Implementation and evaluation of FD-MIMO beamforming ...beamforming. As a generalization of...

TECHNISCHE UNIVERSITÄT WIEN

DIPLOMA THESIS

Implementation and evaluation ofFD-MIMO beamforming schemes for

highway scenarios

Author:Félix Pablo CANO PAÍNO

Supervisor:Fjolla ADEMAJ

Martin K. MÜLLERStefan SCHWARZ

Markus RUPP

A thesis submitted in fulfillment of the requirementsof the Telecommunications Master programme

August 29, 2017

http://www.tuwien.ac.at/

ii

Technische Universität Wien

AbstractInstitute of Telecommunications

Mobile Communications Department

Telecommunications Master programme

Implementation and evaluation of FD-MIMO beamforming schemes forhighway scenarios

by Félix Pablo CANO PAÍNO

With the widespread growth of urban environments and the appearance of vehicle-to-X access communications, new techniques have emerged to overcome this densifi-cation matter. One promising technology is the Full-Dimension MIMO (FD-MIMO),which by using 2-dimensional planar antenna arrays, can model the beams andsteer them not only horizontally, but also in the vertical domain. This is called 3D-beamforming.

As a generalization of beamforming in multi-antenna wireless communications,three different non-codebook based precoders have been studied and compared inthis thesis. These are, the Maximum Ratio Transmission (MRT), the so-called Geom-etry Based and the Exhaustive Search over azimuth precoders.

Using the Vienna LTE-A Downlink System Level Simulator, several simulationshave been performed to evaluate the performance of the aforementioned scenar-ios. These are the comparison between Line-of-Sight (LOS) and Non-Line-of-Sight(NLOS) propagation conditions and the inclusion of an uncertainty area around theusers, accounting for moving users. All these contrasting scenarios will help thereader to understand the pros and cons of the three aforementioned precoding meth-ods.

http://www.tuwien.ac.at/

https://tiss.tuwien.ac.at/adressbuch/adressbuch/orgeinheit/1682

https://www.nt.tuwien.ac.at/research/mobile-communications/

iii

AcknowledgementsSpecial thanks to my Principal Supervisors, Prof. Markus Rupp and Dr. Stefan

Schwarz for giving me the opportunity to work along with the Mobile Communica-tions Department.

I would also like to express my sincere appreciation to Martin Müller and es-pecially to my supervisor Fjolla Ademaj for all the support she has shown to meduring my whole Master Thesis project, without whom this work would have notbeen possible.

Finally, I am truly grateful to my friends Patricia, Sergio, Victoria and Álvaro andto my family for their encouragement throughout this whole year.



iv

Contents

Abstract ii

Acknowledgements iii

1 Introduction 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Vehicular Communications . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Full-Dimension MIMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Theoretical framework 52.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1.1 The Vienna LTE-A Downlink System Level Simulator . . . . . . 5Link Level Simulations . . . . . . . . . . . . . . . . . . . . . . . . 6Link Quality Model . . . . . . . . . . . . . . . . . . . . . . . . . 6Link Performance Model . . . . . . . . . . . . . . . . . . . . . . 6

2.1.2 Link-to-System (L2S) Model Validation . . . . . . . . . . . . . . 72.1.3 The Spatial Channel Model . . . . . . . . . . . . . . . . . . . . . 82.1.4 The 3GPP 3D channel model . . . . . . . . . . . . . . . . . . . . 9

2.2 Propagation conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Line-of-sight propagation . . . . . . . . . . . . . . . . . . . . . . 122.2.2 Non-line-of-sight propagation . . . . . . . . . . . . . . . . . . . 13

2.3 3D Beamforming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.3.1 Antenna modeling . . . . . . . . . . . . . . . . . . . . . . . . . . 152.3.2 Array steering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.3.3 First Null Beamwidth (FNBW) and Half Power Beamwidth

(HPBW) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

3 Methodology 193.1 Codebook based precoding . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.1.1 Maximum Ratio Transmission precoder . . . . . . . . . . . . . . 203.1.2 Geometry based precoder . . . . . . . . . . . . . . . . . . . . . . 213.1.3 Exhaustive search over azimuth . . . . . . . . . . . . . . . . . . 21

3.2 Position uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4 Simulations 254.1 Scenario definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.2 Fixed user location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2.1 Precoders comparison for fixed UE location . . . . . . . . . . . . 284.2.2 Uncertainty versus Non-uncertainty comparison . . . . . . . . . 294.2.3 LOS versus NLOS comparison . . . . . . . . . . . . . . . . . . . 30

4.3 Random user location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.3.1 Precoders comparison for random UE location . . . . . . . . . . 31

v

4.3.2 LOS versus NLOS comparison (Random case) . . . . . . . . . . 324.3.3 LOS versus NLOS comparison for all precoders (Random case) 33

5 Conclusion 34

vi

List of Figures

1.1 V2X access technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 FD-mimo and beamforming . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Link quality and link performance model . . . . . . . . . . . . . . . . . 62.2 Link quality and link performance model . . . . . . . . . . . . . . . . . 72.3 Comparison between link and system level simulation time . . . . . . 82.4 Zenith angle of Departure (ZoD) and Zenith angle of Arrival (ZoA) in

outdoor LOS conditions [6] . . . . . . . . . . . . . . . . . . . . . . . . . . 102.5 Clusters and multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.6 Definition of d2D and d3D for outdoor UEs [18] . . . . . . . . . . . . . 112.7 Definition of d2D and d3D for indoor UEs [18] . . . . . . . . . . . . . . 122.8 LOS versus NLOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.9 Beamforming comparison between 2D case and 3D case . . . . . . . . . 142.10 2-dimensional planar antenna arrays . . . . . . . . . . . . . . . . . . . . 152.11 Broadside direction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.12 Steering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.13 Antenna radiation pattern . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.1 Exhaustive search over azimuth . . . . . . . . . . . . . . . . . . . . . . . 223.2 Azimuth angles per RB . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3 Uncertainty area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.1 Scenario definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2 Fixed UE locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Precoders comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.4 Uncertainty versus Non-uncertainty comparison . . . . . . . . . . . . . 294.5 LOS versus NLOS comparison . . . . . . . . . . . . . . . . . . . . . . . 304.6 Random UE locations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.7 Precoders comparison (Random case) . . . . . . . . . . . . . . . . . . . 314.8 LOS versus NLOS comparison (Random case) . . . . . . . . . . . . . . 324.9 LOS versus NLOS comparison for all precoders (Random case) . . . . 33

vii

List of Tables

2.1 FNBW and HPBW for different number of beams. Broadside (Θ=π/2) 18

3.1 LTE codebook for CLSM mode and two transmit antennas for each ofthe possible number of layers (v) . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Maximum deviation for UEs for given speeds of 30 km/h and 120 km/h 24

