models used in our tools. In 3.2, we present how multi-connectivity is implemented. We present our vision on the algorithm steps, and how we can define schemes. Section 3.3 goes further and a detailed description of our proposals are given.

3.1 Models Implementation

Our study relies on the usage of a simulator tool for the evaluation. We decided to use a solution developed and used internally at Ericsson. Our tool is a Matlab-based discrete time event simulator. Since this is a property of Ericsson, no more information can be disclosed. We however present below the parameters or models we modified for this thesis.

3.1.1 Handover Model

The Handover (HO) refers to the procedure of transferring user resources from one cell to another. In 3G, the upkeep of the connection is assured by so-called soft-handovers [70]. In 4G, only hard handovers are available, being mainly due to the usage of OFDM (Orthogonal Frequency-Division Multiplexing), an orthogonal modulation. However, the seamless transition must also be assured in LTE.

3 types of handovers are defined for LTE:

• Intra-LTE Handover

• Inter-LTE Handover

• inter-RAT

The first type corresponds to the scenario where both eNodeB are within the same LTE network.

In this case, the X2 interface is used when available, otherwise the S1 interface is used through the evolved packet core (EPC).

The second type happens when the user also needs a handover the Mobility Management Entity (MME) and/or Serving Gateway (S-GW).

Finally, LTE implements the possibility for the user to switch from one Radio Access Technol- ogy to another, i.e. from LTE to 5G, or UMTS to LTE.

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Studies have been carried out to investigate handovers in LTE-A and 5G. Ferng and Huang [71] proposed a new handover scheme for heterogeneous networks, while Vasudeva et al. [72]

investigated HO failures by analysis and Lee et al. [73] showed the impact of TTT on HO performance.

Some radio quality measurements are needed in order to instantiate a handover procedure. Dif- ferent metrics have been specified by 3GPP [74] for this purpose. We list here a few of them:

• Reference Signal Received Power (RSRP)

• Received Signal Strength Indicator (RSSI)

• Reference Signal Received Quality (RSRQ)

• Signal over Interference plus Noise Ratio (SINR)

• etc...

In our work, we decided to use the Signal over Interference plus Noise Ratio (SINR) as our metric to detect and initiate the HO procedure. This choice is due to our interest in allowing both intra-LTE and inter-RAT handovers. Propagation losses, transmitted power and interferences must be taken into account when selecting the cell.

The SINR is mathematically defined by:

γ_c= G_c,u∗P_tx,c P

ci6=cG_c_i_,u∗P_tx,c_i+N (3)

whereuis the user, c,c_i are base stations,G_c,u the channel gain between the base station cand the useru, andP_tx,cthe transmitted power of base stationc.

Extended to multiple cells transmitting the same information (in CoMP):

γ_C_u =

P

c∈CuG_c,u∗P_tx,c P

ci6∈CuG_c_i_,u∗P_tx,c_i +N (4)

whereC_u is the set of base stations transmitting the useful data.

In the handover procedure, the event A3 describes the time where the connected cell signal falls below the signal of another cell. In order to avoid a ping-pong effect of handovers, a hysteresis is defined. The ping-pong effect relates to the case where a user switches repeatedly between two cells in a very short period of time. If the SINRγ_cstays belowγ_t+hysteresisfor a certain period of time calledTime To Trigger(TTT), the handover procedure is initiated. After at_exectime and

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Figure 9. Handover Procedure

if the connection does not have a failure, the handover is successful. Figure 9 represents a HO procedure and table 1 contains all the parameters related to HO.

Mathematically, we have:

γ_c(t)< γ_t(t) +hysteresis;fort₀ −T T T < t < t₀. (5) whereγ_c(t), γ_t(t)the SINR value of the connected BS and the target BS respectively, andT T T the Time To Trigger.

Table 1.Handover Parameters Parameters Values Hysteresis [dB] -2 Time To Trigger [ms] 50 Execution Time [ms] 40

3.1.2 Radio Link Failure Model

Radio Link Failure (RLF) denotes the status where a wireless connection is considered dropped.

Its definition hasn’t changed much from 3G to 4G [75, 76], and is unlikely to evolve in a drastic

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way for 5G.

A link is considered in RLF if the received SINR is below a thresholdQ_outfor a certain period of time noted T_RLF. If the signal goes higher than the threshold during that time, the counter is reset and the link is not in failure. If, after that time, the signal is still below the threshold, the link is considered in RLF and the connection is dropped. Therefore, the user has to initiate a new connection procedure, which can take some time. Table 2 contains the values used in our model.

Mathematically, we write:

γ_c(t)< Q_out;fort₀−T_RLF < t < t₀. (6) whereQ_out is the threshold andT_RLFis the time after which the connection is dropped.

Table 2.Radio Link Failure Parameters Parameters Values

Q_out[dB] -5 T_RLF [ms] 50 Reconnection time [ms] 1300

3.1.3 Sleep Mode Model

As presented in section 2.4.2, will be improved in 5G. For our implementation, we follow the concept of sleep levels from [57] that we already discussed. Figure 10 shows our implementation. Unlike Debaillie’s paper, we define only two levels: micro-sleep and deep-sleep.

