final report on link level and system level channel...

WINNER D5.4 v. 1.4

Page 1 (167)

IST-2003-507581 WINNER D5.4 v. 1.4

Final Report on Link Level and System Level Channel Models

Date of Delivery to the CEC: Nov. 18th, 2005

Author(s): Daniel S. Baum, Hassan El-Sallabi, Tommi Jms, Juha Meinil, Pekka Kysti, Xiongwen Zhao, Daniela Laselva, Jukka-Pekka Nuutinen, Lassi Hentil, Pertti Vainikainen, Jarmo Kivinen, Lasse Vuokko, Per Zetterberg, Mats Bengtsson, Kai Yu, Niklas Jaldn, Terhi Rautiainen, Kimmo Kalliola, Marko Milojevic, Christian Schneider, Jan Hansen.

Participant(s): EBIT, EBITT, ETHZ, HUT, KTH, NOK, TUI

Workpackage: WP5 Channel Modelling

Estimated person months: 66

Security: Public

Nature: R

Version: 1.4

Total number of pages: 167

Abstract: This document presents WINNER channel models. The channel models cover WINNER propagation scenarios for indoor, urban macro-cell and micro-cell, stationary feeder, suburban macro-cell, and rural macro-cell. Both geometric-based stochastic channel model and reduced-variability (clustered delay-line) models are presented. The channel models are mainly based on measurement data.

Keyword list: Channel modelling, propagation scenarios, wideband, channel sounder, cluster delay domain, angle domain, measurements, delay spread, ray, angle-spread, arrival, departure

Disclaimer:

WINNER D5.4 v. 1.4

Page 2 (167)

Executive Summary This deliverable presents WINNER channel models for link level and system level simulations of short range and wide area wireless communication systems. The developed channel models follow guidelines stated in WINNER deliverable D5.2. The models are antenna independent, i.e., different antenna configurations and different element patterns can be inserted. The covered propagation scenarios are indoor small office, urban micro-cell, indoor, stationary feeder, suburban macro-cell, urban macro-cell, and rural macro-cell. The generic WINNER channel model follows a geometric-based stochastic channel modelling approach, which allows creating of virtually unlimited double directional radio channel model. Clustered delay line models have also been created for calibration and comparison of different simulations. The developed models are based on both literature and extensive measurement campaigns that have been carried out within the WINNER project.

WINNER D5.4 v. 1.4

Page 3 (167)

Authors

Partner Name Phone / Fax / e-mail

ETHZ Daniel S. Baum Phone: +41 44 632 2791

Fax: +41 44 632 1209

E-mail: [email protected]

HUT Hassan El-Sallabi Phone: +358 9 451 5960

Fax: +358 9 451 2152


EBIT Tommi Jms Phone: +358 40 344 2000

Fax: +358 8 551 4344


EBIT Juha Meinil Phone: +358 40 344 2000

Fax: +358 8 551 4344


EBIT Pekka Kysti Phone: +358 40 344 2000

Fax: +358 8 551 4344


EBIT Xiongwen Zhao Phone: +358 40 344 2000

Fax: +358 9 5121233


EBIT Daniela Laselva Phone: +358 40 344 2000

Fax: +358 8 551 4344


EBIT Jukka-Pekka Nuutinen Phone: +358 40 344 2000

Fax: +358 8 551 4344


WINNER D5.4 v. 1.4

Page 4 (167)


EBIT Lassi Hentil Phone: +358 40 344 2000

Fax: +358 8 551 4344


HUT Pertti Vainikainen Phone: +358 9 451 2251

Fax: +358 9 451 2152


HUT Jarmo Kivinen Phone: +358 9 451 2242

Fax: +358 9 451 2152


HUT Lasse Vuokko Phone: +358 9 451 6064

Fax: +358 9 451 2152


KTH Per Zetterberg Phone: +46 8 7907785

Fax:


KTH Mats Bengtsson Phone: +46 8 7908463

Fax:


KTH Niklas Jaldn Phone: +46 8 7908415

Fax:


NOK Terhi Rautiainen Phone: +358 50 4837218

Fax: +358 7180 36857


WINNER D5.4 v. 1.4

Page 5 (167)


NOK Kimmo Kalliola Phone: +358 50 4837226

Fax: +358 7180 36857


TUI Marko Milojevic Phone: +49 3677 69 2615

Fax: +49 3677 69 1195


TUI Christian Schneider Phone: +49 3677 69 1157

Fax: +49 3677 69 1113


ETHZ Jan Hansen Phone : +41 44 632 0290

Fax: +41 44 632 1209


WINNER D5.4 v. 1.4

Page 6 (167)

Table of Contents

Part I................................................................................................................. 11

1. Introduction ............................................................................................... 12

2. WINNER Scenarios.................................................................................... 14 2.1 Scenario definitions.............................................................................................................. 14

2.1.1 Scenario A1: Indoor small office .................................................................................. 14 2.1.2 Scenario B1: Urban micro-cell ..................................................................................... 15 2.1.3 Scenario B3: Indoor hotspot ......................................................................................... 15 2.1.4 Scenario B5: Stationary feeder ..................................................................................... 15 2.1.5 Scenario C1: Suburban macro-cell................................................................................ 16 2.1.6 Scenario C2: Urban macro-cell..................................................................................... 16 2.1.7 Scenario D1: Rural macro-cell...................................................................................... 17

3. WINNER Channel Models ......................................................................... 17 3.1 Generic model...................................................................................................................... 17

3.1.1 Large-scale parameters................................................................................................. 17 3.1.2 Average power of ZDSC conditioned on their delays .................................................... 22 3.1.3 Directional distributions of ZDSCs............................................................................... 23 3.1.4 Antenna gain................................................................................................................ 25 3.1.5 Path-loss models .......................................................................................................... 26 3.1.6 Probability of line of sight............................................................................................ 26 3.1.7 Generation of channel coefficients................................................................................ 27

3.2 Reduced variability clustered delay line model .................................................................. 29 3.2.1 Scenario A1 ................................................................................................................. 30 3.2.2 Scenario B1 ................................................................................................................. 31 3.2.3 Scenario B3 ................................................................................................................. 32 3.2.4 Scenario B5 ................................................................................................................. 33 3.2.5 Scenario C1 ................................................................................................................. 37 3.2.6 Scenario C2 ................................................................................................................. 38 3.2.7 Scenario D1 ................................................................................................................. 39

Part II................................................................................................................ 41

4. Modelling Approaches.............................................................................. 42 4.1 Generic channel modelling approach .................................................................................... 42

4.1.1 Distinction between channel models for link-level and system-level simulation............. 42 4.1.2 Comparison between deterministic and stochastic channel modeling............................. 42 4.1.3 Interference modeling .................................................................................................. 43 4.1.4 Framework .................................................................................................................. 43

4.2 Stationary-feeder scenarios B5 ............................................................................................. 51 4.2.1 B5a LOS stationary feeder: rooftop-to-rooftop.............................................................. 51 4.2.2 B5b LOS stationary feeder: street-level to street-level................................................... 52 4.2.3 B5c hotspot LOS stationary-feeder: below rooftop to street-level. ................................. 52 4.2.4 B5d hotspot NLOS stationary feeder: rooftop to street-level.......................................... 52

4.3 Coefficient generation approaches ........................................................................................ 53 4.3.1 Stationary stochastic .................................................................................................... 53

WINNER D5.4 v. 1.4

Page 7 (167)

4.3.2 Sum-of-Sinusoids......................................................................................................... 54 4.3.3 Problem details ............................................................................................................ 54 4.3.4 Comparison ................................................................................................................. 55 4.3.5 Kronecker correlation................................................................................................... 55

5. Measurements and Literature Review ..................................................... 56 5.1 Measurement systems .......................................................................................................... 56

5.1.1 Principle of channel sounding....................................................................................... 56 5.1.2 Channel sounders employed ......................................................................................... 56