4.1 Simulation parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.2 Antenna parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3 Scenario parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

viii

List of Abbreviations

3GPP 3rd Generation Partnership Project5G Fifth GenerationAAS Active Antenna SystemsBS Base StationBLER Block Error RateCLSM Close Loop Spatial MultiplexingCoMP Coordinated Multi PointCRS Common ReferenceSignalCSI Channel State InformationCSIT Channel State Information at TransmittereNodeB evolved Node BFDD Frequency Division DuplexFD-MIMO Full Dimension Multiple Input Multiple OutputFNBW First Null Beam WidthHPBW Half Power Beam WidthITS Intelligent Transport SystemL2S Link To SystemLNA Low Noise AmplifierLOS Line of SightLTE Long Term EvolutionMRT Maximum Ratio TransmissionNLOS Non Line of SightOLSM Open Loop Spatial MultiplexingPA Power AmplifierPDP Power Delay ProfilePMI Precoding Matrix IndexQoS Quality of ServiceRB Resource BlockSINR Signal to Interference plus Noise RatioSCM Spatial Channel ModelSM Spatial MultiplexingTTI Transmit Time IntervalUE User EquipmentULA Uniform Linear ArrayV2I Vehicle to InfraestructureV2P Vehicle to PedestrianV2V Vehicle to VehicleV2X Vehicle to Everything

ix

List of Symbols

A broadside patternCk,j covariance matrix between the user k and the eNodeB jH channel matrixk wave number m−1

N number of antenna array elementsNtx number of transmitting antennasNrx number of receiving antennasp power allocationr spherical unit vector (o)t time between measurements sv user speed m

sW precoding matrix

∆ distance between antenna array elements mλ wavelength mϕ azimuth departure angle array (o)ϕ0 azimuth departure angle from direct link (o)µ mean of the Gaussian distribution for an uncertainty area mσ2 variance of the Gaussian distribution for an uncertainty area m

σ2k noise power WHz

θ elevation departure angle (o)

1

Chapter 1

Introduction

1.1 Motivation

Day by day, society is experiencing a huge development in technological fields suchas vehicular communications. These improvements, unfortunately, come along withsome challenges. One of them, is the necessity of supporting crowded scenarios ofquasi-static users, while at the same time, giving service to moving users.Another example, related to highly mobile users, is that yet, the Long-Term Evolu-tion (LTE) standard cannot support both efficient and reliable wireless communica-tion at such high mobility users.

In this Master Thesis, we first introduce relevant concepts such as Vehicular-to-everything (V2X) and the Full Dimension Multiple Input Multiple Output (FD-MIMO).Next we give a theoretical framework which presents the system model utilized andthe beamforming schemes. In chapter 3, different precoding techniques are ana-lyzed. Besides, several scenarios with moving users placed on a highway are ex-plained and the methodology used to tackle these challenging set-ups introducedbefore. A final discuss along with the results will give detailed insights in the find-ings of this thesis.

1.2 Vehicular Communications

The main goal of vehicular communications is to improve the safety on roads. Byenabling bilateral data conversation between vehicles as well as improving the effi-ciency of transportation through smart traffic management, road fatality reductionof up to 50% in the near future is an ambitious but feasible goal. This objective isachievable by so-called Intelligent Transport Systems (ITSs). Furthermore, a mini-mum traffic environmental impact will be needed due to the reutilization of avail-able road infrastructure by adaptive traffic management.

In addition to traffic efficiency and safety-related issues, entertainment systemsthat support on-demand video streaming and online Internet access for passengersare growing gradually in interest.

V2X communications includes different connectivity types. Vehicle-to-vehicle(V2V), Vehicle-to-pedestrian (V2P) if communication between people in proximityand the cars is carried and Vehicle-to-infrastructure (V2I). In this Thesis we focuson the V2I case. V2I communication is realized by employing Base Stations (BSs) astransmission hubs. Figure 1.1 shows the different access technologies. [5]

2 Chapter 1. Introduction

FIGURE 1.1: Vehicular communication scenario of an urban area withdifferent access technologies for moving users [5]

1.3 Challenges

Regarding some challenges to overcome in vehicular comunications, we distinct twogroups of users:

• Large groups of (mostly) indoor quasi-static best effort users which need al-most unlimited bandwidth wireless communications.

• Moving users who demand diverse Quality of Service (QoS).

Even though LTE provides point-to-point connectivity to moving users up to 500km/h, such big data throughput (up to Gbps) with latency below 10 ms can onlybe achieved by few static users. Moreover, the increasing demand of higher staticnetwork capacity, makes many Five Generation (5G) proposals to focus only on thefirst group at expenses of the second group that can even experence a degradationin its performance.

1.4. Full-Dimension MIMO 3

1.4 Full-Dimension MIMO

FD-MIMO has been identified as one of the main technologies for the fifth gener-ation of mobile cellular networks, 5G. By employing hundreds of antennas (oftenreferred to as massive MIMO systems) at the BS as a two dimensional array, thespectral efficiency gain improves various orders of magnitude.

Another interesting feature of the FD-MIMO system is the introduction of activeantennas with Two-dimensional (2D) planar arrays. In the Active Antenna-basedSystems (AAS), gain and phase are controlled by active components, such as PowerAmplifier (PA) and Low Noise Amplifier (LNA), attached to each antenna element.In the 2D structured antenna array, one can control the radiation beam pattern toprovide more degrees of freedom in supporting users on both vertical (elevation)and horizontal (azimuth) direction so that the control of the transmit beam in 3Dspace is possible. This technique is called Three-dimensional (3D) Beamforming.

3D Beamforming is a signal processing technique which, combining elements inan antenna array, allows that signals at particular angles experience constructive in-terference while others experience destructive interference. The 3D Beamformingtechnique is explained in more detail in the next chapter [8] .

Figure 1.2 gives an example of FD-MIMO supporting 3D-beamforming.

FIGURE 1.2: Example of FD-MIMO antenna supporting both azimuthand elevation beamforming [6]

Another important benefit of 2D AAS is that it can accommodate a large numberof antennas without increasing the deployment space. For instance, large linear an-tenna arrays deployed in a horizontal direction require surfaces up to some meters.Due to the limited space on a rooftop or mast, this space would be excessive for mostof the cell sites. In contrast, when the antennas are arranged in a square array, thespace required is drastically reduced.

4 Chapter 1. Introduction

Finally, it is important to note some undesirable effects that can be problematic inhigh-speed scenarios. For instance, the multi-user interference and the degradationof the Signal-to-interference-plus-noise Ratio (SINR).

In order to avoid multi-user interference (especially noticeable due to the highspatial resolution that is achievable) accurate Channel State Information (CSI) is re-quired. The CSI describes how the signal propagates from the transmitter to thereceiver and represents the combined effect of, for example, scattering, fading, andpower decay with distance.