As defined in equation (2), the deep-sleep mode consumption is set as a fraction of the micro- sleep consumption by a factor δ_NR. For our simulations, we chooseδ_NR = 0.29, as used in [56].

We define the sleep and wake-up procedures as the following:

• After 10ms in active mode and without any transmission, the base station goes to micro- sleep mode;

• The base stations wakes up directly if it needs to transmit something. Without any transmission, the base station stays in micro-sleep for a maximum duration of 10ms. After that

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Figure 10.Sleep Mode Model for 5G

time, it goes into deep-sleep.

• The base station stays in deep-sleep until it needs to transmit something. If data arrived, it directly goes to active mode.

The consumption values we use are slightly higher than real consumption. It allows us to account within the sleep consumption small wake-up phase. No wake-up transition phase is modelled as its duration would be inferior to the simulation time step.

3.1.4 Network Related Models

In this work, LTE or the LTE evolution is considered using the 2 GHz carrier. As stated in the related work, 5G NR will likely use frequencies around 30 GHz. Therefore, we use the 28 GHz carrier for NR, which seems to be the global consensus. In order to model the higher capacity of the new air interface, we use a bandwidth of 100 MHz, unlike LTE which is modelled with a carrier bandwidth of 20 MHz.

Since LTE is used as the macro-layer, we use tri-sector antennas, as often deployed in reality.

We also set the height to 30 meters, such as the cells are a few meters above the rooftop level, or at the top of a tower. As 5G NR is used as small cells, we use a simple omni-directional model for the antenna. The antenna gain is set to 0 dBi, such as there is no gain compared to

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Table 3.Network Parameters

Parameters LTE 5G NR

Frequency [GHz] 2 28

Carrier Bandwidth [MHz] 20 100 Antenna Type Tri-sector Omni

Antenna Gain [dBi] 15 0

Antenna Height [m] 30 10

N_S(Number of Sectors) 3 1

N_C (Number of Carriers) 4 4

P0[W] 130 56

∆_P 4.7 2.6

P_max[W] 20 6.3

P_sleep[W] 75 39

δ_NR 0.29

Number of Sites 3 61

Inter Site Distance 400 100

the isotropic antenna. The height is set to 10 meters, which can be seen as an antenna on a façade. Sometimes, antenna are also considered at the top of a lamp post. This is the usual height defined in the METIS guidelines.

For the consumption model, the EARTH framework defined a set of values for different size of cells [52]. LTE values are based on the macro-cell power decomposition, whereas 5G NR are taken from the micro-cell values. Table 3 lists all the different network parameters used in our simulations. Both radio access technologies will use four carriers in this thesis. Carrier Aggregation is allowed.

For the network deployment, we use a hexagonal grid positioning system. We therefore always have a deterministic network. RAT are considered non co-sited as they have different inter-site distances. Some cells might however have the same location; this is not a problem. Figure 11 represents the deployment used. Furthermore, we use a wrap around parameter, such as the network is repeated at its edges. Cells at the edge receives interferences from the wrapped network and user going out of the area are wrapped around.

The COST Hata model is used for radio propagations. As the original covers frequencies up to 2 GHz, we extend it via linear interpolation. Fading is modelled as shadowing, and follows a Gaussian distribution with a mean of 0 dB andσ² = 5dB.

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Figure 11.Deployment Strategy

3.1.5 User and Traffic Models

A user is represented by its equipment, called user equipment, or UE. User mobility is defined as a linear movement, i.e. the UE follows a straight line. A bouncing circle is used to ensure that all UEs remain within the simulated area. When the user reaches the circle, it simply “bounces”

back towards the simulated region with a random angle.

The number of users remains fixed throughout a simulation. However, UEs can be in different states. When connected to a base station, it is considered in active state. After some inactivity period, the user can go into idle mode, which relax the signalling transmission between the serving BS and the UE. As explained before, a UE can also be in RLF if the received signal is too weak.

Users also move with a fixed speed. Unless stated otherwise, UEs are considered pedestrians with a velocity of 3 km/h. Table 4 summarizes the different parameters. Once created, we keep the user alive until the end of the simulation. After transmitting and a counter, the user goes to idle mode.

Daily, weekly and even monthly consumption are well known and can be found in [77, 78].

Some model for future networks are also described for the METIS test-cases in [79]. Based on

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Table 4.Users Parameters

Parameters Values

Number of UEs Fixed

Movement Model Linear, Bouncing Circle

Velocity Fixed

Traffic Type Bursty, FTP

these different solutions, we propose our own model which aims at modelling data traffic at a smaller time scale, typically a few seconds or minutes.

As recommended by the simulation guidelines of METIS [79], a bursty user-driven model is chosen. Specifically, the File Transfer Protocol (FTP) is selected for modelling data transfer.