5.2 Measurement campaigns ...................................................................................................... 61 5.2.1 Scenario A1 ................................................................................................................. 61 5.2.2 Scenario B1 ................................................................................................................. 62 5.2.3 Scenario B3 ................................................................................................................. 62 5.2.4 Scenario C1 ................................................................................................................. 63 5.2.5 Scenario C2 ................................................................................................................. 63 5.2.6 Scenario D1 ................................................................................................................. 64 5.2.7 Measurement summary ................................................................................................ 65

5.3 Description of key references ............................................................................................... 67 5.4 Results of analysis items ...................................................................................................... 67

5.4.1 Path-loss and shadow fading ........................................................................................ 67 5.4.2 LOS probability ........................................................................................................... 73 5.4.3 DS and maximum excess-delay distribution.................................................................. 74 5.4.4 Azimuth AS at BS and MS........................................................................................... 79 5.4.5 Distribution of the azimuth angles of the multipath components.................................... 83 5.4.6 Angle proportionality factor ......................................................................................... 85 5.4.7 Modelling of PDP ........................................................................................................ 87 5.4.8 Number of ZDSC......................................................................................................... 91 5.4.9 Distribution of ZDSC delays ........................................................................................ 93 5.4.10 Delay proportionality factor ......................................................................................... 96 5.4.11 Ricean K-factor............................................................................................................ 98 5.4.12 Cross-polarization ratio (XPR) ................................................................................... 101 5.4.13 Large-scale parameter analysis item ........................................................................... 105

5.5 Literature review................................................................................................................ 111 5.5.1 Scenario A1 ............................................................................................................... 111 5.5.2 Scenario B3 ............................................................................................................... 115 5.5.3 Scenario B5 ............................................................................................................... 116 5.5.4 Scenario C1 ............................................................................................................... 120 5.5.5 Scenario C2 ............................................................................................................... 122 5.5.6 Scenario D1 ............................................................................................................... 125

5.6 Interpretation of results ...................................................................................................... 127 5.6.1 Path-loss .................................................................................................................... 127 5.6.2 Power-delay profile.................................................................................................... 131 5.6.3 Delay spread .............................................................................................................. 131 5.6.4 K-factor ..................................................................................................................... 132 5.6.5 Cross-polarization discrimination (XPR) .................................................................... 132 5.6.6 Doppler ..................................................................................................................... 132 5.6.7 Angle-spread.............................................................................................................. 132

WINNER D5.4 v. 1.4

Page 8 (167)

5.6.8 Antenna gain.............................................................................................................. 132 5.6.9 Frequency dependence of the propagation parameters................................................. 133

6. Channel Model Implementation ............................................................. 135 6.1 Overview for implementing the model................................................................................ 135

6.1.1 Time sampling and interpolation ................................................................................ 135 6.1.2 Coordinate system...................................................................................................... 135 6.1.3 Generation of correlated large-scale parameters.......................................................... 137

6.2 Interfaces ........................................................................................................................... 138 6.2.1 Example input parameters .......................................................................................... 138 6.2.2 Example output parameters ........................................................................................ 141

6.3 Guidelines and examples on performing system-level simulations....................................... 142 6.3.1 Handover ................................................................................................................... 142 6.3.2 Interference................................................................................................................ 143 6.3.3 Multi-cell and multi-user............................................................................................ 143 6.3.4 Multihop and relaying................................................................................................ 144

7. Test and Verification of the Channel Model and Its Implementation .. 145 7.1 Test cases........................................................................................................................... 145

7.1.1 General test cases....................................................................................................... 145 7.1.2 Input/output parameters.............................................................................................. 145 7.1.3 Validation of computation .......................................................................................... 146

8. References............................................................................................... 148

9. Appendix .................................................................................................. 153 9.1 Other scenarios .................................................................................................................. 153

9.1.1 Scenario definitions.................................................................................................... 153 9.2 Measurement campaigns for other scenarios ....................................................................... 153