5

Chapter 2

Theoretical framework

2.1 Simulation setup

As mobile communications evolve, new challenges must be overcome. Such de-velopment, incurs in a growing complexity and the effort of simulations becomesenormous. A common solution is to divide the simulations into two stages, the link-level and the system-level, so that computational costs and complexity in system-level simulations are reduced while keeping accuracy. For that purpose, we use theVienna LTE-A Downlink System Level Simulator [19][20][21] .

2.1.1 The Vienna LTE-A Downlink System Level Simulator

System level simulations aims to evaluate the performance of a whole network withmultiple User Equipments (UEs) and Evolved Node B (eNodeB) stations. More-over, the propagation effects are modeled in terms of large- and small-scale fading,considering both desired and destructive interferences. The large-scale parameterscomprise the geometric positions of the eNodeB sectors and the UEs, they are usedto parameterize the statistics of the small scale parameters. The latter, also known aschannel models, are challenging when describing wireless communications.

In order to evaluate the system level performance of a wireless network, complexsimulations comprising a high number of network elements and its interconnect-ing links are employed. A simple approach to system level performance evaluationwould be to perform all of the Physical (PHY) and Medium Access Control (MAC)layer procedures. But due to the high computational complexity of the channel cod-ing/decoding procedures and the MIMO receiver, this approach does not scale welland results in impractical simulation times.

The increasing interest in wireless cellular systems and a method for predictingit, makes the system-level simulations a fundamental tool. However, exact link-levelmodeling is unfeasible due to its huge complexity. Therefore, mathematical abstrac-tion is recommended in order to obtain equivalent results reducing the complexity.A widely accepted solution is the application of link abstraction models that specifythe interaction between link- and system level simulators.

6 Chapter 2. Theoretical framework

Link Level Simulations

Regarding link level simulations, by upscaling the number of simulated links andnetwork elements, it is possible to affirm that link level improvements do also im-prove the network performance. Moreover it is also possible to test and evaluate thealgorithms controlling the PHY and MAC layers.

Link level simulations normally calculates a range of Signal to Noise Ratios (SNRs)for which link performance is evaluated in terms of throughput. The simulation runtime varies depending on the LTE system bandwidth chosen. For instance, a band-width of 1.4 MHz, results in a simulation run time in the order of hours. If we in-crement the bandwidth up to 20 MHz and we generate a simple interference-limitedscenario, such a typical LTE system level simulation would require a simulation timein the order of months, which is clearly not practical.

In order to simplify this problem, it can be divided in two parts, which jointlymodel the performance of the link: a link quality model and a link performancemodel. The link quality model, quantifies the quality of the received signal after re-ception and equalization, the channel quality output measured by the link qualitymodel serves as input to the link performance model. Figure 2.1 illustrates the sep-aration of the link into a link quality and a link performance model, as well as theinputs necessary to perform each step.

FIGURE 2.1: Separation of the LTE link into link quality and link per-formance model [24] .

Link Quality Model

The link quality model models the measurements used for link adaptation and re-source allocation. It is a measure of the quality of the signal received. A straight-forward solution to do so, is the post-equalization SINR. Moreover, the complexityof the link quality model can be reduced by considering only a subset of the totalpost-equalization SINRs.

Link Performance Model

As said in section 2.1.2, the link quality model measures the channel quality outputwhich serves as input to the link performance model. For the RB in which the UEis scheduled (if scheduled), the link performance model combines the output of thelink quality model with the applied modulation order and code rate and predicts theBLER of the received Transport Block (TB). Figure 2.2 describes the aforementionedinputs to the link quality and link performance models.

2.1. Simulation setup 7

FIGURE 2.2: Full scheme of the Link to System model [24] .

2.1.2 Link-to-System (L2S) Model Validation

The objective of the link quality and link performance models is to provide an ac-curate link throughput prediction. Furthermore, with a negligible loss of accuracy,the computationally-intensive MIMO precoder feedback is additionally performedoff-line, speeding-up simulation run-time. Link abstraction models for system levelsimulations, such as a capacity-based model suggested in the LTE standard are em-ployed as a much simpler solution, since accurate link abstraction models are labo-rious to design and implement.

Regarding the complexity evaluation, the run-time complexity of system levelsimulations compared to link level simulations, shows a significant reduction insimulation run-time when employing the L2S model. As from the values, listed inTable 2.3, link level simulation run times scale linearly with the number of RBs. Thelink level simulation time for the 20MHz case has been extrapolated from the exist-ing values. Hence, Table 2.3 validates the statement, that a link abstraction modelis required for significantly faster simulation times compared to detailed link levelsimulations.


FIGURE 2.3: Simulation run time comparison in seconds. Bold face:system level simulation times. Normal type: link level simulation

time [24].

The reduced and almost constant simulation run time of the system level sim-ulator can be explained by the removal of the most computationally-intensive task,which is the channel trace generation. Thus, the complexity increase of the bandwidth-dependent part of the L2S model is almost negligible in comparison to the overallrun time. Such a computational complexity reduction enables performing multi-usersimulations with high channel bandwidths, necessary to evaluate complex schedul-ing scenarios or multi-user gain with more practical simulation time durations.

2.1.3 The Spatial Channel Model

Channel models can be divided into two categories, deterministic and stochastic.Deterministic models describe the channel for a specific propagation environmentbetween eNodeB sector and UE. While in stochastic models, the channel charac-teristics are gathered into a statistical description such as the Power Delay Profile(PDP).

In order to model such different approaches , the 3rd Generation PartnershipProject (3GPP) introduced the Spatial Channel Model (SCM). The SCM is a geomet-ric stochastic model that difference large-scale parameters such as shadow fading,delay spread and angular spread from small-scale parameters (such as delays, ar-rival and departure angles and cluster powers). SCM includes several different sce-narios such as the urban, rural or WINNER models, each of them representing aunique environment.

Moreover, a 3D SCM that describes channel characteristics in three dimensionshas been introduced in 3GPP TR 36.873. This extension to the third spatial dimen-sion, the elevation, allows to use large antenna arrays at both transmitter and re-ceiver.

2.1. Simulation setup 9

2.1.4 The 3GPP 3D channel model

In this section, deeper insight about the 3D channel model will be given. As previ-ously explained, multi-antenna techniques are capable of exploiting the elevation di-mension. Since the existing 2D channel models do not capture the elevation channelcharacteristics, the channel models are not handling the expected growth in mobiletraffic communications. Therefore, new techniques are necessary that require newmodels.

In order to evaluate techniques such as UE specific elevation beamforming andFD-MIMO, where the transmission is adapted efficiently in both elevation and az-imuth to a particular UE, a 3D channel model is necessary. Thus, the 3GPP hasrecently developed a 3D channel model.

One key aspect of this model is the ability to design channels for users locatedon different floors of a building (at different heights). This is achieved by captur-ing a user height dependency in modeling some channel characteristics includingpathloss and LOS probability.

