measurement-based millimeter-wave radio channel ... · the spectrum shortage at microwaves has...

This thesis focuses on mm-wave channel modeling for future 5G wireless communication systems. The main contributions of the work include simulations tools and insights acquired through channel measurements. The work emphasize the need for more detailed descriptions of the model frameworks and environment descriptions at mm-waves compared to lower frequencies.

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ISBN 978-952-60-6971-5 (printed) ISBN 978-952-60-6972-2 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) Aalto University School of Electrical Engineering Department of Radio Science and Engineering www.aalto.fi

BUSINESS + ECONOMY ART + DESIGN + ARCHITECTURE SCIENCE + TECHNOLOGY CROSSOVER DOCTORAL DISSERTATIONS

Jan Järveläinen M

easurement-Based M

illimeter-W

ave Radio C

hannel Simulations and M

odeling A

alto U

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2016

Department of Radio Science and Engineering

Measurement-Based Millimeter-Wave Radio Channel Simulations and Modeling

Jan Järveläinen

DOCTORAL DISSERTATIONS

Aalto University publication series DOCTORAL DISSERTATIONS 164/2016


Jan Järveläinen

A doctoral dissertation completed for the degree of Doctor of Science (Technology) to be defended, with the permission of the Aalto University School of Electrical Engineering, at a public examination held at the lecture hall S1 of the school on 30 September 2016 at 12.

Aalto University School of Electrical Engineering Department of Radio Science and Engineering

Supervising professors Prof. Pertti Vainikainen Prof. Antti Räisänen Assist. Prof. Katsuyuki Haneda Thesis advisor Dr. Aki Karttunen Preliminary examiners Prof. Henry Bertoni, New York University, USA Prof. Wout Joseph, Ghent University, Belgium Opponents Prof. Henry Bertoni, New York University, USA Prof. Preben Mogensen, Aalborg University, Denmark

Aalto University publication series DOCTORAL DISSERTATIONS 164/2016 © Jan Järveläinen ISBN 978-952-60-6971-5 (printed) ISBN 978-952-60-6972-2 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) http://urn.fi/URN:ISBN:978-952-60-6972-2 Unigrafia Oy Helsinki 2016 Finland

Abstract Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi

Author Jan Järveläinen Name of the doctoral dissertation Measurement-Based Millimeter-Wave Radio Channel Simulations and Modeling Publisher School of Electrical Engineering Unit Department of Radio Science and Engineering Series Aalto University publication series DOCTORAL DISSERTATIONS 164/2016 Field of research Radio Engineering Manuscript submitted 21 April 2016 Date of the defence 30 September 2016 Permission to publish granted (date) 10 June 2016 Language English

Monograph Article dissertation Essay dissertation

Abstract The spectrum shortage at microwaves has necessitated the use of millimeter-wave (mm-wave) frequencies in future fifth generation (5G) wireless communication systems. In order to evaluate the performance of 5G networks at mm-waves, propagation channels in various scenarios must be properly characterized and modeled. This thesis aims at providing important insights, methods and tools for mm-wave channel modeling.

Deterministic field prediction tools, previously used mainly for coverage analysis, are increasingly used also for stochastic channel model parametrization or for evaluating system performance. The prediction accuracy of such tools, e.g., ray tracing, may be compromised due to missing details in the environment databases. In this work, a novel field prediction tool relying on accurate environmental information in the form of point clouds is developed. The prediction method parameters are tuned by measurements, and the performance is validated in several indoor environments by comparing predicted and measured channels in terms of power, delay and angular metrics. The results demonstrate excellent prediction accuracy in both line-of-sight (LOS) and non-line-of-sight links.

As the upcoming 5G mm-wave deployment will be made in new scenarios, channel sounding is essential to acquire knowledge about the propagation characteristics and the applicability of existing channel model frameworks. This work presents insights obtained from channel sounding results conducted in environments such as offices and a shopping mall in the 60- and 70-GHz bands. Unlike existing channel models, clustering was not found apparent in the large indoor environments, allowing a simpler spatio-temporal channel model structure to be developed. A parametrization of the WINNER II model at 60 GHz and a study on the depolarization at mm-waves is also presented. The results show that polarization is better preserved at mm-waves than at lower frequencies. Moreover, a novel method to evaluate LOS probability based on point clouds is demonstrated.

The last part of the thesis deals with the use of stochastic and site-specific channel models in evaluating the performance of mm-wave wireless systems. The point cloud-based simulation tool is used to study the mutual orthogonality of mm-wave links equipped with large antenna arrays. The result shows that compared to microwave frequencies, the number of active users should be smaller at mm-waves to guarantee efficient spatial multiplexing.

Keywords Millimeter-wave, channel modeling, prediction, point cloud ISBN (printed) 978-952-60-6971-5 ISBN (pdf) 978-952-60-6972-2 ISSN-L 1799-4934 ISSN (printed) 1799-4934 ISSN (pdf) 1799-4942 Location of publisher Helsinki Location of printing Helsinki Year 2016 Pages 167 urn http://urn.fi/URN:ISBN:978-952-60-6972-2

Tiivistelmä Aalto-yliopisto, PL 11000, 00076 Aalto www.aalto.fi

Tekijä Jan Järveläinen Väitöskirjan nimi Mittauksiin perustuva millimetriaaltoalueen radiokanavasimulointi ja -mallinnus Julkaisija Sähkötekniikan korkeakoulu Yksikkö Radiotieteen ja -tekniikan laitos Sarja Aalto University publication series DOCTORAL DISSERTATIONS 164/2016 Tutkimusala Radiotekniikka Käsikirjoituksen pvm 21.04.2016 Väitöspäivä 30.09.2016 Julkaisuluvan myöntämispäivä 10.06.2016 Kieli Englanti

Monografia Artikkeliväitöskirja Esseeväitöskirja

Tiivistelmä Mikroaaltotaajuuksien tiheä käyttö tietoliikenteessä on johtanut radiospektrin puutteeseen, jonka seurauksena tulevassa viidennen sukupolven (5G) langattomissa järjestelmissä myös millimetriaaltoaluetta on hyödynnettävä. Jotta 5G-verkkojen suorituskykyä olisi mahdollista arvioida millimetriaalloilla, etenemiskanavaa on mallinnettava eri ympäristöissä. Tämän väitöskirjan tavoitteena on tarjota tärkeitä havaintoja, menetelmiä ja työkaluja millimetriaaltoalueen radiokanavamallinnusta varten.

Deterministiset radioaaltojen etenemisen ennustusmenetelmät, joita aikaisemmin on käytetty verkkojen kuuluvuuden tutkimiseen, ovat yhä enemmän varteenotettavia työkaluja myös stokastisten kanavamallien parametrisoinnissa ja radiojärjestelmien suorituskykyarvioinnissa. Ennustusmenetelmien, kuten säteenseurannan, ennustustarkkuus saattaa kärsiä ympäristön tietokantojen huonosta tarkkuudesta johtuen. Tässä työssä on kehitetty uusi ennustusmenetelmä, jossa käytetään erittäin tarkkoja pistepilviä kuvaamaan ympäristöä. Menetelmän parametrit kalibroidaan mittausten avulla ja toimintakykyä arvioidaan useissa sisätilaympäristöissä vertaamalla simuloituja ja mitattuja kanavia eri tehon, viiveen ja kulma-alueen mittareilla. Tulokset osoittavat ennustustarkkuuden erinomaiseksi sekä näköyhteyden ollessa vapaa että sen puuttuessa.

Koska millimetriaaltoalueen 5G-verkkoja sijoitetaan uusiin ympäristöihin, kanavamittaukset ovat oleellisia radiokanavien ominaisuuksien ja olemassa olevien kanavamallien käyttökelpoisuuden tutkimiseksi. Tässä työssä esitetään havaintoja kanavamittauksista, joita on suoritettu esim. toimistotiloissa ja kauppakeskuksessa 60 ja 70 GHz:n taajuusalueilla. Vastoin yleisiä kanavamallinnusperiaatteita, mittaukset osoittavat että monitiekomponenttien ryhmittyminen ei ole voimakasta tutkituissa ympäristöissä, mikä sallii yksinkertaisemman rakenteen tilassa ja ajassa määritellylle kanavamallille. Lisäksi WINNER II-mallille lasketaan parametrit 60 GHz:llä ja polarisaation kääntymistä tutkitaan sisätilaympäristöissä. Tulokset osoittavat polarisaation säilyvän paremmin millimetriaalloilla alempiin taajuuksiin verrattuna. Työssä esitetään myös uusi menetelmä näköyhteysreitin todennäköisyyden laskemiseksi pistepilviä hyödyntäen.

Työn viimeinen osio käsittelee determinististen ja stokastisten kanavamallien sovelluskohteita langattomien järjestelmien suorituskykyarvioinnissa. Pistepilveen pohjautuvaa ennustusmenetelmää käytetään tutkimaan isoilla antenniryhmillä varustettujen millimetriaaltolinkkien keskinäiskorrelaatiota. Tuloksissa todetaan, että aktiivisten käyttäjien määrän on oltava millimetriaaltotaajuuksilla pienempi mikroaaltotaajuuksiin verrattuna, jotta tilallista limitystä (eng. spatial multiplexing) voidaan hyödyntää mahdollisimman hyvin. Avainsanat Millimeteriaallot, radiokanavamallinnus, ennustaminen, pistepilvi ISBN (painettu) 978-952-60-6971-5 ISBN (pdf) 978-952-60-6972-2 ISSN-L 1799-4934 ISSN (painettu) 1799-4934 ISSN (pdf) 1799-4942 Julkaisupaikka Helsinki Painopaikka Helsinki Vuosi 2016 Sivumäärä 167 urn http://urn.fi/URN:ISBN:978-952-60-6972-2

Sammandrag Aalto-universitetet, PB 11000, 00076 Aalto www.aalto.fi

Författare Jan Järveläinen Doktorsavhandlingens titel Mätningsbaserad simulation och modellering av utbredningskanaler på millimetervågsområdet Utgivare Högskolan för elektroteknik Enhet Institutionen för radiovetenskap och radioteknik Seriens namn Aalto University publication series DOCTORAL DISSERTATIONS 164/2016 Forskningsområde Radioteknik Inlämningsdatum för manuskript 21.04.2016 Datum för disputation 30.09.2016 Beviljande av publiceringstillstånd (datum) 10.06.2016 Språk Engelska

Monografi Sammanläggningsavhandling

Sammandrag Den begränsade tillgängligheten av radiospektrum på mikrovågsområdet har lett till att framtida femte generationens (5G) nätverk också måste använda sig av millimetervågsområdet (mm-vågsområdet). För att utvärdera prestationsförmågan av nätverk på mm-vågsområdet måste utbredningskanalen karakteriseras och modelleras i olika omgivningar. Denna doktorsavhandling strävar efter att erbjuda viktiga iakttagelser, metoder och redskap för kanalmodellering på mm-vågsområdet.

Traditionellt har redskap för att förutspå utbredningskanaler använts främst för att analysera hörbarheten i mobilnätverk, men nuförtiden behövs dessa redskap också för att parametrisera stokastiska kanalmodeller samt för att utvärdera prestandan i trådlösa system. Estimeringsprecisionen för dylika redskap, som tex. strålföljning (eng. ray tracing), kan försämras av att omgivningsmodellen inte är tillräcklig detaljerad. I detta arbete presenteras en ny metod för att förutspå utbredningskanaler som baserar sig på noggranna omgivningsmodeller i form av punktmoln. Modellparametrarna kalibreras med hjälp av radiokanalmätningar och funktionaliteten bekräftas i flera interiörer genom att jämföra simulerade och mätta radiokanaler beträffande effekt, fördröjning och vinklar. Resultaten tyder på en hög estimeringsprecision både med fri siktlinje samt med siktlinjen blockerad.

Eftersom 5G-nätverk på mm-vågsområdet kommer att placeras i flera nya omgivningar är det nödvändigt att utföra radiokanalmätningar för att skaffa sig information om utbredningskanalens egenskaper och användbarheten av existerande kanalmodeller. I detta arbete presenteras insikter som erhållits via radiokanalmätningar i tex. kontorsutrymmen och ett köpcentrum i frekvensbanden kring 60 och 70 GHz. I motsats till populära kanalmodeller som grupperar radiovågskomponenter i kluster, påvisar mätningarna ingen klustring vilket möjliggör en enklare kanalmodell i rum och tid. Mätningarna används också till att parametrisera WINNER II kanalmodellen vid 60 GHz samt till att studera hur polariseringen förändras under utbredningen. Resultaten visar att polariseringen bevaras bättre på mm-vågsområdet jämfört med lägre frekvenser. Vidare presenteras en ny metod för att utvärdera sannolikheten för fri siktlinje med hjälp av punktmoln.

Avhandlingen sista del ger en översikt av olika användningsändamål för både deterministiska och stokastiska kanalmodeller. Estimeringsmetoden som baserar sig på punkmoln används för att studera korrelationen mellan användare på mm-vågsområdet som är utrustade med massiva gruppantenner. Resultaten tyder på att antalet aktiva användare bör vara lägre än på mikrovågsområdet för att garantera effektiv rumsmultiplexering.