Although, in their guidelines, the 3GPP FTP Model 2 [80] is used with a data size of 20 MB, in order to model web browsing traffic. In our model, FTP is used in conjunction with different packet size to realistically emulate the diversity of flows.

Packets are generated randomly with three different size (headers included): 1 MB, 10 MB or 100 MB. Packet size distribution follows a Poisson law, i.e. there are more chances of generated a small packet rather than a big one. Two successive packets have independent distributions.

The inter-arrival time is non-periodic and also modelled following an exponential distribution.

The mean time between two packets is set to 3 seconds. Again, the inter-arrival time distribution is independent of the packet size and between two successive packets.

Parameters of the traffic model are summarized in table 5.

Table 5.Traffic Model

Parameters Values

Traffic Type FTP, Bursty

Size Distribution Exponential File Size 1 MB, 10 MB, 100 MB Size Probability 5/8, 2/8, 1/8 Inter-arrival Time Distribution Exponential Mean Inter-arrival Time 3 seconds

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3.2 Multi-Connectivity Implementation

One of the objectives of this thesis is to develop energy efficient multi-connectivity schemes.

In this section, we describe the working principles of our proposals. For this, we present first a breakdown of the mechanics of the MC procedure. We will then elaborate on each scheme.

3.2.1 Basic Algorithm Decomposition

In order to provide the best service and based on the specification of dual connectivity, we can enumerate few principles that should be followed for the implementation:

• User association logic is based on one or a set of metrics.

• Channel quality, BS information and user information are the inputs available. They are obtained through the same assessments and reports used in the handover procedure.

• As for handovers, ping-pong effects (consecutive connection, disconnection or switching) should be avoided.

• The association procedure should be initiated at the network side. The user might have the ability to request a specific connection, but the cell is the final decision maker.

• The base station serving the user (the master cell) is the one running the scheme. It can receive measurements from target cells but should be the decision maker.

Each scheme might use or combine metrics differently. Different metrics are also available to estimate the channel quality, as presented before. The X2 interface is used to share information between two base stations.

Counters and hysteresis are commonly used to tackle the ping pong problem. In our work, we solve this problem by isolating the connection and the disconnection procedures into two mechanisms. Counters are also used to avoid noisy metrics such as high variation in the channel quality that could lead to early disconnection. Hysteresis can also be used within the mechanisms.

Figures 12 and 13 are flowcharts representing respectively the connection and disconnection procedures. It can be noted that both have a similar structure.

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Following the reception of information, our inputs, a first selection is made. If a target is selected, a Time To Trigger (TTT) counter, associated with a specific user, is started. When the counter reaches the threshold and if that target is still available, the actual connection (or disconnection) procedure is initiated.

Figure 12.Association Procedure Flowchart

The scheme can either be implemented for serving one user only, and therefore the algorithm will loop for each user, or it can be implemented to serve all the connected users at the same time. Of course, taking into accounts all the UEs is better when trying to find a global optimal solution.

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Figure 13.Disassociation Procedure Flowchart

3.2.2 Classification of Metrics Usage

Numerous metrics can be taken as input. We decide to combine them into the following categories:

• Robustness: it typically regroups the channel quality metrics, such as RSRP, RSSI and SINR. They can be used to provide reliable connection.

• Performance:the bitrate can be used as a metric for performance. Since the exact bitrate cannot be estimated before establishing the connection, a simple estimation such as the Shannon law can be useful. It allows connection to cell with high bandwidth (and possibly high bitrate) but with signal strength not as good.

• Energy:Power required for transmitting the data for the user or the entire energy used to

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keep the base station active can be used as metric as a way to estimate energy efficiency.

Power models need to be implemented within the scheme in order to provide the best estimations.

• Cell Utilization: this class contains all the information relative to the target cell. The cell state (active, sleep), its load or the number of users associated with it can be used for traffic steering and load balancing.

By looking at the metrics used as input, it is possible to classify the purpose of a scheme. It is also possible to combine metrics coming from different categories, in order to create an all- purpose solution. However, in that case, a trade-off would be needed. Indeed, optimizing the energy usage will have a negative impact on the performance or the robustness, and the most reliable connection might not offer the best performance.

Differentiating schemes also let us compare them more easily. We can define a scheme as a baseline if it takes input from one category only and if it uses simple logic. More strictly, we can specify a baseline algorithm if it uses one single metric and if its logic consists of optimizing (by maximizing or minimizing) that input.

3.3 Presentation of our proposals

We present here five of the algorithms developed during our work. Table 6 show the relation between these schemes and the classification introduced earlier.

It can already be noted that the first two algorithms follow the definition of a baseline given before. The naming convention tries to include the optimization principle and the metric used when possible.

Table 6.Multi-Connectivity Schemes Parameters

Schemes Performance Robustness Energy Utilization

Max Bitrate x

Max SINR x

Max Bitrate-EE x x

Max Clustered-Bitrate x x x

Analytic Hierarchy Process x x x x

Energy efficient multi-connectivity for ultra-dense networks

Valentin Poirot