Regarding the application environments, Urban Macro (3D-UMa) and Urban Mi-cro (3D-UMi) with eNodeBs located outdoors are considered. The 3D-UMa and the3D-UMi scenarios follow the conventional 2D-UMa and the 2D-UMi scenarios as de-termined in ITU-R. Both scenarios are considered to be densely and homogeneouslypopulated by buildings.

A detailed description of antenna modeling, LOS probability and pathloss isgiven in subsections 2.3 and 2.4. Hence, fast fading model is explained in this sec-tion. The fast fading channel coefficients model the time-varying fluctuations ofwireless channels that are caused by the combination of multipath component andUE movement.

Cellular downlink is assumed for describing the fast fading model, so the depar-ture angles are defined at the eNodeB side and the arrival angles are defined at theUE side. The channel coefficients of a link between a transmitter and a receiver aredetermined by the composite channel impulse responses of the Multi-path Compo-nents (MPCs) [11] .

Each MPC is characterized by a path delay, a path power and random phasesintroduced during the propagation as well as the incident path angles, azimuth andelevation angles of departure and arrival (see Figure 2.4). After these angles arecalculated, the spherical unit vector with azimuth departure angle φ and elevationdeparture angle θ is generated:

rtx =

sinθ cosφsinθ sinφcosθ

(2.1)


FIGURE 2.4: Zenith angle of Departure (ZoD) and Zenith angle ofArrival (ZoA) in outdoor LOS conditions [6] .

These angles will be used to compute the channel matrix H with dimensionsNRx

x NTx for each sampling point on the time-frequency grid. The term NTx representsthe number of transmitting antenna ports and NRx the number of receiving antennaports. These channel realizations are generated per Resource Block (RB) and Trans-mission Time Interval (TTI). The channel coefficients are calculated at runtime anddepend on the position of the UE location [2] .

Finally, some clusters (Figure 2.5) are placed around the scenario in order to sim-ulate obstacles, buildings and any objects that may produce multipath propagation.

FIGURE 2.5: Scattering concept in the 3D model. φ and θ representthe azimuth and elevation departure angles [2] .

2.2. Propagation conditions 11

2.2 Propagation conditions

Two propagation conditions are described in the technical report TR36.873 of the 3Dchannel model, LOS and NLOS.

The probability of being a LOS propagation is described in equation 2.2 and 2.3[18] .

For Urban Micro (3D-UMi):

PrLOSUMi=

{1 d2D−out ≤ 18m

18d2D−out

+ exp(−d2D−out

36

)(1− 18

d2D−out

)18m < d2D−out

(2.2)

and for the Urban Macro case (3D-UMa):

PrLOSUMa=

1 d2D−out ≤ 18m

18d2D−out

+ exp(−d2D−out

63

)(1− 18

d2D−out

)(1 + C ′(hUT )54

(d2D−out

100

)3exp

(−d2D−out

150

))18m < d2D−out

(2.3)where hUT is the antenna height of the UE, d2D−out the horizontal distance be-

tween the BS and the UE (Figure 2.6) and C ′(hUT ) is described as:

C ′(hUT ) =

{0 hUT ≤ 13m(

hUT−1310

)1.513m < hUT ≤ 23m

(2.4)

FIGURE 2.6: Definition of d2D and d3D for outdoor UEs [18]


In case that we have to distinguish between indoor and outdoor users, we con-sider two different 2D distances (d2D−out and d2D−in) and 3D distances (d3D−out andd3D−in) as showed in Figure 2.7.

FIGURE 2.7: Definition of d2D and d3D for indoor UEs [18]

Note that

d3D−out + d3D−in =√

(d2D−out + d2D−out)2 + (hBS + hUT )2 (2.5)

where the term d2D−out is the distance d2D for the outdoor case, and the termd2D−in the distance from the external wall to the UE.

2.2.1 Line-of-sight propagation

In this section, we explain the model we consider for LOS environment. Dependingon how far the UEs are from the base station, two different equations are used:

• For distances between 10 meters and the breakpoint(dBP = 4hBShUE

fcc

)PL = 22.0log10d+ 28.0 + 20log10fc (2.6)

• For higher distances (up to 5000 meters)

PL = 22.0log10d+ 28.0 + 20log10fc − 9 ∗ log10(d2 + (hBS − hUE)2) (2.7)

2.2. Propagation conditions 13

2.2.2 Non-line-of-sight propagation

We also distinguish two cases for the NLOS case:

• For Urban Macro (UMa)

PL = 161.04− 7.1 ∗ log10W + 7.5 ∗ log10h−

(24.37− 3.7

(h

hBS

)2)log10d

− 3 + 20log10fc −(3.2(log10(11.75hUT ))2 − 4.97))

)− 0.6 ∗ (hUT − 1.5)

(2.8)

• For Urban Micro (UMi)

PL = 36.7log10d+ 22.7 + 26log10fc − 0.3 ∗ (hUE − 1.5) (2.9)

where hBS and hUE are the heights of the eNodeB and the UEs respectively, dthe distance between them and fc is the carrier frequency.

Figure 2.8 shows an example of these two propagation effects.

FIGURE 2.8: LOS and NLOS propagation [12]


2.3 3D Beamforming

As an improvement for horizontal beamforming techniques, 3D beamforming al-lows an enhancement in the signal strength at the UE combining the vertical dimen-sion with the horizontal dimension using a 2D active array. Figure 2.9 shows thebeam pattern comparison between 2D beamforming and 3D beamforming.

FIGURE 2.9: Beamforming comparison between 2D case and 3D case[3]

The relative displacements of the antenna elements with respect to each otherintroduce relative phase shifts in the radiation vectors, which can then add con-structively in some directions or destructively in others. This is a direct consequenceof the translational phase-shift property of Fourier transforms: a translation in spaceor time becomes a phase shift in the Fourier domain.

To steer the beam directionality when transmitting, the beamforming coordina-tor controls the phase and relative amplitude of the signal at each transmitter. Andtherefore create a pattern of constructive and destructive interference in the wave-front [13] .

2.3. 3D Beamforming 15

2.3.1 Antenna modeling

In order to apply 3D beamforming, 2-dimensional antenna arrays are necessary. Thestructure of an antenna array comprises of antenna elements arranged in both hori-zontal and vertical directions as depicted in Figure 2.10.

FIGURE 2.10: Antenna elements scheme for a 2D array [3]

Since each antenna port contains V vertical antenna elements, the channel coef-ficient H(k) for the k-th UE is composed of S by T antenna ports (S is the numberof antenna ports on BS and T is the number of antenna ports on UE). This can bewritten as:

H(k) =

h(k)1,1 h

(k)2,1 ... h

(k)S,1

h(k)1,2 h

(k)2,2 ... h

(k)S,2

. . .

. . .

h(k)1,T h

(k)2,T ... h

(k)S,T

(2.10)

where

h(k)s,t =

V∑i=1

w(k)s,i .h

(k)i,s,t (2.11)

where h(k)s,t is the channel coefficient from the s-th antenna port of to the t-th an-tenna port of the k-th UE (we assume that each antenna port of UE has only oneantenna element).