Nyckelord Millimetervågor, modellering av utbredningskanaler, estimering ISBN (tryckt) 978-952-60-6971-5 ISBN (pdf) 978-952-60-6972-2 ISSN-L 1799-4934 ISSN (tryckt) 1799-4934 ISSN (pdf) 1799-4942 Utgivningsort Helsingfors Tryckort Helsingfors År 2016 Sidantal 167 urn http://urn.fi/URN:ISBN:978-952-60-6972-2

Dei Gratia

9

Preface

The work for this thesis begun five years ago, in a time when I felt I didn’t

know anything. The hopes for learning something useful for the future

have truly been fulfilled, thanks to interesting topics, patient colleagues

and the passing of time. I want to express my gratitude to the late Prof.

Pertti Vainikainen for guiding me into the wonders of wireless communi-

cations. I will always be amazed by Pertti’s extraordinary combination of

expertise and kindness.

My journey from a struggling doctoral student to a confident researcher

would not have been possible without the help of my supervisor, Prof.

Katsuyuki Haneda. Thank you for teaching me about logical reasoning

and for expanding my horizons. I am also very grateful to Dr. Veli-Matti

Kolmonen, Dr. Mikko Kyrö and Dr. Aki Karttunen for instructing and

encouraging me during my work. I appreciate your lessons in leadership

and practical research work. Many thanks to our research group and the

whole RAD department for creating a warm and optimistic atmosphere

that enables top-level research. Especially, I want to thank all the profes-

sors for always having time with even the most trivial questions.

Besides the colleagues in our department, I have had the opportunity

to work with many great experts during research visits and projects. I

would like to thank Prof. Jun-ichi Takada, Prof. Vittorio Degli-Esposti,

Dr. Kenichi Takizawa, Mr. Pekka Kyösti, Mr. Tommi Jämsä and Mr. Jyri

Putkonen for valuable discussions and insights.

The preliminary examiners of this thesis, Prof. Henry Bertoni and Prof.

Wout Joseph, deserve my deepest gratitude for their contribution in mak-

ing the thesis more comprehensible. In addition I want to acknowledge

the effort of Dr. Joni Vehmas for the proofreading.

Furthermore, I am thankful for the funding received by Aalto ELEC

Doctoral School, the Walter Ahlström Foundation, the Scandinavia-Japan

11

Preface

Sasakawa Foundation, Svenska tekniska vetenskapsakademien i Finland,

the COST Action IC1004 and the HPY Research Foundation.

Last, I want to give my most sincere thanks to all my friends for their

support and “unbelievably deep” interest in my research topic. Pappa,

mamma, Hanna och Henrik, jag är oerhört tacksam för ert stöd och er

omsorg om mig. Freja, tack för din kärlek och din stora vishet. Filip,

tack för att du vill ha mig med i dina lekar. Till sist vill jag tacka Gud

för Hans stora nåd och förlåtelse, och för alla människor som möjliggjort

denna avhandling.

Helsinki, August 22, 2016,

Jan Järveläinen

12

Contents

Preface 11

Contents 13

List of Publications 16

Author’s Contribution 18

List of Abbreviations 20

List of Symbols 22

1. Introduction 24

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

1.2 Objectives and contribution of the thesis . . . . . . . . . . . . 25

2. The Wireless Propagation Channel 28

2.1 Propagation mechanisms at mm-waves . . . . . . . . . . . . 28

2.1.1 Reflection and transmission . . . . . . . . . . . . . . . 28

2.1.2 Scattering . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.1.3 Diffraction . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.2 Characterization of wireless channels . . . . . . . . . . . . . 33

2.2.1 The double-directional channel . . . . . . . . . . . . . 33

2.2.2 Channel characterization metrics . . . . . . . . . . . . 34

2.2.3 Depolarization . . . . . . . . . . . . . . . . . . . . . . . 35

3. Site-Specific Channel Modeling 36

3.1 Ray tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.2 Point cloud-based propagation prediction . . . . . . . . . . . 39

3.2.1 Point cloud-based prediction of diffusive propagation

channels in small rooms . . . . . . . . . . . . . . . . . 39

13

Contents

3.2.2 Shadowing detection . . . . . . . . . . . . . . . . . . . 41

3.2.3 Prediction of overall channel using point cloud data . 42

3.3 Contribution of the thesis . . . . . . . . . . . . . . . . . . . . 43

3.3.1 Validation of propagation prediction based on diffuse

scattering . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.3.2 Influence of point cloud density . . . . . . . . . . . . . 44

3.3.3 Shadowing detection . . . . . . . . . . . . . . . . . . . 45

3.3.4 Validation of overall channel prediction tool . . . . . . 46

4. Stochastic Channel Modeling 48

4.1 WINNER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2 COST 2100 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.3 IEEE 802.11ad . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.4 METIS channel model . . . . . . . . . . . . . . . . . . . . . . 51

4.5 Other mm-wave channel modeling works . . . . . . . . . . . 52


4.6.1 Spatio-temporal channel model for large indoor envi-

ronments . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.6.2 Parametrization of WINNER channel model in shop-

ping mall at 60 GHz . . . . . . . . . . . . . . . . . . . 53

4.6.3 Characterization of cross-polarization at 70 GHz . . . 53

4.6.4 Line-of-sight probability at millimeter-wave frequen-

cies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5. Millimeter-Wave Channel Sounding 57

5.1 Narrowband measurements . . . . . . . . . . . . . . . . . . . 58

5.2 Wideband channel measurements . . . . . . . . . . . . . . . 58

5.2.1 Measurements in the delay domain . . . . . . . . . . . 58

5.2.2 Measurements in the frequency domain . . . . . . . . 59

5.3 Directional channel measurements . . . . . . . . . . . . . . . 59

5.3.1 Rotation of directional antenna . . . . . . . . . . . . . 59

5.3.2 Antenna array measurements . . . . . . . . . . . . . . 60

5.4 Polarization measurements . . . . . . . . . . . . . . . . . . . 60

5.5 Millimeter-wave channel sounding campaigns for 5G sce-

narios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61


6. Applications for Channel Models 63

6.1 The use of stochastic channel models . . . . . . . . . . . . . . 63

14

Contents

6.1.1 Coding and modulation . . . . . . . . . . . . . . . . . . 63

6.1.2 Mobile terminal antenna design . . . . . . . . . . . . 63

6.1.3 Network design . . . . . . . . . . . . . . . . . . . . . . 64

6.1.4 Capacity and throughput evaluation . . . . . . . . . . 64

6.2 The use of site-specific channel models . . . . . . . . . . . . . 64

6.2.1 Coverage analysis . . . . . . . . . . . . . . . . . . . . . 64

6.2.2 Base station antenna design . . . . . . . . . . . . . . . 65

6.3 Contribution of thesis . . . . . . . . . . . . . . . . . . . . . . . 65

7. Summary of Publications 66

8. Conclusions 69

References 71

Errata 88

Publications 89

15

List of Publications

This thesis consists of an overview and of the following publications which

are referred to in the text by their Roman numerals.

I J. Järveläinen and K. Haneda, “Sixty Gigahertz Indoor Radio Wave

Propagation Prediction Method Based on Full Scattering Model,”

Radio Science, vol. 49, no. 4, pp. 293-305, April 2014.

II J. Järveläinen, M. Kurkela, and K. Haneda, “Impacts of Room Struc-

ture Models on the Accuracy of 60 GHz Indoor Radio Propagation

Prediction,” IEEE Antennas and Wireless Propagation Letters, vol.

14, pp. 1137-1140, January 2015.

III J. Järveläinen, K. Haneda, and A. Karttunen, “Indoor Propagation

Channel Simulations at 60 GHz Using Point Cloud Data,” IEEE

Transactions on Antennas and Propagation, Accepted for publica-

tion in a future issue, 2016.

IV A. Karttunen, J. Järveläinen, A. Khatun, and K. Haneda, “Radio

Propagation Measurements and WINNER II Parameterization for a

Shopping Mall at 60 GHz,” In Proceedings of the IEEE 81st Vehicular

Technology Conference (VTC Spring), Glasgow, UK, pp. 1-5, May

2015.

V K. Haneda, J. Järveläinen, A. Karttunen, M. Kyrö, and J. Putko-

nen, “A Statistical Spatio-Temporal Radio Channel Model for Large

Indoor Environments at 60 and 70 GHz,” IEEE Transactions on An-

tennas and Propagation, vol. 63, no. 6, pp. 2694-2704, June 2015.

VI A. Karttunen, K. Haneda, J. Järveläinen, and J. Putkonen, “Polari-

sation Characteristics of Propagation Paths in Indoor 70 GHz Chan-

16

List of Publications

nels,” In Proceedings of the IEEE 9th European Conference on An-

tennas and Propagation (EuCAP), Lisbon, Portugal, pp. 1-4, May

2015.

VII J. Järveläinen, S. L. H. Nguyen, K. Haneda, R. Naderpour, and U. T.

Virk, “Evaluation of Millimeter-Wave Line-of-Sight Probability With

Point Cloud Data,” IEEE Wireless Communications Letters, vol. 5,

no. 3, June 2016.

VIII S. L. H. Nguyen, K. Haneda, J. Järveläinen, A. Karttunen, and J.

Putkonen, “On the Mutual Orthogonality of Millimeter-Wave Mas-

sive MIMO Channels,” In Proceedings of the IEEE 81st Vehicular

Technology Conference (VTC Spring), Glasgow, UK, pp. 1-5, May

2015.

17

Author’s Contribution

Publication I: “Sixty Gigahertz Indoor Radio Wave PropagationPrediction Method Based on Full Scattering Model”

The author had the main responsibility for implementing and validating

the prediction tool, and preparing the manuscript. Prof. Haneda per-

formed the measurements and supervised the work.

Publication II: “Impacts of Room Structure Models on the Accuracyof 60 GHz Indoor Radio Propagation Prediction”

The author had a leading role in developing the idea and content for the

paper. Mr. Kurkela assisted in point cloud handling and in the paper

writing. Prof. Haneda supervised the work.

Publication III: “Indoor Propagation Channel Simulations at 60 GHzUsing Point Cloud Data”

The author had the main responsibility of the prediction tool implemen-

tation and validation, and preparation of the manuscript. The measure-

ments were planned and performed together with Dr. Aki Karttunen.

Prof. Haneda supervised the work.

Publication IV: “Radio Propagation Measurements and WINNER IIParameterization for a Shopping Mall at 60 GHz”

Dr. Karttunen had the leading role in preparing the content for the manu-

script. The channel measurements were performed by the author and Dr.

18

Author’s Contribution

Karttunen. Dr. Khatun assisted in data processing and Prof. Haneda

supervised the work.

Publication V: “A Statistical Spatio-Temporal Radio Channel Modelfor Large Indoor Environments at 60 and 70 GHz”

Prof. Haneda had the main responsibility of developing the the channel

model and writing the paper. The author had a leading role in performing

the channel measurements together with Dr. Kyrö and Dr. Karttunen.

The author, Dr. Kyrö, Dr. Karttunen and Mr. Putkonen assisted in writ-

ing the manuscript.

Publication VI: “Polarisation Characteristics of Propagation Paths inIndoor 70 GHz Channels”

Dr. Karttunen had the leading role in preparing the content for the paper

and writing the manuscript together with Prof. Haneda. The author was

the main responsible person in conducting the channel measurements and

assisted in the paper writing. Mr. Putkonen assisted in the paper writ-

ing.

Publication VII: “Evaluation of Millimeter-Wave Line-of-SightProbability With Point Cloud Data”

The author had the main responsibility in developing the content for

the manuscript. Dr. Nguyen contributed to contents of the manuscript.

Mr. Naderpour and Mr. Virk assisted in point cloud handling and Prof.

Haneda supervised the work.

Publication VIII: “On the Mutual Orthogonality of Millimeter-WaveMassive MIMO Channels”

Dr. Nguyen had the main responsibility for developing the content and

writing the manuscript together with Prof. Haneda. The author imple-

mented the channel prediction tool, simulated the channel data and as-

sisted in the paper writing. Dr. Karttunen and Mr. Putkonen assisted in

the paper writing.

19

List of Abbreviations

3GPP Third generation partnership project

4G Fourth generation

5G Fifth generation

5GCM 5G channel model

A/D Analog-to-digital

ASA Azimuth spread of arrival

ASD Azimuth spread of departure

BER Bit error rate

CDF Cumulative distribution function

CIR Channel impulse response

co-pol Co-polarized

COST European Cooperation in Science and Technology

CTF Channel transfer function

D2D Device-to-device

DS Delay spread

ER Effective roughness

FCC Federal communications commission

FDTD Finite-difference time-domain

GSCM Geometry-based stochastic channel model

IEEE Institute of Electrical and Electronics Engineers

IMT International Mobile Telecommunications

ITU-R International Telecommunication Union, Radiocommunication Sector

LOS Line-of-sight

LSP Large-scale parameter

LTE Long term evolution

METIS Mobile and wireless communications Enablers for the

Twenty-twenty Information Society

MIMO Multiple-input multiple-output

MiWEBA Millimetre-Wave Evolution for Backhaul and Access

20

List of Abbreviations

mmMAGIC Millimetre-wave based mobile radio access network

for fifth generation integrated communications

mm-wave Millimeter-wave

MoM Method of moments

MS Mobile station

MU Multiuser

NLOS Non-line-of-sight

OLOS Obstructed line-of-sight

PADP Power angular delay profile

PAS Power angular spectrum

PDP Power delay profile

PL Path loss

QoE Quality of experience

QuaDRiGa Quasi deterministic radio channel generator

RF Radio frequency

rms Root mean square

Rx Receiving

SCM Spatial channel model

SCME Spatial channel model extension

SF Shadow fading

SIR Signal-to-interference ratio

SINR Signal-to-interference-and-noise ratio

SR Specular reflector

SSP Small-scale parameter

Tx Transmitting

UIR Ultrasonic inspection room

UTD Uniform theory of diffraction

VNA Vector network analyzer

WINNER Wireless world initiative new radio

WLAN Wireless local area network

XPD Cross-polarization discrimination

x-pol Cross-polarized

XPR Cross-polarization ratio

21

List of Symbols

A Path loss exponent

Att Penetration loss

B Path loss intercept

D Diffraction coefficient

D1,...,4 Coefficients for calculating diffraction

d3D Three-dimensional link distance

dave Average distance to neighboring points

dS Area of surface element

E0 Electric field at transmitter

Ei Incident electric field vector

Es Scattered electric field vector

EUTD Amplitude of diffracted electric field

f Frequency

FαR Scaling coefficient

GRx,Tx Antenna gain at receiver, transmitter

H Channel transfer function

h Channel impulse response

k Wavenumber

L Number of paths

lp Path length

n Normal vector

Nn Number of neighboring points

p0,spec Specular reflection point

PDP Power delay profile

PDPmeas Measured power delay profile

PDPpred Predicted power delay profile

PL Path loss

R0,n Reflection coefficients

ri Distance from scatterer to transmitter

22

List of Symbols

ri Direction vector for incident electric field

Rpara Reflection coefficient for parallel polarization

Rperp Reflection coefficient for perpendicular polarization

rr Direction of specular reflection

Rrough Effective reflection coefficient

rs Distance from scatterer to receiver

rs Direction vector for scattered electric field

Rsmooth Fresnel reflection coefficient

S Scattering coefficient

s Distance from wedge to receiver

s′ Distance from transmitter to wedge

Sφ Azimuth spread

Sϑ Elevation spread

α Amplitude of multipath

αR Width of scattering lobe

δ(·) Dirac delta function

δmax Maximum plane depth

δplane Plane depth

εr Relative permittivity

θi Incident angle

θr Outgoing angle

ϑRx,Tx Elevation angle at receiver, transmitter

λ Wavelength

μ Mean value

μφ Center of gravity for azimuth angle

ξ Phase randomly distributed over [0 2π)

σ Standard deviation

σh Standard deviation of height distribution

τ Delay

τm Mean delay

τrms Root mean square delay spread

ϕ Phase of multipath

φRx,Tx Azimuth angle at receiver, transmitter

ψR Angle between reflection and scattering

ΩRx,Tx Angle vector at receiver, transmitter

23

1. Introduction

1.1 Background

Over the past decade, the world has witnessed a tremendous growth in

mobile traffic and the number of connected devices. For twenty years, the

mobile traffic was dominated by voice, but since the commercial success

of smart devices in 2007, the data traffic has surpassed voice traffic and

continues to rise exponentially, as seen from Figure 1.1 [1–3]. In 2015,

the global mobile traffic grew by 74%, largely driven by increased video

consumption that already accounts for over half of all mobile traffic [4].

The huge availability of video content accompanied by livestreaming will

further expand the share of video traffic, which is forecast to make up over

70% of the global mobile data traffic in 2021 [1,5].

The constantly growing mobile data volumes have resulted in a band-

width shortage, and the microwave frequency bands, which have been

used in the earlier generations of mobile communications, have become

very crowded [6, 7].1 Consequently, the spectrum of the upcoming fifth

generation (5G) wireless systems has been extended to cover frequency

bands in the millimeter-wave (mm-wave) range [8, 9]. Mm-waves have

been of particular interest due to the availability of large bandwidths in,

for instance, the 57–64 GHz, the 71–76 GHz and the 81–86 GHz bands,

allowing multigigabit data rates [10, 11]. Recently, the federal commu-

nications commission (FCC) proposed that also the 28 GHz and 39 GHz

bands as well as the 64-71 GHz band would be used for high-throughput

small cell deployment [12]. Yet, using mm-waves brings new challenges,

e.g., due to the high attenuation by building materials [13] and high power

consumption in analog-to-digital (A/D) converters [14]. A successful de-

1In this work, the term “microwave frequency” refers to a frequency below 6 GHz.

24

Introduction

Figure 1.1. Mobile voice and data traffic between 2008 and 2015 [3].

ployment of 5G is expected to introduce extremely low latencies, very high

peak data rates and improved quality of experience (QoE) [8,9].

To evaluate the performance of 5G networks, proper models for the ra-

dio channel are required [15]. A comparison between the mm-wave and

microwave propagation channels reveals a few distinct differences, such

as the significantly higher shadowing and diffraction losses at mm-wave

frequencies [16–19]. As a result, the mm-wave channel is more dominated

by specular paths and is presumed to use highly directive antennas and

beamforming [20–23]. These differences, and the fact that many new en-

vironments are considered for mm-wave deployment, necessitate the need

for new channel models at mm-wave frequencies [24,25]. The urge for new

channel models has been aided by research projects involving both indus-

try and academia, such as METIS [26], mmMAGIC [27], MiWEBA [28]

and 5GCM [29].

1.2 Objectives and contribution of the thesis

The objective of the thesis is to participate in the mm-wave channel mod-

eling activities by the following contributions:

1. Simulation tools for accurate site-specific channel modeling and line-

of-sight probability evaluation.

2. A simplified stochastic channel model structure.

3. Channel measurements for providing essential insight about mm-

25

Introduction

wave channels and parametrization of existing stochastic channel

models.

4. Examples of radio link performance evaluation with site-specific chan-

nel models.

First, the relevant properties of mm-wave wireless propagation chan-

nels are reviewed in Chapter 2, including propagation mechanisms and

characterization metrics of wireless channels. Based on material rough-

ness parameters it is concluded that surface materials are usually quite

smooth in indoor environments, but rougher in outdoor scenarios. The im-

portant channel characterization metrics such as path loss, delay spread

and angular spread are also defined.

Using the theory behind propagation mechanisms to predict wireless

propagation channels, which commonly goes under the term site-specific

channel modeling, is discussed in Chapter 3. Ray tracing, which is the

most widespread field prediction tool in wireless communications, is pre-

sented, and the prediction accuracy compared to mm-wave measurements

is studied based on results found in the literature. The results suggest

that the mean values in terms of path loss and delay spread can be well

predicted, but a good agreement in angular domain is more difficult to

achieve. Looking at the agreement between measurements and ray trac-

ing for individual links and their power delay profiles, results indicate

that it is necessary to include also diffuse scattering in the simulation

tool. Furthermore, it is found that models of the environment usually

lack important details. To solve the issue regarding insufficient database

accuracies, a propagation prediction tool relying on accurate environmen-

tal information in the form of point clouds, obtained with laser scanning,

is proposed. As the data format is different from what is used in ray trac-

ing, new methods to consider the propagation mechanisms are developed.

The validity of the simulation tool in both diffuse- and specular-dominated

indoor environments is demonstrated in [I-III].

Chapter 4 covers stochastic channel modeling, reviewing the WINNER,

COST 2100, IEEE 802.11ad and METIS models and their applicability

in the mm-wave bands. Based on channel sounding campaigns in large

indoor environments such as a shopping mall and a train station, a sim-

plified spatio-temporal channel model is proposed and parametrized at 60

and 70 GHz in [V]. Directional 60-GHz measurements in a shopping mall

are used to parametrize the WINNER II channel model, which is reported

in [IV]. In [VI], cross-polarization characteristics of indoor environments

26

Introduction

are studied. The results show that in the studied shopping mall the polar-

ization is preserved better at mm-waves than at microwave frequencies,

allowing more efficient polarization multiplexing. A method to evaluate

line-of-sight (LOS) probability using point clouds is presented in [VII],

where the LOS probability is derived in two environments which until

now have been lacking LOS probability models, an open square and a

shopping mall. A proposed generic exponential model is found to perform

excellently in the studied environments, and a study on frequency depen-

dency shows that the LOS probability at mm-waves differs from that at

lower frequencies due to the narrower Fresnel zone in the mm-wave re-

gion.

In Chapter 5, channel sounding techniques are reviewed with a focus on

mm-wave frequencies. It is noted that channel measurements are time-

consuming and that one sounder can usually not account for all the chan-

nel properties of interest. For instance, wideband, directional sounders

are generally unable to capture the time variance of dynamic channels.

Channel measurements used for validating the point cloud-based chan-

nel prediction tools and parametrizing stochastic channel models are por-

trayed.

The last topic of the thesis is the use of channel models for evaluation

of radio link performance, which is discussed in Chapter 6. The primary

use cases for site-specific and stochastic channel models are depicted in

order to provide the “big picture” of wireless communications and to jus-

tify the need for propagation channel modeling. The point cloud-based

propagation prediction tool is used to study the mutual orthogonality of

mm-wave multi-user channels using large antenna arrays in [VIII], where

it is found that the number of active users should be smaller at mm-waves

compared to microwave frequencies to guarantee effective spatial multi-

plexing.

Apart from publications [I-VIII], the author has authored or co-authored

several other publications related to mm-wave channel modeling [25, 26,

29–39].

27

2. The Wireless Propagation Channel

In order to model propagation channels for evaluating network perfor-

mance, channels must first be studied and characterized. Let us start

with the definition: A wireless propagation channel is the medium through

which a transmitted radio wave reaches the receiver. In urban and in-

door environments, the medium includes besides the air both natural and

man-made structures such as vegetation, building walls, pavement and

furniture. If the first Fresnel zone between the transmitter and receiver

is free of obstacles, the channel is denoted as a LOS channel, whereas

blockage leads to a non-line-of-sight (NLOS) condition. In LOS channels,

the direct path through the air is the shortest and strongest one, and its

amplitude can be calculated with the Friis’ law. As real environments

are not in vacuum, the radio wave will interact with objects in the envi-

ronment causing multipath propagation, that is, weaker, delayed copies

of the original signal, to arrive at the receiver. The mechanisms leading

to multipaths are generally divided into reflection, scattering and diffrac-

tion. Furthermore, all these paths can be attenuated due to shadowing

objects. Next, the propagation mechanisms at mm-wave frequencies are

discussed.

2.1 Propagation mechanisms at mm-waves

2.1.1 Reflection and transmission

A specular reflection occurs when an electromagnetic field interacts with

an electrically large, smooth and distant surface. Snell’s law, stating that

the incident and outgoing angles are identical, can be applied and the re-

flection loss for perpendicular and parallel polarization can be calculated

28

The Wireless Propagation Channel

Table 2.1. Relative permittivities and penetration losses of materials around 60 GHz.

Material |εr| Att [dB/cm] Ref.

Brick 2.6–4.4 1.5–14.7 [17,40–42]Concrete 3.1–6.5 3.7–17 [17,41–43]Plasterboard 2.3–2.8 0.09–2.4 [13,41–43]Glass 6.2–8.9 2.8–18.8 [41–45]Plywood/wood 1.6–2.8 0.8–12 [41–43,46–48]Vinyl floor 6.5–7.8 5.5–6.9 [46]

from the Fresnel reflection coefficients

Rperp =cos θi −

√εr − sin2 θi

cos θi +√εr − sin2 θi

,

Rpara =−εr cos θi +

√εr − sin2 θi

εr cos θi +√εr − sin2 θi

, (2.1)

where θi is the angle of incidence and εr is the complex relative per-

mittivity describing the dielectric properties of the material. A specular

reflection is illustrated by Figure 2.1. Due to the importance of permittiv-

ity in determining the strength of a reflected wave, many measurement

campaigns have been dedicated to dielectric properties of typical urban

and indoor materials. Typical values for the absolute value of εr in the

60-GHz band are listed in Table 2.1.

At mm-wave the penetration losses are usually very large and therefore

accounting for penetration can be simplified to only inducing extra atten-

uation similarly to the Motley–Keenan model [15, 49]. Typical building

material losses at 60 GHz are shown in Table 2.1, where Att is the pen-

etration loss in dB/cm. It can be seen that Att varies wildly even for a

single material because the material samples used in measurements are

not identical and their composition depends on, e.g., the moisture and

possible metallic reinforcements. Also the permittivity is seen to vary de-

pending on the specific material sample.

θi θ

i

εr

n

Figure 2.1. Illustration of specular reflection. The vector n denotes the surface normal.

29


σh [mm]

0 0.2 0.4 0.6 0.8 1

Rro

ugh/R

smoo

th

0

0.2

0.4

0.6

0.8

1

θi = 75°

θi = 45°

θi = 15°

Figure 2.2. Effective reflection coefficient.

2.1.2 Scattering

When an electromagnetic wave impinges on a surface which is rough or

small compared to the wavelength, the equations (2.1) are no longer valid

and the propagation phenomenon is referred to as scattering. Many stud-

ies have been made to study the effect of rough surfaces, such as introduc-

ing an effective reflection coefficient which models the power scattered to

the specular direction [15]

Rrough = Rsmooth exp[2(kσh cos θi)2], (2.2)

where Rsmooth is the Fresnel reflection coefficient calculated with (2.1),

k = 2π/λ is the wavenumber for the wavelength λ and σh is the standard

deviation of the height distribution. To illustrate the effect of the surface

roughness at 60 GHz, the ratio Rrough/Rsmooth, which ideally should be

close to 1, is plotted as a function of σh for different incident angles in

Figure 2.2. Other well-known surface roughness criteria are the Rayleigh

criterion, requiring σh < λ/(8 cos θi) for a smooth surface, or the stricter

Fraunhofer criterion which presumes σh < λ/(32 cos θi) for a smooth sur-

face [50,51]. For θi = {15◦, 45◦, 75◦}, the Rayleigh criterion gives σh values

ranging from 0.65 to 2.4 mm at 60 GHz, while the Fraunhofer criterion

gives values between 0.16 and 0.6 mm. As a reference, Table 2.2 shows

the surface roughness, i.e., the standard deviation in height σh, for com-

mon indoor and urban materials. Most indoor materials have σh < 0.2 and

can thus be considered smooth, implying that surface roughness does not

require particular consideration. On the other hand, it can be observed

30


Table 2.2. Surface roughness for indoor and urban materials.