As said before, the beamforming is a signal processing technique which appliesthe beamforming weights to adjust the phase and the amplitude of signals to formthe beam pattern toward the desired direction. The beamforming weights are ap-plied on each antenna elements as shown in Figure 2.10. The term ws,i representsthe beamforming weight of the i-th element on the s-th antenna port which affectsthe antenna pattern in the vertical direction [3] .

2.3.2 Array steering

An array is typically designed to have maximum directive gain at broadside, thatis, at φ = 90o (for an array along the x-axis.) The maximum of the array factor A(Ψ)corresponds to Ψ= kd cosφ= 0, so that |A|max = |A(0)|. Where k is the wavenumber,d the array spacing and φ the direction the beam is pointing at.

In order to steer "electronically" the antenna array to a different angle φ0 withoutchanging the physical orientation of the antenna, it can be achieved by wavenumbertranslation in Ψ-space, that is, replacing the broadside pattern A(Ψ) by the translatedpattern A(Ψ - Ψ0).

Ψ0 = kd cos(φ0) (2.12)

The translated wave number is:

Ψ′ = Ψ−Ψ0 = kd(cos(φ)− cos(φ0)) (2.13)

Therefore, the maximum of A’(Ψ) will coincide with the maximum of A(Ψ’),which occurs at Ψ = 0, or equivalently at Ψ = Ψ0, or at angle φ = φ0. Figure 2.11explains the scheme for steering a beam in the broadside direction. Figure 2.12, anexample of a steered beam in azimuth.

FIGURE 2.11: Horizontal array scheme to steer the beam in the broad-side direction [14]

2.3. 3D Beamforming 17

FIGURE 2.12: 30o steering angle in azimuth [22]

2.3.3 First Null Beamwidth (FNBW) and Half Power Beamwidth (HPBW)

In order to set a maximum number of beams, we calculate the FNBW and HPBW fordifferent number of beams N.

The FNBW is the angular span between the first pattern nulls adjacent to themain lobe and is represented as

GFNBW = 2[(π/2)− cos−1(λ/Nd)] (2.14)

where N is the number of beams and d denotes the spacing between antenna el-ements.

The HPBW is the angular separation, in which the magnitude of the radiationpattern decreases by 50% or -3dB from the peak of the main beam. It can be repre-sented as

GHPBW = 2[(π/2)− cos−1(1.39λ/πNd)] (2.15)

Note that these equations only hold for regularly spaced Uniform Linear Arrays(ULAs).

Figure 2.13 illustrates both FNBW and HPBW radiation patterns.

In table 2.1 are shown the corresponding values for FNBW and HPBW, consider-ing a broadside array and a spacing of half-wavelength between antenna elements,for 2 to 32 antenna elements. We see that with 32 beams we achieve a high beam res-olution, hence in this thesis we consider the maximum beam resolution to be equalto 32 beams.


FIGURE 2.13: Antenna radiation pattern [14]

TABLE 2.1: FNBW and HPBW for different number of beams.Broadside (Θ=π/2)

Number of beams FNBW (o) HPBW (o)

2 180 52.524 60 25.568 28.9 12.7016 14.36 6.3432 7.16 3.16

19

Chapter 3

Methodology

3.1 Codebook based precoding

The codebook based precoding is a promising technology adopted by LTE, whichfixes a common codebook comprising a set of vectors and matrices at both the trans-mitter and the receiver. To design this precoding technique, high precoding gain,lower feedback overhead and flexibility are mandatory to support various antennaconfigurations and different numbers of data streams.

In order to increase diversity, data rate, or both, the supported multi-antennatransmit modes employ either a Transmit Diversity (TxD) or Spatial Multiplexing(SM) transmission scheme. SM can be operated in two modes: Open Loop Spa-tial Multiplexing (OLSM) and Closed Loop Spatial Multiplexing (CLSM). The maindifference between them, is that in the latter, the optimum precoding matrix infor-mation is additionally fed back to the eNodeB by the UE. Based on whether a finitenumber of precoding matrices is used, the close-loop MIMO can be categorized ascodebook and non-codebook based precoding.[15]

The procedure of codebook-based precoding technology performs as follows:The UE gets the CSI from the Common Reference Signal (CRS) sent by the eNodeBand feeds back a Precoding Matrix Index (PMI). Then the eNodeB applies the spatialdomain precoding on the transmitted signal taking into account the PMI so that thetransmitted signal matches with the channel experienced by the UE. The PMI maybe changed by the eNodeB according to the instantaneous state and then will be sentback to UE. After the precoding operation, the UE receives the information from theeNodeB on what precoding matrix is used, which is utilized by the UE for demod-ulating the data. Table 3.1 lists the available precoders for the two-transmit-antennacase. For the four-antenna case, the codebook size increases to sixteen precoders,supporting up to four layers (v).

TABLE 3.1: LTE codebook for CLSM mode and two transmit antennasfor each of the possible number of layers (v)

Layers (v) Precoder codebook

1 1√2

[11

], 1√

2

[1−1

], 1√

2

[1i

], 1√

2

[1−i

]2 1√

2

[1 11 −1

], 1√

2

[1 1i −i

]

20 Chapter 3. Methodology

Nevertheless, codebook based precoders major drawback is it limitation up to 8-by-8 MIMO antennas. Since the number of ports assumed in this thesis exceeds thatquantity (such as 16, 32 or higher), this thesis will take into account non-codebookbased precoders. Those are the Maximum Ratio Transmission and the GeometryBased precoders.

3.1.1 Maximum Ratio Transmission precoder

The maximum ratio transmission resembles a matched filter where the gain of eachunit-norm beamforming direction, w is the strength of the corresponding channelcoefficient h and the phase makes the signal contribution from each channel coeffi-cient add up constructively. Generally, the term w is a 3-dimensional matrix definedper RB of dimensions the number of transmitting antennas (nTX) by the numberof receiving antennas (nRX) by the assigned RB. h is again a 3-dimension matrix(dimensions: nRX x nTX x RB). For this thesis though, the nRX will be 1, reducingthe matrices to vectors. Therefore, the vector w is calculated dividing the channelcoefficient by its Euclidean norm as showed in 3.1

wj,k =hj,k‖hj,k‖2

(3.1)

where j represents each of the BSs (in this Thesis we consider only one) and k rep-resents each of the users. The inner product between the precoding vector and thechannel coefficient is therefore maximized, which protects the useful signal againstchannel fading and gives a close-to-optimal solution.

Thus, the MRT maximizes the SNR at the mobile station in multi-antenna trans-missions, providing the optimal beamforming directions in a low-SNR regime, inde-pendently of which point in the performance region we are interested in. The exactoperating point is determined by the power allocation. After the MRT selects thebeamforming directions w, the power allocation p determines the operating point inthe performance region that is achieved by the heuristic transmit strategy[16] .

Therefore, the SINR is computed as:

SINRk =pj,k ∗ ρk,k

σ2k +∑

i 6=k pj,i ∗ ρi,k(3.2)

where SINR is the signal-to-interference-plus-noise ratio, σ2k the noise power, andρ is fixed and equal to:

ρi,k = |hHj,k ∗ Cj,k ∗wj,i|2 (3.3)

whereCj,k is the covariance matrix of each base station j and user k. The subindexi represents each of the interfering co-users[17] .

3.1. Codebook based precoding 21

3.1.2 Geometry based precoder

In order to reduce the feedback overhead at the UEs, a precoder which does not needfull knowledge of the channel is desirable. Thus, the so-called Geometry based pre-coder has the advantage over the MRT precoder, that only the location of the user isneeded.

In this regard, azimuth and elevation angles from the direct link between eN-odeB and UE is calculated. Complex weights are calculated and applied for eachantenna port as number of antenna elements in the vertical direction

wn =1√Ne−i(n−1)∗d∗sin(ϕ0)∗sin(θ)∗ 2πλ (3.4)

where N is the number of ports set on the antenna configuration, n denotes theweight for the n-th antenna element horizontally (antenna port), ϕ0 is the azimuthangle (from 0 to 2π) and θ, the elevation angle (from 0 to π ).

In case we have LOS propagation, the direct link is strong enough to capturemost of the channel energy. On the other hand, if there is NLOS between the eN-odeB and the UEs, we lack this direct link, and thus, the precoding matrix (vector incase that the nRX is 1) calculated for this link may not be optimal due to destructiveinterference and multipath components.

This undesirable effect results in a degradation of the service experimented bythe users. In order to alleviate this effect, not only the direct link but also all theazimuth angles in a 2π circumference, must be taken into account. Thus, the linkwho gives the best performance will be selected, optimizing the resulting precodingmatrix. This improved method is called Exhaustive search over azimuth.

3.1.3 Exhaustive search over azimuth

In order to improve the chosen precoder, especially in the NLOS environment, wepropose to perform exhaustive search over azimuth. This search explores the opti-mal precoding matrix throughout the whole azimuth angles (from 0 to 2π)

ϕ = ϕ0 + ∆ϕ (3.5)

where ϕ0 is the azimuth departure angle, corresponding to the direct link be-tween eNodeB and the UE, ∆ϕ, the whole azimuth angles vector (evenly-spacedfrom 0 to 2π) with dimension, the number of antenna ports selected. Finally ϕ isan array with every azimuth angles that search for the maximum channel energy.Figure 3.1 displays these concepts.

We calculate for each azimuth angle the precoding matrix w. It is done with thefollowing equation:

wn =1√Ne−i(n−1)∗d∗sin(ϕ)∗sin(θ)∗ 2π

λ (3.6)

which is similar to Equation 3.4, but now, instead of having just one azimuthdeparture angle ϕ0, we have an array with all the azimuth angles between 0 and 2π.


FIGURE 3.1: Example of the exhaustive search over azimuth for the 4antenna ports case. Direct link azimuth angle ϕ0 in black, rest of the

angle sweep in light blue

The exhaustive search over azimuth, needs full knowledge of the channel h.Hence, for the k-th RB we multiply the channel vector h with the precoding vec-tor w and then choose the angle which maximizes the norm,

maxmRBean(

∥∥hHk ∗wk

∥∥) (3.7)

In Figure 3.2 a scheme of how the exhaustive search chooses the best angle per RBis depicted. The first column corresponds to the azimuth angle (ϕ) of the direct linkbetween the eNodeB and the UE. The following columns (as many as antenna ports)complete the circumference from ϕ to ϕ + 2π. Thus, the more ports the antenna uses,the more angles can be searched and more accurate will be the examination of theoptimal precoding matrix.

3.2. Position uncertainty 23

FIGURE 3.2: Azimuth angle that captures most of the channel energyper RB.

3.2 Position uncertainty

In order to be more realistic, we assume that the CSI is delayed. Since the user ismoving, this can lead to an outdated CSI. In our work we account for an uncer-tainty region around the user position. This implies that the angles calculated withrespect to the location of the user differs from its actual position. Therefore, this off-set reduces the performance of the precoders. An example of an uncertainty area isrepresented in figure 3.3.

FIGURE 3.3: Scheme of uncertainty areas around the UEs [23]

The offset x from the actual user position is modeled as Gaussian distributionsas follows:

x ∼ N (µ, σ2) (3.8)


TABLE 3.2: Maximum deviation for UEs for given speeds of 30 km/hand 120 km/h

Velocity Max. deviation

30 Km/h 8.3 meters120 Km/h 33.3 meters

Where µ is the actual location of the user and σ2 is the variance that depends onthe velocity of the user and the time between measurements,

σ2 = (v ∗ t)2 (3.9)

where v is the UEs speed (in ms ), and t the time between measurements (we con-

sider the time between two measurements to be 1s or 1000 TTIs).

Table 3.2 summarizes the maximum uncertainty areas σmax that can be experi-mented for the eNodeB when the users move at 30 km/h and 120 km/h. The termσmax is calculated as the maximum deviation between the actual location of the userand the location the eNodeB expects the UE to be. This value is obtained as an aver-age over 100 realizations with a single eNodeB and a single UE always in the sameposition.

25

Chapter 4

Simulations

In this chapter, we describe the results of the simulations that we have performedin order to compare the three different precoders (MRT, Geometry based and Ex-haustive search). Firstly, we define the scenario utilized on the simulations. Afterthe scenario definition, we describe two different set-ups regarding the UE locations.The first one considers fixed UE locations, while the second part assumes randomUEs positions throughout the highway.

4.1 Scenario definition

The scenario setup for the simulations consists of a single eNodeB that covers 120o

and encompasses a highway of 25 meters width by 200 meters length. The eNodeBantenna consists of 10 elements in each antenna port, and up to 32 ports. The carrierfrequency is 2 GHz with 10 MHz of bandwidth. A co-polarized multiple-antennaarray is assumed with a spacing between antenna elements of λ

2 (corresponding to7.49 cm). Finally, an electrical downtilt of 108.4o is assumed (900 represents the hor-izontal direction perpendicular to the eNodeB ).

The distance between the eNodeB and the highway is 58 meters as seen in Fig-ure 4.1 and it has been selected in order to cover a distance of 200 meters of highwaywith the eNodeB. Throughout the highway, there are placed 11 users, each of themwith a single omni-directional antenna.

To reduce the simulations complexity, especially when we deal with a very highnumber of antenna elements, (see [2]), we consider no interfering base stations. Inorder to account for interference, we increase the noise level. To define a realisticinterference level, we perform simulations with an hexagonal arrangement ofeNodeBs with 6 interfering nodes and a 500 m inter site distance.

Tables 4.1, 4.2 and 4.3 gathers the main parameters used in the simulations.