Material σh [mm] Ref.

Acrylic 0.001 [52]Glass 0.01 [40,53]Vinyl floor 0.028 [52]Plaster 0.05–0.15 [54]Wooden panel < 0.1 [40]Wallpaper 0.13 [54]Hardwood floor 0.14 [52]Smooth concrete 0.2 [55]Brick 0.3–2 [40]Rough asphalt 0.9 [55]

that outdoor materials such as brick and asphalt are clearly rougher.

A more holistic method for dealing with scattering mechanisms, includ-

ing both real surface roughness and the effect of small objects, is the

so-called effective roughness (ER) approach [56]. The ER assumes that

all plane surfaces contribute to diffuse scattering because the building

databases do not include details about small irregularities. Two models

have been proposed to model the scattering, a non-directional model with

a Lambertian scattering pattern and a single-lobe directive model, which

focuses the scattered signal in the direction of the specular reflection.

Among these, the single-lobe directive model has been found to perform

better than the Lambertian model for many typical wall materials [57].

In the directive single-lobe model, presented in Figure 2.3, the scattered

field is written as

|Es|2 =(SEi

rirs· λ

4π

)2

· dS cos θiFαR

·(1 + cosψR

2

)αR

, (2.3)

where Ei is the incident field, S = |Es|/|Ei| is the scattering coefficient in

the range [0, 1], ri and rs are the distances from the scatterer to the trans-

mitter and receiver, respectively, dS is the area of the surface element, ψR

is the angle between the direction of the reflected wave and the scattering

direction, αR determines the width of the scattering lobe so that a higher

value gives more directive scattering, and FαR is a scaling coefficient de-

fined as

FαR =1

2αR·αR∑j=0

(αR

j

)· Ij , (2.4)

where

Ij =2π

j + 1·[cos θi ·

j−12∑

ω=0

(2ω

ω

)· sin

2ω θi22ω

]( 1−(−1)j

2)

. (2.5)

31


ri

rs

θi

θr

rr

ψR

dS

Figure 2.3. Single-lobe directive scattering model where ri and rs are the directions ofthe incident and scattering waves and rr is the direction of the specular re-flection with the outgoing angle θr = θi [57].

2.1.3 Diffraction

At microwave frequencies, diffraction has been known as a major contrib-

utor especially in urban NLOS scenarios [58, 59]. At mm-waves, diffrac-

tion is negligible in most cases [60] with the exception of weak NLOS

links and when the receiver is in the transition region close to the shadow

boundary [17,61].

When considering diffraction in deterministic field prediction, Huygens’

principle is commonly used [62]. In short, it states that an object being

radiated by a field can be seen as a secondary source which re-radiates

a field to the receiver. In wireless communications especially the wedge

diffraction is of interest because building corners, both indoors and out-

doors, are the main objects causing diffraction [15]. The amplitude of a

diffracted path can be calculated with the uniform theory of diffraction

(UTD) for an imperfectly conducting right-angle wedge [63]:

D = D1 +R0RnD2 +R0D3 +RnD4, (2.6)

where R0 and Rn are the reflection coefficients for the illuminated and

shadow faces, respectively, and Dg(g = 1, 2, 3, 4) depend on the frequency,

distances and angles [64]. The diffracted field is calculated from the

diffraction coefficient as

EUTD =E0 exp(−jk(s′ + s))

s′ + sD

√s′ + s

s′s, (2.7)

where E0 is the field at the transmitting (Tx) antenna and s′ and s are the

32


τ

| h(τ

)|

Figure 2.4. Example of a channel impulse response.

distances from the edge to the transmitting and reveiving (Rx) antennas,

respectively.

2.2 Characterization of wireless channels

2.2.1 The double-directional channel

The environment and propagation mechanisms described above give rise

to multipath components, which cause the received signal to be spread

out in both delay and angle. Looking only at the delay τ , the received

signal can be depicted by a channel impulse response (CIR) h(τ) shown

in Figure 2.4. Taking into account the angle for each multipath at both

the Tx and Rx sides, as well as the delay, we end up with the so-called

double-directional impulse response consisting of L multipaths [65]

h(τ,ΩRx,ΩTx) =L∑l=1

αl exp (jϕl)δ(τ − τl)δ(ΩRx −ΩRx

l )δ(ΩTx −ΩTxl ), (2.8)

where ΩTx = [φTx ϑTx] and ΩRx = [φRx ϑRx] are vectors composed of az-

imuth and elevation angles on the Tx and Rx sides, respectively, αl, ϕl

and τl are the amplitude, phase and delay of the lth multipath compo-

nent, and δ(·) stands for the Dirac delta function.

Furthermore, due to the movement of the environment, the transmitter

or the receiver, the channel can also be time-variant. This may cause indi-

vidual Doppler shifts in the different multipath components, which causes

the original signal to be spread out also in frequency. The distribution of

frequency shifts for the multipaths can be modeled by the Doppler spec-

trum. The effect of the Doppler spread can be significant in narrowband

systems, but is usually small in wideband systems.

33


2.2.2 Channel characterization metrics

A single CIR experiences small scale fading due to constructive and de-

structive interference of multipaths, which can be seen as a variation in

signal amplitude or phase over a short time. To filter out the small scale

fading, a number of CIRs taken over a small area can be averaged to form

a power delay profile (PDP):

PDP (τ) =1

M

M∑m=1

|h(τ)|2, (2.9)

where M is the number of CIRs.

To simplify the characterization of the channel power and delay disper-

sion, a set of parameters can be extracted from the PDP [15]. Path loss

(PL) describes how much the power is attenuated in the channel and can

be calculated simply as the integral over the PDP for those delay bins

lying over the noise floor. The first delay parameter is the mean delay,

which is derived as

τm =

∫∞−∞ PDP (τ)τdτ∫∞−∞ PDP (τ)dτ

. (2.10)

The second, and more important parameter describing the delay disper-

sion, is the root mean square (rms) delay spread:

τrms =

√∫∞−∞ PDP (τ)τ2dτ∫∞−∞ PDP (τ)dτ

− τm. (2.11)

The importance of the delay spread is due to its proportionality with bit

error rate (BER) and inverse correlation with the coherence bandwidth

[15].

Analogous to the delay dispersion, also the angular dispersion can be ex-

pressed with the angular spread from the power angular spectrum (PAS),

which characterizes how the signal power varies over an angle. The az-

imuth spread is computed with [15,66]

Sφ =

√∫ | exp(jφ)− μφ|2PAS(φ)dφ∫PAS(φ)dφ

, (2.12)

where φ is the azimuth angle and

μφ =

∫exp(jφ)PAS(φ)dφ∫

PAS(φ)dφ. (2.13)

The elevation spread Sϑ can be determined similarly from the elevation

34


angle ϑ. Also other definitions of the angular spreads exist [67]. The de-

lay and angular dispersion parameters are commonly used for describing

channels, and hence they can also be used for comparing the agreement

between measured and predicted channels.

2.2.3 Depolarization

Due to edges and rough surfaces, the polarization of a transmitted wave

is not perfectly preserved, but a part of the power is coupled to the or-

thogonal polarization [62]. This phenomenon, called depolarization, can

be characterized by the cross-polarization ratio (XPR), which is the ratio

between the co-polarized and cross-polarized powers.

35

3. Site-Specific Channel Modeling

For modeling a radio channel, channel measurements always provide the

most accurate option, or put in another way, the “truth”. However, mea-

surements, as will be discussed in Chapter 5, can sometimes be very ex-

hausting, especially when a large amount of directional data is required

for statistical purposes, e.g., [68]. The long measurement durations can

cause limitations as all measurement sites are not accessible for long pe-

riods of time. Furthermore, the placement of antennas may be infeasible

due to non-stationary objects such as vehicles and people. It must also be

kept in mind that the antenna radiation pattern affect the measurements.

In contrast to measurements, using simulation tools to estimate the

channel does not suffer from all these limitations. Simulation, often de-

noted as deterministic field prediction, relies on electromagnetic theory to

determine the electromagnetic fields in a given environment as detailed

in Chapter 2. To obtain accurate channel estimation results, the detailed

geometry and material properties of the entire environment should be

known [15]. The highest precision is given by so-called full-wave ap-

proaches such as the method of moments (MoM) or the finite-difference

time-domain (FDTD) method, which solve the Maxwell’s equations for a

discretized environmental model given that the most accurate represen-

tation of the environment is available.. As the resolution of the geome-

try should be less than one wavelength [69, 70], full-wave simulations of

large environments are computationally prohibitive, particularly at mm-

wave frequencies. At microwave frequencies, simple indoor geometries

have been characterized using both MoM and FDTD [69,71–73]. Another

full-wave method for repetitive structures has been reported in [74].

The growing need for site-specific network planning in the early nineties

necessitated field prediction tools with reduced computational complex-

ity [75, 76]. This initiated the development of ray-based methods, relying

36

Site-Specific Channel Modeling

on high frequency approximations of Maxwell’s equations. The approx-

imations are based on the geometric optics assumption that the wave-

length of the propagating radio wave is much smaller than the objects

in the surrounding environment and that radio waves can be treated as

infinitely narrow rays, which reflect and penetrate according to Snell’s

law. Among ray-based methods, ray tracing is the most established one

and is used in many commercially available software such as the Wireless

InSite [77], WaveSight [78], Winprop [79] and Volcano [80]. Moreover,

many universities and research institutes have developed their own ray

tracing engines [81–84]. Some works use a hybrid method combining ray

tracing and FDTD [85,86]. Another prediction method relying on the ray

assumption was introduced in [30], where the 3D model of the environ-

ment is obtained through laser scanning in the form of point clouds. Point

cloud-based methods eliminate the need for manually building detailed

environmental models, but require different computation techniques to

model the propagation mechanisms as will be discussed in Section 3.2.

3.1 Ray tracing

Ray tracing rests on the so-called image principle, where the image of the

transmitter is determined with respect to each planar surface (first order

reflection) and combination of surfaces (second and higher order reflec-

tions) [87]. The environment is represented by surfaces, which for urban

locations might be available from online databases such as Google Earth

[88], but can also be built using computer-aided design software [89]. All

possible rays are then formed by tracing the ray from the transmitter im-

ages to the receiver, along with diffracted rays (discussed in Section 2.1.3).

The power of the paths are computed from free-space path loss, reflection

loss (Eq. 2.1) and penetration loss. For improved prediction accuracy,

also scattering has to be included. For this purpose, scattering models

presented in Section 2.1.2 are good candidates.

To validate ray tracing tools, measured channels are commonly used as

a reference. The simplest metric for validation is, as presented in Section

2.2.2, the path loss. Delay and angular spreads are also popular compar-

ison metrics. Table 3.1 presents the prediction accuracy obtained from

literature in terms of power, delay and angular metrics. The comparison

shows that path loss can be accurately predicted in both LOS and NLOS

scenarios. The predicted delay spread is also in good agreement with

37


Table 3.1. Prediction accuracy for path loss, delay spread and angular spread.

Environment Freq. Meas. Pred. Error Ref.P

ath

loss

Indoor (NLOS) 28 GHz 4.5 dB [90]Indoor (LOS) 58 GHz 67.1 dB 67.2 dB [91]Indoor (LOS) 58 GHz 1.2 dB [92]Indoor (NLOS) 58 GHz 0.5 dB [92]Indoor (LOS) 58 GHz 48 dB 46 dB [93]

Del

aysp

read

Office (LOS) 61 GHz 6.3 ns 6.6 ns 1.2 ns [82]Office (LOS) 61 GHz 6.3 ns 6.3 ns 1.1 ns [94]Urban (NLOS) 28 GHz 58.6 ns 50.7 ns [95]Indoor (LOS) 58 GHz 4.1 ns 4.1 ns [91]Indoor (LOS) 58 GHz 1.4 ns [92]Indoor (NLOS) 58 GHz 2.6 ns [92]Indoor (LOS) 60 GHz 13.3 ns 14.3 ns [93]Office (LOS) 60 GHz 10.8 ns 18.6 ns [96]Office (LOS) 60 GHz 11.8 ns 14.1 ns [96]Urban 60 GHz 63.5 ns 63.3 ns [97]Office (LOS) 73 GHz 9 ns 8 ns [89]Office (NLOS) 73 GHz 13 ns 12 ns [89]Urban 73 GHz 6 ns 5 ns [68]Office (LOS) 60 GHz 12.3 ns 13.1 ns [98]

Azi

mut

hsp

read

Urban (NLOS) 28 GHz 8.5° 4.1° [95]Urban (NLOS) 28 GHz 33.5° 17.3° [95]Urban (NLOS) 28 GHz 52.9° 47.9° [95]Indoor (LOS) 58 GHz 41.5° 55.1° [91]Indoor (LOS) 58 GHz 18.4° 16.5° [91]Urban 73 GHz 35° 27° [68]

Ele

vati

onsp

read

Urban (NLOS) 28 GHz 4.7° 1.1° [95]Urban (NLOS) 28 GHz 8.8° 7.2° [95]Indoor (LOS) 58 GHz 7.3° 3.3° [91]Urban 73 GHz 3° 6° [68]

measurements when looking at mean values. The azimuth and elevation

spreads clearly suffer from large errors, and in NLOS links the predicted

spreads are heavily underestimated. As many works present the compar-

ison only in terms of mean values, the reasons for the discrepancy is not

always evident. However, papers which present the agreement in the form

of a PDP or a PAS, such as [82,91,97,99,100], highlight the fact that the

3D models are usually quite simple and therefore ray tracing is not able to

predict even the specular peaks correctly. Ray tracing also fails in predict-

ing a large number of weaker paths that might originate from electrically

small objects or rough surfaces which are not included in the database,

that is, scattering. For example in [101], lamps and bookshelves were

found to cause strong scattering. The effect of furniture is investigated in,

e.g., [102], which shows that the difference between PASs in an empty and

38


a furnished room can be significant. The importance of considering the de-

tails of the environment is discussed in, e.g., [97, 101, 103–107]. Further

ray tracing results, whose agreement with measurements have not been

quantified but presented visually, can be found in [89,99–101,107–113].

3.2 Point cloud-based propagation prediction

Due to the fact that simplified geometrical descriptions lead to the afore-

mentioned inaccuracies, a field prediction method relying on accurate en-

vironmental data in the form of point clouds has been developed [I]. The

point clouds are obtained with laser scanners, which use moveable mir-

rors to steer a laser beam in different directions. An object will cause the

beam to reflect back to the scanner, where the distance to the object is de-

termined, for instance, by comparing the phase of the transmitted and re-

ceived beams [114]. For each reflecting object, the x-, y- and z-coordinates

are determined from the distance and the direction of the laser beam. The

position accuracy of laser scanners is in the order of 5 mm [115]. To cover

an environment without shadowed areas, scans are made in multiple lo-

cations and the data is combined to form one complete point cloud. For

example, the small office in [I] and the open square in [VIII] were scanned

with two and 20 locations, respectively, where each scan lasted less than 4

minutes. To decrease the computational load of the simulation, the point

cloud density can be reduced with different sampling methods [116].

Ray-tracing software require a surface representation of the environ-

ment, but finding a surface description from the point cloud, commonly

known as meshing, is not a straight-forward task [116]. The single-lobe

scattering model presented in (2.3) is on the other hand not restricted by

the format of the environment model and can be used directly with the

point cloud. Using a scattering model can also be justified based on the

importance of scattering in improving the accuracy of deterministic field

prediction, as was discussed in Section 2.1.2.

3.2.1 Point cloud-based prediction of diffusive propagationchannels in small rooms

Assuming scattering to be the dominant propagation mechanism, the to-

tal field can be estimated as the sum of scattering from all points in the

point cloud, where scattering is calculated with the single-lobe directive

model (2.3). To determine the angles θi and ψR, the normal vector for the

39


Figure 3.1. Point cloud of a small office room [I].

local surface dS of each point is required (see Figure 2.3). A simple ap-

proach to find the angles is to find the normal from the three neighboring

points [30], or for a more stable solution the normal can be acquired from

fitting a plane to the Nn neighboring points (In [III], Nn = 8). The area

dS of each point is similarly calculated from the average distance to the

4 neighboring points dave as dS = d2ave. A scattered path is defined as the

propagation path from the Tx antenna, scattered from a point in the point

cloud and received by the Rx antenna. Each path can be described as

P ={τl, αl,Ω

Txl ,ΩRx

l

}L

l=1, (3.1)

where the delay τ is determined by the propagation distance of each path,

α is the amplitude computed with (2.3), ΩTx = [φTx ϑTx] and ΩRx =

[φRx ϑRx] are vectors composed of azimuth and elevation angles on the

Tx and Rx sides, respectively and L is the number of paths. Using the

discrete Fourier transform we can calculate the channel transfer function

(CTF) as a sum of the paths with

Hi =L∑l=1

GRx(ΩRx)αlGTx(ΩTx) exp [−j(2πτlfi − ξ)], (3.2)

where GTx and GRx are the gains on the Tx and Rx sides, respectively, f is

the frequency with I frequency steps, 1 ≤ i ≤ I and ξ is a phase uniformly

distributed over [0 2π). Using the inverse discrete Fourier transform, the

transfer function H can be converted back to a CIR:

hn =1

I

I∑i=1

Hi ej2πfiτn , (3.3)

40


where n corresponds to the nth delay bin, 1 ≤ n ≤ I. To account for the

effects of small-scale fading in deriving the PDP, the phase of the scattered

paths ξ in (3.2) can be varied randomly. The PDP can be calculated as an

ensemble average of M small-scale realizations of the CIRs with (2.9).

As it is difficult to know the material parameters for a propagation envi-

ronment exactly [41, 42] as highlighted by Table 2.1, all the points in the

point cloud are assumed to have the same scattering model parameters αR

and S in (2.3). Using channel measurements, the scattering model param-

eters are then tuned so that the measured and predicted PDPs agree as

well as possible. It should be noted that because the diffuse scattering is

observed both due to surface roughness and diffraction from small objects,

it is difficult to treat objects separately. Recent results also support the

assumption that different objects and materials can share similar scatter-

ing model parameters when they have similar surface roughness [117]. To

find the optimum scattering model parameter values for a single link, the

parameter values are tuned by a brute force search to minimize the cost

function

ε =1

J

J∑j=1

∣∣∣PDPmeas(j)− PDPpred(j)∣∣∣, (3.4)

where PDPmeas and PDPpred are the measured and predicted PDPs, re-

spectively, and 1 ≤ j ≤ J are the delay indices for which the corre-

sponding power levels are within a 30 dB dynamic range seen from the

maximum signal amplitude of the PDP.

3.2.2 Shadowing detection

At mm-waves, shadowing due to walls, furniture and people [118, 119]

causes severe signal attenuation as shown in, e.g., Table 2.1. Therefore

the detection of shadowing is a necessary feature in any deterministic

field prediction tool. As a surface representation is not available in point

cloud environments, shadowing is detected by searching for points within

the first Fresnel zone between the Tx and Rx antennas, as proposed in

[33]. In other words, a path is free of blockage if

lp ≤ d3D +λ

2, (3.5)

where lp is the length of the path from the Tx antenna via a point in the

point cloud to the Rx antenna and d3D is the distance from the Tx to the Rx

antenna [VII]. Based on the material of the shadowing object, penetration

41


Tx n Rx

ndavg

Figure 3.2. Point cloud densification for improved shadowing detection. Blue points de-note the original point cloud, red squares denote dense points and the grayellipse marks the Fresnel zone [III].

loss is added to the amplitude of shadowed paths.

The point cloud density is a crucial factor determining a successful de-

tection of shadowing objects. In [33], it is found that with low point den-

sities, the second Fresnel zone should be used instead of the first order

zone. To assure that shadowing is detected also close to the link ends, an

improved shadowing detection method was proposed in [III]. The point

cloud is densified, that is, new points are interpolated around objects close

to the Fresnel zone ends as shown in Figure 3.2.

3.2.3 Prediction of overall channel using point cloud data

In small environments where there are a lot of small objects, scattering

might be a dominant propagation mechanism, as seen in [I], [II]. How-

ever, in larger spaces with smooth walls, also specular reflections have

to be modeled. Moreover, even physically small objects such as computer

screens might act as specular reflectors at mm-waves [120]. A method for

simulating specular reflections is presented in [121], where points lying in

the first Fresnel zone between the image source and the receiver are iden-

tified as specular reflection points. The method sums up the contributions

from points within the first Fresnel zone, and thus requires a high point

density to accurately predict the amplitude of a reflected path. This be-

comes especially challenging in mm-wave bands where the Fresnel zones

have small dimensions, since a dense point cloud increases the compu-

tational burden significantly. To calculate specular reflections with point

clouds having lower density, an approach similar to [121] is developed

in [III]. In this new method, it is assumed that the surface is larger than

the first Fresnel zone and that the amplitude thus can be calculated di-

rectly with the Fresnel equations shown in (2.1). To assure that specular

reflections are calculated from flat surfaces, the plane depth δplane of the

local surface around the reflection point is checked. For a particular point,

this is accomplished by computing δplane as the maximum variation in the

direction of the normal among the eight neighboring points (see Figure

42


n

δplane

Figure 3.3. Determination of plane depth δplane. Blue points denote neigboring points.

3.3), and checking whether this value is smaller than a predefined maxi-

mum plane depth δmax. The value δmax is dependent on the environment

and the point cloud density, and is usually determined heuristically. All

points which are found within the nth Fresnel zone and which satisfy the

smoothness criterion δplane < δmax are said to be specular reflection points

p0,spec. However, in practical implementations there are cases where mul-

tiple nearby specular reflection points are found within the same Fresnel

zone. These points must be grouped such that a specular reflection from

the same object is not calculated multiple times, thus overestimating the

amplitude of the reflected path. In particular, specular reflection points

lying inside the same Fresnel zone are grouped, and in each group the

point closest to the Fresnel zone center forms a specular reflector (SR).

Every point p0,spec then either belongs to an SR or forms an SR of its own.

Finally, the specular paths are calculated from the SRs only.

In severe NLOS scenarios also diffraction around corners is taken into

account. The relevant wedges where diffraction occurs are modeled by

rows of points, and the diffracted field is calculated with the UTD diffrac-

tion formulas presented in Section 2.1.3.

3.3 Contribution of the thesis

3.3.1 Validation of propagation prediction based on diffusescattering

The validity of the diffusive propagation prediction method has been in-

vestigated in [I] in two indoor environments:, an ultrasonic inspection

room (UIR) with approximate dimensions of 7.2×6.1×2.6 m3, and a small

office with dimensions of 4.5×4.3×2.9 m3. The results show that the scat-

tering lobe width αR has a much smaller effect on the predicted channel

compared to the scattering coefficient S, and that a single value for S can

be used within one scenario. In the ultrasonic inspection room and the

office, the best values for S are found to be 0.9 and 0.5, respectively. Ex-

emplary PDPs and PASs from the small office are presented in Figures

3.4 and 3.5, which show that the predicted and measured channels agree

43


Table 3.2. Prediction accuracy for the point cloud-based diffuse prediction method [I].

Env. Metric Meas. Pred. Error

UIR (LOS) τm 6.8 ns 7.3 ns 0.6 nsSmall office (LOS) τm 5.3 ns 5.3 ns 0.1 nsUIR (LOS) τrms 1.9 ns 1.9 ns 0.2 nsSmall office (LOS) τrms 3.1 ns 2.6 ns 0.5 nsSmall office (LOS) Sφ 23.0° 22.7° 2.6°Small office (LOS) Sϑ 16.7° 16.4° 0.6°

excellently both in delay and azimuth domains. The prediction accuracy

is also presented in Table 3.2, from which it can be observed that errors

in both delay and angular spreads are smaller compared to the results in

Table 3.1. It should be noted that due to the small size of the rooms and

the absence of objects leading to high shadowing losses, the prediction is

carried out with only first-order scattering and by neglecting the effects

of shadowing.

It is difficult to assess the general applicability of the optimized scat-

tering model parameters as the availability of those is still very scarce.

Moreover, the optimum values αR and S do not depend on the material

alone, but also on the amount of fixtures and furniture in the environ-

ment.

3.3.2 Influence of point cloud density

As the computational burden is proportional to the number of points in a

point cloud, the effect of point cloud density has been studied at 60 GHz

in [II]. The study is performed in the same small office described earlier

with point cloud sizes ranging from 1000 points to 676 000 points, cor-

responding to point separations of roughly 30 and 1 cm, or 60λ and 2λ,

Delay [ns]0 10 20 30 40 50

Am

plitu

de [

dB]

-110

-100

-90

-80

-70

-60 MeasuredPredicted

Figure 3.4. Measured and predicted PDPs in small office [I].

44


Azimuth angle at Tx [°]0 60 120 180 240 300 360

Azi

mut

h an

gle

at R

x [ °]

45

90

135

[dB]

-110

-100

-90

-80

-70

(a)

Azimuth angle at Tx [°]0 60 120 180 240 300 360

Azi

mut

h an

gle

at R

x [ °]

45

90

135

[dB]

-110

-100

-90

-80

-70

(b)

Figure 3.5. a) Measured and b) predicted power angular spectrum for small office [I].

respectively. A comparison of optimum scattering model parameters and

prediction accuracy for delay and angular spreads reveals virtually no dif-

ferences between different densities, which can be explained by the fact

that the strongest paths can be well predicted with all densities. Yet, the

PDPs for the low point cloud densities can be seen to fluctuate more and

have a higher number of peaks, as seen from the PDPs in Figure 3.6. This

results from the increased relative power of a single scatterer and the un-

certainty in determining the surface normals due to the small number of

points. A further decrease in the number of points is believed to high-

light this phenomenon even more. The predicted PDPs also emphasize

the effect of omitting double-order scattering, as the prediction fails in

representing the tail of the measured PDP, i.e., delays larger than 30 ns.

However, this tail is more than 40 dB weaker compared to the maximum

amplitude and will thus have a negligible effect on the channel.

3.3.3 Shadowing detection

The method to detect shadowing, which was proposed in [33], is used

in [III] to validate the channel prediction method in NLOS conditions.

Also an improved shadowing detection method which is described in Sec-

tion 3.2.2 is presented to account for shadowing objects close to the link

ends. In [VII], the shadowing detection method is applied for deriving

45


Delay [ns]0 10 20 30 40 50

Am

plitu

de [

dB]

-130

-120

-110

-100

-90

-80

-70 Measured1k5k20k49k676k

Figure 3.6. Measured and predicted PDPs with different point cloud densities, where “k”in the legend denotes 1000 [II].