26 Chapter 4. Simulations

TABLE 4.1: Simulation parameters

Parameter Value(s)

Carrier frequency 2 GHzLTE Bandwidth 10 MHzWavelength (λ) 14.99 cmTX power 40 dBTTI 10Feedback channel delay 3 TTIMacro-site deployment Hexagonal gridScenarios 3D-UMa, 3D-UMiPropagation conditions LOS, NLOSOTOI Only outdoor

TABLE 4.2: Antenna parameters

Parameter Value(s)

Antenna ports N=2,4,8,16,32Antenna elements per port M=10UE antenna elements 1Horizontal spacing between elements 0.5λVertical spacing between elements 0.5λAntenna polarization Co-polSlant angle 0o

Mechanical downtilt 0o

Electrical downtilt 108.435o

Sector antenna height 25 metersUE antenna height 1.5 metersTransmission mode TM7 (Non-codebook based precoding)UE antenna pattern Omni directional

TABLE 4.3: Scenario parameters

Parameter Value(s)

Number of eNodeB 1Number of eNodeB sectors 1Number of UEs 11UE distribution Uniform along roadHighway length 200 metersHighway width 25 metersDistance eNodeB-highway 58 metersUE speed 30 km/h, 120 km/hMaximum uncertainty area σmax= 11 meters, 48 meters

4.2. Fixed user location 27

FIGURE 4.1: Scenario scheme of a eNodeB covering a 120 degreesarea.

4.2 Fixed user location

Different settings have been studied regarding the propagation conditions (LOS ver-sus NLOS) and the position uncertainty of the users for the three precoding tech-niques. The UEs are located in fixed positions in the middle of the highway andare placed all 20 meters. UEs are assumed to be at 1.5 meters height. This resultsin eleven user for a highway length of 200 meters. The following image shows thedescribed setup:

FIGURE 4.2: Fixed UE locations throughout the highway.


4.2.1 Precoders comparison for fixed UE location

Firstly we compare the three precoders for each number of beams from 2 to 32. Thecontinuous line is the MRT precoder, the dashed line is the Geometry based pre-coder and the dotted line is the Exhaustive Search precoder. The x-axis representsthe position of each user every 20 meters with respect to the throughput (in Mbps)in the y-axis. The UEs are placed with respect to the horizontal position of the basestation (x=0), falling in the negative part of the y-axis if they are located on the leftpart of the highway and in the positive part of the x-axis otherwise.

-100 -80 -60 -40 -20 0 20 40 60 80 100

x position (m)

0

0.5

1

1.5

2

2.5

3

Thro

ughput (M

bps)

Comparison between precoders for LOS propagation

2 beams MRT

4 beams MRT

8 beams MRT

16 beams MRT

32 beams MRT

2 beams Geom.

4 beams Geom.

8 beams Geom.

16 beams Geom.

32 beams Geom.

2 beams Geom. Ex.

4 beams Geom. Ex.

8 beams Geom. Ex.

16 beams Geom. Ex.

32 beams Geom. Ex.

FIGURE 4.3: Comparison between precoders for LOS propagation.

As shown in Figure 4.3, the Geometry based and the Exhaustive search precodersget similar performance and slightly higher throughput than the MRT precoder. Thisis reasonable since in the LOS case the Exhaustive search catches most of the chan-nel energy on the angle established with the direct link between the base station andthe UE, which is exactly the angle chosen by the Geometry Based precoder. On theother side, the MRT performs acceptably good, but a bit worse due to the lack ofknowledge of the exact position of the UE.

Another important fact is that the performance is slightly better in the center ofthe highway than at the edges. This is because the base station boresight direction isphysically pointing to the middle of the highway.

4.2. Fixed user location 29

4.2.2 Uncertainty versus Non-uncertainty comparison

In order to characterize the UE location uncertainty experimented by the base stationdue to moving users, Figure 4.4 shows the average throughput for each user whenthe location is known (denoted by a continuous line) and when the UE location atmaximum speed is unknown (denoted by a dashed line) for the Exhaustive Searchprecoder. We assume a velocity of 120 km/h and a maximum deviation uncertaintyof 48 meters.

-100 -80 -60 -40 -20 0 20 40 60 80 100

x position (m)

0

0.5

1

1.5

2

2.5

Thro

ughput (M

bps)

Comparison between Uncertainty and No Uncertainty for LOS propagation

2 beams Geom. Ex.

4 beams Geom. Ex.

8 beams Geom. Ex.

16 beams Geom. Ex.

32 beams Geom. Ex.

2 beams Geom. Ex. Uncertainty





FIGURE 4.4: Uncertainty versus No Uncertainty for LOS propagation.

The simulations consider from 2 to 32 beams. As we increment the beam resolu-tion, the gap between the uncertainty and non-uncertainty performances (regardingsame beam resolution curves) grows. This is because, as we increase the number ofbeams, the beamwidth lowers. In case of full knowledge of the user location, thisprovokes an improvement of the performance as the directivity increases. However,if the user location is uncertain, since the departure angles (azimuth and elevation)calculated for the direct link between the eNodeB and the UEs are not exactly point-ing to the user location, the chances of hitting the user diminishes as the beams getsharper.


4.2.3 LOS versus NLOS comparison

Regarding the propagation conditions, we perform simulations considering bothLOS and NLOS. Figure 4.5 compares both propagation conditions for the Geome-try based and the Exhaustive Search precoders for 16 and 32 beams. The blue andorange lines correspond to LOS propagation and the green and purple to the NLOSpropagation.

-100 -80 -60 -40 -20 0 20 40 60 80 100

x position (m)

0

0.5

1

1.5

2

2.5

3

Th

rou

gh

pu

t (M

bp

s)

Comparison between LOS and NLOS propagation for Geom. and Exhaustive precoders

16 beams Geom. NLOS

32 beams Geom. NLOS

16 beams Geom. Ex. NLOS


16 beams Geom. LOS

32 beams Geom. LOS

16 beams Geom. Ex. LOS


FIGURE 4.5: Comparison between LOS and NLOS propagation forExhaustive Search precoder having 16 and 32 antenna ports.

Note that in the NLOS propagation case, the response throughout the position ofthe users is flatter than in the LOS propagation case. This is fundamentally becauseno direct link component between the base station and the UEs is available.

For LOS case, both precoders perform similar as explained in section 4.2.1. InNLOS case, however, contrasting performances appear between the precoders. Whilethe Exhaustive search improves its performance as the number of beams increases,no visible enhancement can be appreciated for the Geometry Based precoder.

Furthermore, no improvement is achieved even if we increase the number ofbeams. The lack of a direct link component, the multipath effect, as well as theGeometry Based precoder that wrongly chooses the physical direct link as the onewho captures most of the channel energy, incurs in no further improvement, even ifthe beams directivity increases as we increment the number of beams.