LOS probability models, which is discussed in Section 4.6.4.

3.3.4 Validation of overall channel prediction tool

In [III], the overall prediction tool is studied in a cafeteria with approx-

imate dimensions of 14×13.5×2.8 m3, which contains smooth walls and

windows as well as scattering objects including tables, chairs, computer

screens and lamps. A solid wall is separating the main cafeteria from an

adjacent smaller space, allowing measurements with the direct path com-

pletely blocked. Channel sounding in the band 61–65 GHz is performed

in 3 LOS and 3 NLOS locations, including a 0–360° azimuth sweep on the

Tx side to study the directional characteristics of the channel. Similar

to the scattering model parameters, the relative permittivity is assumed

to be equal for all materials and optimized based on a brute force search

minimizing the delay spread error compared to measured channels. The

optimization yields relative permittivity values between 3.5 and 6 and S

of around 0.8 in LOS links and 0.6 in NLOS links, while the scattering

lobe width αR is fixed to 1 because it is found to be of small influence com-

pared to S. A comparison between measured and predicted PDPs, PASs

and channel metrics are presented in Figures 3.7 and 3.8 and Table 3.3.

The result shows that the prediction accuracy in LOS channels is excel-

lent in terms of path loss, mean delay and delay spread, and very good for

angular spread. The agreement between measured and predicted links

is very good also for NLOS links. Furthermore, it is shown that spec-

ular reflections can be modeled by a single relative permittivity for all

surfaces as a result of common indoor materials having similar reflection

coefficients. The only exception is metal, which causes underestimation

46


Delay [ns]0 50 100 150 200

Am

plitu

de [

dB]

-140

-120

-100


(a)

Delay [ns]0 50 100 150 200

Am

plitu

de [

dB]

-140

-120

-100


(b)

Figure 3.7. Measured and predicted PDPs for a) LOS location Tx1, b) NLOS location Tx4in [III].

Azimuth angle [°]0 90 180 270 360

Am

plitu

de [

dB]

-140

-120

-100


(a)

Azimuth angle [°]0 90 180 270 360

Am

plitu

de [

dB]

-140

-120

-100


(b)

Figure 3.8. Measured and predicted PASs for a) LOS location Tx1, b) NLOS location Tx4in [III].

of the path amplitude and should be modeled with a separate permittiv-

ity value. In general, the more different materials are considered, i.e., the

higher number of relative permittivities that are optimized, the better the

agreement between measurements and prediction should be.

Table 3.3. Comparison of measured (m.) and predicted (p.) large scale parameters for theoverall channel prediction [III].

Link Tx PL [dB] τm [ns] τrms [ns] Sφ [°]type m. p. m. p. m. p. m. p.

LO

S 1 78.0 78.4 11.9 12.1 5.1 5.6 16.8 19.52 77.4 78.7 11.1 11.1 6.9 6.9 14.0 19.53 79.1 80.4 14.3 14.1 7.7 7.3 20.2 22.0

NL

OS 4 98.8 97.3 50.3 48.2 18.4 18.1 44.5 41.7

5 98.9 96.6 52.2 46.6 14.4 10.3 32.3 27.06 97.1 98.5 53.6 59.1 11.3 22.7 38.7 44.0

47

4. Stochastic Channel Modeling

In contrast to site-specific models, stochastic channel models aim at repro-

ducing the statistical properties of a propagation channel in terms of, for

instance, received power, delay or angular dispersion. They can be used

for transmission technique design or performance comparison. Stochas-

tic models consider balance between accuracy and simplicity, and their

requirements depend on the system for which the models are built. For

instance, wireless systems in hospitals require a much higher reliability

compared to conventional indoor networks [122]. The importance of accu-

rate models can also be seen in network planning, where overestimating

the path loss leads to increased costs due to, e.g., redundancy in base

station deployment, but underestimation leads to unsatisfactory coverage

and thus decreased QoE.

Stochastic channel models are usually formulated as a set of mathemat-

ical equations, including parameters describing the characteristics of the

environment and the deployment such as the antenna height, the street

width and the path loss. The parametrization of channel models is done

either with channel measurements [123,124] or by utilizing deterministic

field prediction such as ray tracing [125–128]. The most well-known chan-

nel models are path loss models such as the Okumura–Hata model [129]

and the COST 231–Walfisch-Ikegami model [130]. As the wireless sys-

tems have become more and more complex, the channel models have de-

veloped to include more features of the channel, e.g., directional proper-

ties. Next, the most common modern channel models and their applica-

bility at mm-waves is reviewed.

48

Stochastic Channel Modeling

Table 4.1. WINNER II scenarios.

Scenario Definition

A1 Indoor office/residentialB1 Urban micro-cell hotspotB2 Bad urban micro-cell

(same as B1 + long delays)B3 Large indoor hall hotspot

(train station, airport)

4.1 WINNER

The 3rd generation partnership project (3GPP) released the spatial chan-

nel model (SCM) in 2002, which was developed for cellular systems with

multiple antennas in the frequency range 2–5 GHz [131]. The original

model was designed for outdoor links and specified in three scenarios:

Suburban macro, urban macro and urban micro. It was parametrized in

two dimensions, considering only azimuth angles and neglecting the ele-

vation domain. Later the development of the SCM lead to the WINNER

(wireless world initiative new radio) [132], SCME (SCM Extension) [133],

WINNER II [134], WINNER+ [135], IMT-Advanced [136] and QuaDRiGa

[137] models, covering a multitude of outdoor and indoor scenarios and ex-

tensions such as elevation angles, continuous time evolution, frequencies

up to 6 GHz and bandwidths up to 100 MHz. The WINNER-based models

are some of the most widely used channel models and have been validated

by several measurement campaigns [138, 139]. Among the many scenar-

ios for which WINNER is parametrized, the scenarios most relevant to

mm-wave systems are listed in Table 4.1.

The WINNER model is a geometry-based stochastic channel model

(GSCM), where the large-scale propagation parameters (LSPs), such as

delay spread or shadow fading, are determined randomly based on sta-

tistical distributions extracted from comprehensive measurements [134].

The model is antenna independent, which means that it can be used

with different antenna configurations. For most scenarios, a distance-

dependent LOS probability function is used to determine the channel con-

dition, and the parameters are defined separately for LOS and NLOS. A

dependency between the different LSPs was observed in many channel

measurements, and are hence taken into account using their correlation.

Based on the LSPs and tabulated distribution functions, small-scale pa-

rameters (SSPs), i.e parameters taking into account the physical prop-

erties of rays including delays, powers and directions of departure and

49


arrival, are formed, thus fully describing the propagation channel. Chan-

nel coefficients are then generated by applying random initial phases to

the rays. Each scenario has a predefined number of clusters ranging from

8 to 15 in LOS cases and 10 to 20 in NLOS cases, and the number of rays

per cluster is fixed to 20.

WINNER models apply the so-called “drop” concept, meaning that the

LSPs are assumed constant in a single channel segment, but have no cor-

relation with LSPs in adjacent channel segments, i.e., other drops. Spa-

tial consistency, meaning that two closely located mobile stations (MSs)

experience similar power, delay and angular dispersion, is thus not sup-

ported. Therefore the WINNER model is incapable of modeling, for in-

stance, device-to-device (D2D) links, where both link ends are moving.

Furthermore, the WINNER model assume that antenna arrays are elec-

trically small, which may not be a valid assumption for very large antenna

arrays [25].

4.2 COST 2100

The COST 2100 channel model [140, 141], developed within the COST

(European cooperation in science and technology) framework, resembles

the WINNER model in being a GSCM and having similar LSPs, SSPs

and clusters. In contrast to WINNER, where the clusters are drawn ran-

domly in each drop, the COST 2100 model has fixed cluster positions. The

position-defined clusters allow smooth user movement and close-by MSs

are able to share the same scatterers. Three type of clusters are defined,

namely local clusters, single-bounce clusters and twin clusters. The local

clusters are always visible to the MS, while single-bounce and twin clus-

ters are associated with a visibility region, which is a circular region in-

side which a cluster is visible. Signal paths propagating through the twin

clusters are reflected several times between the transmitter and receiver.

A drawback of the COST 2100 channel model is that parametrization of

the cluster parameters such as the radii of the visibility regions requires

identification of wave scatters from the channel sounding, which is not

always straightforward [25, 142]. Moreover, alike the WINNER model,

also the COST 2100 model does not support D2D links because the COST

model is designed for cellular channels where the base station is always

fixed, while the D2D link may have mobility on both link ends. In an

attempt to improve the COST 2100 model, a modification of the original

50


model framework for covering D2D links has been proposed in, e.g., [143].

4.3 IEEE 802.11ad

The IEEE 802.11ad channel model was developed by the IEEE 802.11

task group AD for 60 GHz wireless local area networks (WLANs) [144,

145]. The model aims at taking into account all the relevant character-

istics of 60 GHz propagation channels, and supports beamforming and

non-stationary channels due to moving people. The model is based on

clustered rays and provides accurate space-time and polarization charac-

teristics of each ray, which consist of the LOS path and first and second

order reflections. Channel sounding and ray tracing has been used to

parametrize the model for three indoor scenarios, namely a conference

room, a cubicle and a living room. As the layouts for the three environ-

ments are specified very precisely, the parameters may not be valid in

other similar environments [25]. Moreover, no diffuse scattering is in-

cluded in the model.

4.4 METIS channel model

The European 7th framework project METIS (Mobile and wireless com-

munications Enablers for the Twenty-twenty Information Society) was

founded to lay a foundation for 5G [26, 146]. To establish the first 5G

channel model, the following requirements were identified:

• Support of a wide range of network topologies, such as D2D

• Frequency bands up to 86 GHz and bandwidths up to 500 MHz

• Support for very large antenna arrays

• Spatial consistency and mobility

• Realistic modeling of specular reflections

The final model consists of a deterministic map-based model, a stochastic

model model, and a hybrid model [146]. The map-based model is meant

for use cases where realistic spatial channel characteristics are needed,

such as for large antenna arrays. The model is based on simple 3D geome-

tries and ray tracing, including the propagation mechanisms discussed in

Section 2.1. Shadowing and scattering objects are placed randomly in the

51


environment. If it is possible to compromise the details of the generated

radio channels with reduced computational load, different propagation

mechanisms may be turned off. Validation work for the map-based model

has been presented in, e.g., [147]. On the other hand, the METIS stochas-

tic model follows the WINNER framework, and is specified separately for

a number of scenarios and frequency bands. The METIS hybrid channel

model takes advantage of the map-based and stochastic models for vary-

ing levels of demands in accuracy and complexity. It obtains the path loss

and shadowing from the map-based model, and other parameters from the

stochastic model. The usability of the METIS model at mm-wave frequen-

cies is still unknown as very little validation work has been conducted.

4.5 Other mm-wave channel modeling works

The project MiWEBA (Millimetre-Wave Evolution for Backhaul and Ac-

cess) has also contributed to mm-wave channel modeling [148]. The chan-

nel modeling approach follows the same general structure as the IEEE

802.11ad model, but where the 802.11ad model takes into acount only

deterministic rays, the MiWEBA model is quasi-deterministic, combining

deterministic rays, rays from random objects and rays from moving ob-

jects [149].


4.6.1 Spatio-temporal channel model for large indoorenvironments

In [V], a simple stochastic channel model structure is proposed for the 60-

and 70-GHz bands based on channel sounding in large indoor environ-

ments. The measurements reveals that clustering is not apparent in the

studied environments, in contrast to the channel modeling frameworks of

WINNER, COST 2100 and IEEE 802.11ad. Moreover, the results show

that specular paths dominate over diffuse paths, and that propagation at

60 and 70 GHz is very similar with slightly faster power decay at 70 GHz

compared to 60 GHz. The channel model is defined for LOS channels and

takes into account both specular and diffuse paths. Based on detailed

instructions, CIRs can be generated for a given link distance, frequency

52


Azimuth angle [°]90 180 270 360

Del

ay [

ns]

150

100

50

0 -120

-100

-80

-60

(a)

Azimuth angle [°]90 180 270 360

Del

ay [

ns]

150

100

50

0 -120

-100

-80

-60

(b)

Figure 4.1. PADPs from a) the measurement and b) the proposed channel model [V].

and bandwidth. An example of a measured power angular delay profile

(PADP) and a PADP generated with the channel model are presented in

Figure 4.1. The validity of the channel model is shown in terms of path

loss and delay spread.

4.6.2 Parametrization of WINNER channel model in shoppingmall at 60 GHz

A first parametrization of the WINNER at 60 GHz is presented in [IV],

in which both LSPs and SSPs are derived for a shopping mall. Both LOS

and obstructed LOS (OLOS) links are measured, where the shadowing for

OLOS links occurs due to pillars. The parameters are diplayed in Table

4.2, where PL stands for the path loss, μ is the mean value and σ is the

standard deviation. An initial validation of the delay spread and K-factor

is performed, showing good agreement between measured channels and

channels produced by the WINNER implementation described in [150].

Furthermore, parametrizations based on simulations for a cafeteria and

an open square are reported in [26].

4.6.3 Characterization of cross-polarization at 70 GHz

In [VI], wideband channel sounding at 70 GHz is conducted in four in-

door sites, namely an empty office, a furnished office, a shopping mall

and a railway station. By rotating a horn antenna at the Tx side, both

co-polarized (co-pol) and cross-polarized (x-pol) channels are measured.

Using a peak detection algorithm to find paths in the PDPs, as shown in

Figure 4.2(a), the path-wise cross polarization ratio (XPR) is calculated as

the ratio between the co- and x-pol path amplitudes. The cumulative dis-

tribution function (CDF) is shown in Figure 4.2(b), in which also the an-

53


Delay [ns]0 50 100 150

Am

plitu

de [

dB]

-130

-120

-110

-100

-90

-80

-70Co-pol PDPX-pol PDPCo-pol peaksX-pol peaks

(a)

XPR, XPD [dB]10 20 30 40

CD

F

0

0.2

0.4

0.6

0.8

1XPRXPD

(b)

Figure 4.2. a) Co- and x-pol PDPs and peaks, b) CDF of XPR and XPD [VI].

tenna cross-polarization discrimination (XPD) is portrayed. The results

show that the mean XPR value in the studied environments is around

23 dB, which is close to the XPR value of 20 dB specified in the IEEE

802.11ad channel model for 60 GHz [144], and clearly higher than XPRs

of 4–12 dB specified in WINNER II for frequencies below 6 GHz [134].

However, it must be kept in mind that the obtained XPR values might be

underestimated as many of the high XPR values might not be detected

due to the poor dynamic range in the x-pol measurement and the antenna

XPD of roughly 34 dB.

4.6.4 Line-of-sight probability at millimeter-wave frequencies

Applying point cloud data and the shadowing detection method described

in Section 3.2.2, a novel method to evaluate LOS probability is proposed

in [VII]. The LOS probability is calculated in two new scenarios, an open

square and a shopping mall, as well as in an office environment, as de-

picted in Figure 4.3. Base stations (BSs) are deployed on typical loca-

tions, such as by the ceiling, and a very high number of MSs are placed

in locations where users can go. For each BS-MS link, the shadowing is

checked with (3.5), and finally the LOS probability as a function of link

distance is calculated for the three scenarios separately. Existing LOS

probability models including the ITU-R and a linear model as well as our

proposed generic exponential model are parametrized. The LOS probabil-

ity for the three scenarios differed notably due to the size and structure of

the environment, but the exponential model shows excellent performance

in all scenarios. A study on the impact of frequency shows that due to

the increasing Fresnel zone radius with decreasing frequency, the LOS

probability at 2.4 GHz is clearly lower than at 63 GHz. This implies that

the ray assumption is not valid at microwave frequencies in the studied

54


Link distance [m]0 20 40 60 80 100 120

LO

S pr

obab

ility

0

0.2

0.4

0.6

0.8

1CalculatedITU-R UMiExponentialLinear

(a)

Link distance [m]0 20 40 60 80 100

LO

S pr

obab

ility

0

0.2

0.4

0.6

0.8

1CalculatedITU-R UMiExponentialLinear

(b)

Link distance [m]0 5 10 15 20 25 30

LO

S pr

obab

ility

0

0.2

0.4

0.6

0.8

1CalculatedITU-R InHExponentialLinear

(c)

Figure 4.3. LOS probability at 63 GHz in a) an open square, b) a shopping mall and c) anoffice [VII].

scenarios.

55


Table 4.2. WINNER II parameters at 70 GHz in a shopping mall.

LOS OLOS

PL = A log10(d) +B [dB] A 18.4 3.6B 68.8 94.3

Delay spread (DS) μ −8.28 −7.78log10([s]) σ 0.32 0.10

Azimuth spread of departure (ASD) μ 1.09 1.61log10([°]) σ 0.43 0.11

Azimuth spread of arrival (ASA) μ 1.19 1.62log10([°]) σ 0.47 0.14

Shadow fading (SF) [dB] σ 1.2 2.1

K-factor [dB] μ 7.9 N/Aσ 5.8 N/A

Cross-correlations

ASD [°] vs DS [s] 0.4 0.5ASA [°] vs DS [s] 0.2 0.3

ASA [°] vs SF [dB] 0.0 0.1ASD [°] vs SF [dB] 0.0 −0.1

DS [s] vs SF [dB] 0.2 −0.4ASD [°] vs ASA [°] 0.0 0.0ASD [°] vs K [dB] −0.4 N/AASA [°] vs K [dB] −0.3 N/A

DS [s] vs K [dB] −0.2 N/ASF [dB] vs K [dB] 0.2 N/A

Delay scaling parameter rτ 2.5 2.0

XPR [dB] μ 20 2σ 20 2

Per cluster shadowing [dB] σ 2.5 5.3

Number of clusters 4 10Number of rays per cluster 20 20Cluster ASD [°] 1.5 1.5Cluster ASA [°] 1.5 1.5

Correlationdistance [m]

DS [s] 1 0.5ASD [°] 2 0.5ASA [°] 1 1SF [dB] 0.5 0.5K [dB] 1 N/A

56

5. Millimeter-Wave Channel Sounding

Measurements have always been the foundation for understanding the

propagation behavior of radio signals. Since Marconi’s first long-range

experiments in 1895, devices for detecting the radio signals have become

increasingly sophisticated and complex, allowing a more detailed charac-

terization of different radio wave propagation phenomena. Field strength

measurements as a function of link distance were conducted already be-

fore the Second World War [151], and still in the 1960s, the field strength

was the principal area of interest within the wireless communications

community. The next step was to study the delay of multipath compo-

nents [152], and finally, in the 1990s, focus was laid also on directional

properties of the channel [15, 153]. Simultaneously, the environments

have changed from large, outdoor spaces to smaller, indoor scenarios.

Also the frequency of interest has shifted from tens of megahertz to even

terahertz frequencies. Although the first mm-wave experiments were con-

ducted already in the 19th century [154], it wasn’t until the 1990s when

mm-wave channel measurements were being performed from a wireless

communications’ perspective [155]. Present mm-wave equipment includ-

ing wideband systems and huge antenna arrays enables a very detailed

study on the effect of small objects in the environment, e.g., [106].

Preferably, measurements should be able to characterize every dimen-

sion of a propagation channel, including the power, delay, azimuth and

elevation angles on both Tx and Rx sides, Doppler spread, polarization

and time variance. However, present channel sounders have certain lim-

itations and, e.g., mm-wave sounders are not able to capture all of the

aforementioned aspects simultaneously, as will be discussed next.

57

Millimeter-Wave Channel Sounding

5.1 Narrowband measurements

When one is interested only in the field strength, i.e., the path loss from

the transmitter to the receiver, narrowband measurements are the easi-

est option. A narrowband signal can be measured for instance by trans-

mitting a continuous wave generated by a Gunn oscillator and observ-

ing the received signal with a spectrum analyzer [60, 156]. Due to the

simple channel sounder structure, the instrumentation costs are low and

the measurement duration is very short, allowing measurements of time-

variant channels. To produce the baseband signal for mm-wave chan-

nels, the oscillator does not have to work at mm-wave frequencies, but

the oscillator frequency can be multiplied to the desired frequency. The

radio signal can be downconverted to lower frequencies also at the detec-

tion [15]. Examples of narrowband measurements at mm-waves can be

found in [48,157,158].

5.2 Wideband channel measurements

To estimate the delays and amplitudes of individual multipaths, that is,

to measure a CIR, wideband measurements are required. These can be

conducted either in the delay or frequency domains.

5.2.1 Measurements in the delay domain

Wideband channel sounding in the delay domain is usually performed

by transmitting short pulses or continuous wave modulated by a pseu-

dorandom sequence, and sampling the received signal in the delay do-

main using a sliding correlator or an A/D sampling card [159–161]. The

CIR is obtained from a convolution of the transmitted and received sig-

nals. Sounding in the delay domain is very fast and can thus be used

to measure time-variant channels. The drawbacks include a quite com-

plex instrumentation and a limited delay resolution caused by the limited

speed of the sampling unit. Results from wideband mm-wave channel

sounding campaigns using delay domain techniques have been shown in,

e.g., [82,162–166].

58


5.2.2 Measurements in the frequency domain

In frequency domain measurements, the frequency is swept either con-

tinuously or with discrete frequency steps referred to as the frequency-

stepping method [167]. Frequency-stepping is slower compared to a con-

tinuous sweep, but it provides higher measurement accuracy. Such mea-

surement systems are usually realized with a vector network analyzer

(VNA), and the time domain response, i.e., the CIR, is obtained by an in-

verse Fourier transform of the frequency transfer function. As the band-

width is inversely proportional to the delay resolution, a large bandwidth

is essential in resolving multipath components from each other. One

drawback of VNA-based systems is that a cable connection is needed be-

tween the transmitter and the receiver, which limits the range of the link

distance [168]. By using optical fibre cables, the limit can nonetheless

be extended to cover hundreds of meters [36]. Compared to delay do-

main sounders, VNA-based systems are also slower and cannot be used

for time-variant channels [159]. The main advantage of using a VNA is,

beside the simple sounder structure, the ability for very wideband chan-

nel measurements. VNA-based channel sounding at mm-wave frequen-

cies has been conducted in [I-VI], [36–38,60,159,169–176].

5.3 Directional channel measurements

From the antenna design point of view, the directional properties of the

channel at both the Tx and Rx sides are highly important. To determine

the direction of the multipath components there are two distinct methods:

rotating a directional antenna or using an antenna array [15].

5.3.1 Rotation of directional antenna

A very directive antenna, typically with a half-power beamwidth less than

10°, is installed on the link end where directional characteristics are of

interest. The antenna is then rotated so that the antenna is pointing in

different directions, and at each rotation angle the radio channel is mea-

sured. The angular resolution can be improved by having a narrower an-

tenna beamwidth, but at the same time the number of pointing directions

is increased. The mechanical rotation of the antenna is a time-consuming

method, especially when using a VNA to measure the CIRs. A full 3D scan

at one link end, including antenna pointing directions in both azimuth

59


and elevation domains, can easily take tens of minutes, and for such mea-

surements at both link ends the total measurement duration can be sev-

eral hours. Thus, only very static directional channels can be measured

accurately. However, the use of directional antennas decreases the need

for post-processing as the CIRs are measured individually for each direc-

tion. To remove the effect of the antennas when deriving channel metrics

such as path loss, the antenna gains have to be subtracted from the mea-

sured channel data, e.g., [36]. Directional channel measurements with

rotating antennas are presented in [III–VI], [36–38,162–164,177,178].

5.3.2 Antenna array measurements

When using an antenna array, the antenna elements should be as omni-

directional as possible and the directional information is determined by

array signal processing. The relative locations of the antennas should be

well defined and the separation between antennas should be in the order

of one wavelength [15]. The array can either be a real array, where the

CIRs for all antennas can be measured simultaneously when the sounder

is equipped with multiple radio frequency (RF) chains, or a multiplexed

array in which case a switch is used to measure the antenna elements

one by one with a single RF chain. The array can be modeled virtually

by moving a single antenna between predefined positions. With the vir-

tual antenna technique the measurement complexity and hardware costs

are low, but they come at a price of increased measurement time. For

example, in [179], the duration of measuring a single link is more than

20 minutes. Directional characterization using antenna arrays has been

done in [I],[II], [65,179–181].

5.4 Polarization measurements

To measure the depolarization due to the environment, orthogonal polar-

izations are required at the Tx or Rx antenna. A common solution at mm-

waves is to use a horn antenna, for which two orthogonal polarizations

are obtained by rotating the antenna by 90°. Polarimetric measurements

at mm-waves have been reported in for example [VI], [102,120,182,183].

60


5.5 Millimeter-wave channel sounding campaigns for 5G scenarios

So far, a number of mm-wave channel measurements have been performed

in a large variety of environments. The first campaigns were dedicated

to point-to-point links [155, 184–187], and in the nineties the research

turned to indoor environments [167,188–191]. As the use of wireless com-

munication systems has become more widespread, new scenarios for net-

work deployment have been defined. Nowadays, mm-waves are foreseen

to be used in point-to-point links, i.e., backhaul links, and in short-range

access point and cellular scenarios, so-called hot spots and small cells.

Considering 5G scenarios, a need for mm-wave communications are seen

in, e.g., urban micro scenarios such as street canyons and open squares,

stadiums, indoor environments such as offices, shopping malls, airports

and train stations, and outdoor-to-indoor scenarios [27, 28, 192–194]. Al-

though mm-wave indoor measurement results are widely available [195],

the number of works in these new environments, especially in the large in-

door spaces, is small. Results found in the literature include, e.g., 28 GHz

measurements in a train station and an airport terminal [196]. Multi-

frequency channel measurements in an airport are reported in [38].


In this work, mm-wave channel sounding has been performed mainly for

the following purposes:

1. To acquire general knowledge of mm-wave propagation in different

scenarios.

2. For material parameter tuning and validation of the point cloud-

based simulation tool (Section 3.2) in [I-III,VIII].

3. For parametrization of stochastic channel models in [IV–VI].

For the work, 60- and 70-GHz measurements have been conducted in an

ultrasonic inspection room, small and large offices, a shopping mall, a

train station and an open square. A few insights acquired from the mea-

surements are listed below

• Small spaces containing plenty of fixtures can be dominated by dif-

fuse scattering [I],[II].

61


• In large open environments, specular paths are dominant and can

carry over 90% of the power in LOS links [V].

• In NLOS links, the second and third order reflections can account

for the majority of the power [III].

• Multipath clustering is not evident in large indoor environments

[IV],[III].

• XPR is higher at mm-waves compared to microwave frequencies [VI].