4.3. Random user location 31

4.3 Random user location

In the second part of this chapter, the setup chosen, assumes uniformly random po-sitions of the UEs throughout the highway. Figure 4.6 shows an example of how theusers could be placed. The simulations averaged the uniformly random UE locationsover 400 realizations.

FIGURE 4.6: Example of one realization of random UE locationsthroughout the highway.

4.3.1 Precoders comparison for random UE location

As before for fixed UE locations, now we study the performance for the GeometryBased and the Exhaustive Search precoders for the random situation. The dashedlines represent the Geometry based approach and the dotted lines, the ExhaustiveSearch precoder.

0 0.5 1 1.5 2 2.5 3

throughput (Mbps)

0

0.2

0.4

0.6

0.8

1

ecdf

Comparison between precoders for random UE location.

2 beams Geom.

4 beams Geom.

8 beams Geom.

16 beams Geom.

32 beams Geom.

2 beams Geom. Ex.

4 beams Geom. Ex.

8 beams Geom. Ex.

16 beams Geom. Ex.

32 beams Geom. Ex.

FIGURE 4.7: Comparison between precoders for LOS propagation forrandom location UEs.


Once again, both precoders perform similarly for the LOS propagation case. TheECDF shows almost vertical slopes for both events, which means that all users areserved almost equally (similarly as in the fixed UEs locations, they are not straightvertical lines because users located at the edges of the highway, perceive a bit worseperformance than the ones near to the middle of the highway due to the base stationpointing direction). It is noticeable that as we increment the number of antennaports, the Geometry Based precoder performance gets better even surpassing theExhaustive Search precoder performance. This deviations are actually expected. Aswe increment the number of simulations, this offset tends to disappear.

4.3.2 LOS versus NLOS comparison (Random case)

Finally, the last case of study is the comparison between the LOS (blue and orangelines) and NLOS (purple and green lines) propagation conditions. Figure 4.8 repre-sents the Geometry Based precoder (denoted by continuous lines) and the Exhaus-tive Search precoder (denoted by dashed lies) for 16 and 32 beams.

0 0.5 1 1.5 2 2.5 3

throughput (Mbps)

0

0.2

0.4

0.6

0.8

1

ecdf

Comparison between LOS and NLOS propagation for random UE location.

16 beams Geom. LOS

32 beams Geom. LOS



16 beams Geom. NLOS

32 beams Geom. NLOS



FIGURE 4.8: Comparison between LOS and NLOS propagation forGeometry based and Exhaustive Search precoders having 16 and 32

antenna ports.

As expected, in LOS propagation conditions, both precoders perform better thanin the NLOS case. It is also noticeable that the slopes in the NLOS propagation situ-ation are steeper than for the LOS case. This demonstrates that in a NLOS situation,the response throughout the whole highway is flatter (slopes at the edges decreasemore gradually) than when the direct link component is accessible. Finally note thatthe distance between the Geometry based and the Exhaustive Search curves enlargesfor the NLOS case as we increment the resolution of the beams. This fact shows thata higher beam resolution produces a larger improvement in the performance of theExhaustive Search precoder than in the Geometry Based precoder performance.

4.3. Random user location 33

4.3.3 LOS versus NLOS comparison for all precoders (Random case)

Finally, to give an overall perspective, we compare all the cases including the dif-ferent precoders and propagation conditions. To avoid confusion, we only comparethe 16 antenna ports case. In purple NLOS conditions are depicted and in blue, theLOS propagation case. The MRT curves are denoted as dashed lines, the Geometrybased curves as continuous lines and the Exhaustive Search curves as dotted lines.

0 0.5 1 1.5 2 2.5

throughput (Mbps)

0

0.2

0.4

0.6

0.8

1

ecdf

Comparison between LOS and NLOS propagation for random UE location.

16 beams Geom. LOS


16 beams MRT LOS

16 beams Geom. NLOS


16 beams MRT NLOS

FIGURE 4.9: Comparison for 16 antenna ports between LOS andNLOS propagation for all precoder.

Once again, both the Geometry based and the Exhaustive Search precoders per-form quite similar for the LOS case and slightly better than the MRT precoder (sincethe position of the user is known).

For the NLOS case, poor performance is obtained for the Geometry based pre-coder (lacking for the direct link component). On the contrary, precoders that takeinto account different angles and maximize the channel energy captured (MRT andExhaustive Search precoders) have a noticeable enhancement, being the ExhaustiveSearch precoder the best of the three aforementioned precoders in performance.

34

Chapter 5

Conclusion

Throughout the thesis, we aim to give an overall view about the vehicular communi-cations, focusing on the V2I case. For this purpose, we consider a highway scenariowith moving users. The main motivation for this research is that, using the 3GPP 3Dmodel, we aim for giving service to small groups of moving users (around a dozen).This is possible by using 2-dimensional planar antenna arrays, which can model thebeams and steer them not only horizontally, but also in the vertical domain. This isthe so-called 3D-beamforming. This suppose an improvement regarding the currentLTE standard since it cannot stand up both efficient and reliable wireless communi-cation at such high mobility users.

These challenges must be overcome and for that purpose, we propose three dif-ferent non-codebook based precoding techniques approaches to calculate the pre-coding matrix. The Maximum Ratio Transmission precoder, is a suboptimal solu-tion that needs full knowledge of the channel. On the other hand, the GeometryBased precoder, is an approach that only considers the location of the UE. Finally, asa combination of the two previous approaches, the Exhaustive Search over azimuthprecoder, is an upgraded version of the Geometry Based precoder, which needs both,full channel knowledge and the position of the users.

We also describe the Vienna LTE-A Downlink System Level Simulator that isused for running the simulations and we give an explanation about the reduction ofcomplexity and run-time of the simulations.

Regarding the results, the simulations have displayed that for LOS propaga-tion situation, the close-to-optimal MRT performs slightly worse than the GeometryBased and the Exhaustive Search precoders. This deterioration is due to a certaininter-user interference that worsens the performance. With respect to the GeometryBased precoder, it behaves exactly the same as the Exhaustive Search in LOS casesince the direct link is available and thus both of them capture most of the channelenergy in that direction.

On the other hand, for the NLOS propagation case, the Geometry Based per-forms noticeably worse than the other precoders. Since no direct link is available,and due to multipath, the precoding matrix calculated for the azimuth and eleva-tion departure angles may not capture most of the channel energy. Conversely, theExhaustive Search over azimuth performs the best since it keeps examining the restof the angles until it locates the path which maximizes the performance. Unfortu-nately, this solution needs full knowledge of the channel and the location of the user,which incurs in big feedback overhead at the UEs.

Chapter 5. Conclusion 35

Finally, regarding the precision of the antenna (by pointing the actual locationof the user), we have shown that performance deteriorates the faster the UEs movethrough the highway. This is because the uncertainty area grows with the increasingvelocity and thus, the geometry chosen to maximize the performance is more likelyto be incorrect.

36

Bibliography

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