62

6. Applications for Channel Models

The performance of wireless systems can be evaluated from many differ-

ent perspectives, but they all require some kind of a channel model. Link

level design, such as comparing transmission techniques, needs stochas-

tic channel models which are independent of specific locations, while base

station deployment requires site-specific models. Next, a short overview

is given as to typical usage of both stochastic and site-specific channel

models.

6.1 The use of stochastic channel models

6.1.1 Coding and modulation

In wireless communications, coding and modulation are used to convert

the data into a form that can be transmitted over the wireless channel and

received by the receiver in an efficient and reliable manner. In [197,198],

various coding schemes are compared in terms of bit error rate (BER) and

throughput. The work in [199] compares different modulations and coding

rates to evaluate the performance of wireless systems in a hospital.

6.1.2 Mobile terminal antenna design

Mobile phones and others mobile terminals need to function regardless

of the orientation of the device, or with the influence of the user. Thus,

the polarization plays an important role and should be considered by

the channel model used for evaluating mobile antenna performance. The

work presented in [200] investigates the influence of antenna placement

in a handset on, e.g., the capacity. The handset antenna placement is also

studied in [201], which also looks at the effect of the user on the antenna

63

Applications for Channel Models

efficiency.

6.1.3 Network design

Network design involves for instance planning the cell layout, determin-

ing the cell radius and designing BS antennas. In [24], the influence of

the antenna and cell radius is investigated with regard to the signal-to-

interference ratio (SIR) and signal-to-interference-and-noise ratio (SINR).

It is observed that beamforming is needed to achieve sufficient network

coverage. In [202], the SINR is studied for different BS antenna down

tilt angles, concluding that tilt angles between 4° and 8° are good for

cells with a radius of 300 m. The work reported in [203] evaluates the

throughput of different multiple-input multiple-output (MIMO) antenna

array configurations.

6.1.4 Capacity and throughput evaluation

For the end user in a wireless system, the throughput, i.e., the data rate,

is one of the most relevant factors affecting the quality of service and

has thus been investigated widely. In [204], a statistical mm-wave chan-

nel model is used for system simulation, showing that mm-wave systems

can offer an order of magnitude increase in capacity compared to 4G net-

works without increasing the cell density. In [205], the performance in

high speed trains is studied in terms of BER and throughput. The differ-

ence between a 2D and a 3D channel model is presented in [206], which

shows that the 2D model underestimates the channel throughput by 20%

compared to the 3D model.

6.2 The use of site-specific channel models

6.2.1 Coverage analysis

Coverage analysis is one of the most typical simulations in network de-

ployment, including tasks such as finding the optimal locations of base

stations. Ray tracing tools have popularly been used for this purpose, as

presented in, e.g., [207,208]. The coverage along with the effects of coding

and antenna sectorization is studied in [209]. In [210], the influence of the

room geometry, wall material and antenna locations are studied. A study

of an urban small cell mm-wave backhaul network, including for instance

64

Applications for Channel Models

0 10 20 300

2

4

6

8

Inter−user distance (m)A

vera

ge n

o. o

fco

mm

on s

catte

rers

(a)

0 10 20 300

0.5

1

Inter−user distance (m)

Cor

rela

tion

coef

fici

ent

6x6 array10x10 array16x16 array20x20 array

(b)

Figure 6.1. Average number of a) common scatterers and b) pairwise orthogonality be-tween two channel vectors for different array sizes [VIII].

outage probabilities and coverage, is presented in [211].

6.2.2 Base station antenna design

In [212], different BS antenna arrays are investigated in an office envi-

ronment in terms of the throughput. It is shown that placing the anten-

nas only in the azimuth domain gives higher throughput that distributing

them in elevation or both azimuth and elevation. A similar investigation

in outdoor macro and pico cells is conducted in [213], where the influence

of BS antenna configurations on the outage and throughput is presented.

6.3 Contribution of thesis

In [VIII], the mutual orthogonality of mm-wave massive multiuser (MU)-

MIMO channels is studied in an open square. The channel data is gener-

ated with the point cloud-based simulation tool described in Section 3.2,

resulting in both LOS and OLOS links. Shadowing is caused by lamp-

posts, trees and people. It is seen that the inter-user distance has a

clear correlation with the number of common scatterers, as seen in Figure

6.1(a). Moreover, the influence of the antenna array size on the pairwise

orthogonality between two channel vector is shown, as depicted by Fig-

ure 6.1(b). Furthermore, the result suggests that the number of active

users should be smaller than at microwave frequencies because mm-wave

channels are sparser in terms of multipaths compared to lower frequen-

cies [182]. The capacity analysis suggests that the separation between

users should be twice the correlation distance of shadowing, 16 m, to take

advantage of spatial multiplexing.

65

7. Summary of Publications

[I] Sixty Gigahertz Indoor Radio Wave Propagation PredictionMethod Based on Full Scattering Model

A propagation prediction method relying on accurate point cloud data,

obtained by laser scanning, and a single-lobe directive scattering model, is

presented. The prediction method is applied in two indoor environments,

an ultrasonic inspection room and a small office, and the scattering model

parameters are tuned based on measurements at 60 GHz. The agreement

between measured and predicted PDPs, mean delay, rms delay spread,

PASs, azimuth spread and elevation spread is found to be very good.

[II] Impacts of Room Structure Models on the Accuracy of 60 GHzIndoor Radio Propagation Prediction

The impact of the point density on the prediction accuracy of the point

cloud-based propagation prediction method is analyzed in a small office

with five densities ranging from an average point separation of 1 to 30

cm. The different densities yield similar estimates of the rms delay spread

and azimuth and elevation spreads compared to 60 GHz measurements,

showing that the simulation speed can be enhanced by lowering the point

cloud density without compromising on the prediction accuracy. The PDPs

show more fluctuation with lower density.

66

Summary of Publications

[III] Indoor Propagation Channel Simulations at 60 GHz Using PointCloud Data

The point cloud-based propagation prediction method is extended by in-

tegrating all relevant propagation mechanisms including specular reflec-

tion, scattering, diffraction and shadowing. The material parameters are

tuned based on 60 GHz measurements in a cafeteria, showing that typ-

ical indoor materials can be modeled with a single relative permittivity.

The method is validated in both LOS and NLOS scenarios, in terms of

path loss, mean delay, rms delay spread and azimuth spread, as well as

by PDPs and PASs. Results show very small prediction errors of 0.5 dB,

0.3 ns and 3° for power, delay and angular domains in LOS links, and

relative errors of only 10% in NLOS links.

[IV] Radio Propagation Measurements and WINNER IIParametrization for a Shopping Mall at 60 GHz

Directional wideband channel sounding in the 60 GHz band is conducted

in a shopping mall to acquire PADPs for both LOS and OLOS links. In

OLOS cases, shadowing is caused by pillars. WINNER II parameters for

power, delay and angles are derived along with those for correlation and

clustering. An initial validation shows good agreement between measured

channels and channels reproduced with the WINNER II implementation

for the rms delay spread and K-factor.

[V] A Statistical Spatio-Temporal Radio Channel Model for LargeIndoor Environments at 60 and 70 GHz

Channel sounding at 60 and 70 GHz in four indoor spaces is used to de-

velop a novel stochastic channel model framework. Specular and diffuse

components are modeled separately, and in contrast to common channel

models, clustering is not included because the measurements do not show

apparent clustering effects of multipaths. Parameters are determined

separately for the two frequencies and the four environments, and in-

structions on implementing the channel model are given. The validity of

the model is demonstrated in terms of path loss and rms delay spread.

67

Summary of Publications

[VI] Polarisation Characteristics of Propagation Paths in Indoor 70GHz Channels

Polarimetric directional wideband channel sounding is conducted in in-

door environments at 70 GHz in order to study the effect of depolariza-

tion. Peak detection is used to find specular propagation paths in PDPs

of co-pol and x-pol links, and the XPR is calculated as the ratio between

the co-pol and x-pol path powers. The XPR is seen to vary between 10 and

30 dB with a mean value of 23 dB. The result shows that in the studied

environment the polarization is better preserved at mm-waves compared

to microwaves, implying that polarization diversity can be used more ef-

fectively.

[VII] Evaluation of Millimeter-Wave Line-of-Sight Probability WithPoint Cloud Data

A novel method to evaluate LOS probability based on point cloud data

and Fresnel zones is presented. The LOS probability is calculated in two

new scenarios, i.e., an open square and a shopping mall, as well as an

office environment. The ITU-R, a linear model and a proposed generic

exponential model are parametrized for all three scenarios. The exponen-

tial model performs excellently in all scenarios despite large differences

among them. The dependency of the frequency is also portrayed.

[VIII] On the Mutual Orthogonality of Millimeter-Wave Massive MIMOChannels

The mutual orthogonality of mm-wave massive MU-MIMO channels are

studied in an open square scenario. The dependency of mutual orthog-

onality on the inter-user distance, number of active users and antenna

array size is characterized. The results show that the number of active

users should be small, and that a separation distance of at least twice the

correlation distance of shadowing (16 m) is required to assure efficient

spatial multiplexing.

68

8. Conclusions

This thesis focuses on mm-wave channel modeling for future 5G wireless

communication systems. The main scientific contributions of the work

include tools and insights for both site-specific and stochastic channel

modeling, and emphasize the need for more detailed descriptions of the

model frameworks and environment descriptions at mm-waves compared

to lower frequencies.

The first part of the thesis describes deterministic field prediction, point-

ing out that even if good agreement between measured and predicted

power and delay metrics can be achieved in terms of mean values, ac-

curate databases are required to obtain good prediction accuracy for the

angular domain and in terms of, e.g., power delay profiles. Moreover, the

effect of scattering is emphasized. This work focuses on developing a novel

field prediction tool relying on accurate environment data in the form of

point clouds. Methods to account for relevant propagation mechanisms,

including specular reflections, scattering, diffraction and shadowing, are

detailed. The point cloud-based prediction method is validated in both

diffuse- and specular-dominant scenarios, showing excellent agreement

in power, delay and angular domains for both LOS and NLOS links.

The second subject of the thesis deals with stochastic channel modeling.

A review of the common channel modeling frameworks stresses that the

use of mm-waves introduces new challenges related to, e.g., massive an-

tenna arrays and D2D links. A novel stochastic spatio-temporal channel

model structure is proposed, which in contrast to common models does

not consider clustering as it is not found apparent in the channel mea-

surements. A detailed channel model implementation recipe is given and

the validity at 60 and 70 GHz is demonstrated by studying path loss and

delay spread. Measurements are also used for deriving parameters for

the WINNER II channel model at 60 GHz in a shopping mall and for an

69

Conclusions

initial validation of the parameters. Moreover, 70-GHz channel sounding

results are used to characterize depolarization in indoor environments,

showing that the XPR at mm-waves is usually over 20 dB and can thus

offer improved polarization diversity compared to lower frequency bands.

Last, a novel method to evaluate LOS probability based on point clouds is

presented.

In the third part of the thesis, mm-wave channel sounding is discussed

in short, comparing pros and cons of typical sounding equipment. It is

noted that no mm-wave sounder can measure everything at once, and

that compromises have to made between speed and accuracy. A litera-

ture review affirms that many of the large indoor spaces envisioned for

mm-wave deployment are lacking channel sounding results. Lastly, a few

insights acquired through mm-wave channel sounding are provided. For

example, large indoor spaces are dominated by specular paths and do not

show clear signs of multipath clustering.

The last part of the thesis is dedicated to applications for channel mod-

els, and provides exemplary use cases for both stochastic and site-specific

channel models, such as BS antenna design and throughput evaluations.

A study on mutual orthogonality for massive mm-wave MU-MIMO chan-

nels is performed with the aid of point cloud-based propagation prediction.

The results show that compared to microwave frequencies, mm-wave sys-

tems must have fewer active users and larger inter-user distances to allow

efficient spatial multiplexing.

Despite the many valuable contributions presented in this work, there

is still a great deal of efforts required to ensure the successful deploy-

ment of mm-wave networks. Among these, the most significant task is

to validate the existing channel model frameworks by means of channel

sounding and accurate field prediction tools in various mm-wave bands

and environments. The results from these actions can be used to derive

models which are valid in an extremely wide range of frequencies, or to

point out possible model deficiencies and propose solutions. Last, as no

thesis devoted to wireless communications is complete without a refer-

ence to higher frequencies, it must be mentioned that the future will most

likely bring even higher frequencies into the midst of our super-connected

society [214].

70

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87

Errata

Publication VII

In Table II, the parameters for Eqs. (3) and (4) should be swapped. In Fig.

3, the exponential model should refer to (4), and the linear model should

refer to (3).

88

This thesis focuses on mm-wave channel modeling for future 5G wireless communication systems. The main contributions of the work include simulations tools and insights acquired through channel measurements. The work emphasize the need for more detailed descriptions of the model frameworks and environment descriptions at mm-waves compared to lower frequencies.

Aalto-D

D 16

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ISBN 978-952-60-6971-5 (printed) ISBN 978-952-60-6972-2 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) Aalto University School of Electrical Engineering Department of Radio Science and Engineering www.aalto.fi

BUSINESS + ECONOMY ART + DESIGN + ARCHITECTURE SCIENCE + TECHNOLOGY CROSSOVER DOCTORAL DISSERTATIONS

Jan Järveläinen M

easurement-Based M

illimeter-W

ave Radio C

hannel Simulations and M

odeling A

alto U

nive

rsity

2016

Department of Radio Science and Engineering


Jan Järveläinen

DOCTORAL DISSERTATIONS

measurement-based millimeter-wave radio channel ... · the spectrum shortage at microwaves has...

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