9.2.1 Scenario high mobility short range hot spot............................................................. 153 9.2.2 Urban ad-hoc peer-to-peer.......................................................................................... 154

9.3 Measurement results for other scenarios ............................................................................. 154 9.3.1 Scenario C2: typical urban macro-cell - KTH campaign.............................................. 154 9.3.2 Scenario high mobility short range hot spot............................................................. 156

9.4 Literature review for other scenarios................................................................................... 167 9.4.1 Scenario high mobility short range hot spot............................................................. 167

WINNER D5.4 v. 1.4

Page 9 (167)

List of Acronyms and Abbreviations 3GPP 3rd Generation Partnership Project

3GPP2 3rd Generation Partnership Project 2

ACF Auto-Correlation Function

ADC Analog-to-Digital Converter

AoA Angle of Arrival

AoD Angle of Departure

APP A Posteriori Probability

APS Angle Power Spectrum

AS Azimuth Spread

AWGN Additive White Gaussian Noise

B3G Beyond 3G

BER Bit Error Rate

BRAN Broadband Radio Access Networks

BS Base Station

BW Bandwidth

C/I Carrier to Interference ratio

CDL Clustered Delay Line

CW Continuous Wave

D3SF Double-Directional Delay-Spread Function

DoA Direction of Arrival

DoD Direction of Departure

DS Delay Spread

EBIT Elektrobit Ltd

EBITT Elektrobit Testing Ltd

ESPRIT Estimation of Signal Parameters via Rotational Invariance Techniques

ETHZ Eidgenssische Technische Hochschule Zrich (Swiss Federal Institute of Technology Zurich)

ETSI European Telecommunications Standards Institute

FDD Frequency Division Duplex

FIR Finite Impulse Response

FS Fixed Station

GPS Global Positioning System

HIPERLAN High Performance Local Area Network

HUT Helsinki University of Technology (TKK)

IR Impulse Response

ISIS Initialization and Search Improved SAGE

KTH Kungliga Tekniska Hgskolan (Royal Institute of Technology in Stockholm)

LNS Log-Normal Shadowing

LOS Line-of-Sight

MCSSS Multi-Carrier Spread Spectrum Signal

METRA Multi-Element Transmit and Receive Antennas (European IST project)

MIMO Multiple-Input Multiple-Output

MPC Multi-Path Component

MS Mobile Station

MUSIC Multiple Signal Classification

WINNER D5.4 v. 1.4

Page 10 (167)

NACM No Auto-Correlation Mode

NLOS Non Line-of-Sight

NOK Nokia

OFDM Orthogonal Frequency-Division Multiplexing

OLOS Obstructed Line-of-Sight

PAS Power Azimuth Spectrum

PD3S Power Double-Directional Delay-Spectrum

PDP Power-Delay Profile

RMS Root Mean Square

PN Pseudo Noise

RIMAX maximum likelihood parameter estimation framework for joint superresolution estimation of both specular and dense multipath components

RF Radio Frequency

RX Receiver

SAGE Space-Alternating Generalized Expectation-maximization

SCM Spatial Channel Model

SCME Spatial Channel Model Extended

SF Shadow Fading

SIMO Single-Input Multiple-Output

SoS Sum of Sinusoids

SW Software

TDL Tapped Delay-Line

TUI Technische Universitt Ilmenau

TX Transmitter

WINNER Wireless World Initiative New Radio

WPx Work Package x of WINNER project

XPR Cross-Polarisation Ratio

XPRH Horizontal Polarisation XPR

XPRV Vertical Polarisation XPR

ZDSC Zero Delay-Spread Cluster

ZDSC_A Zero Delay-Spread Cluster of Arrival

ZDSC_D Zero Delay-Spread Cluster of Departure

WINNER D5.4 v. 1.4

Page 11 (167)

PART I The deliverable D5.4 is divided into two major parts. This first part is the

relatively short main part and contains the essence of the deliverable,

specifically the channel model definition.

WINNER D5.4 v. 1.4

Page 12 (167)

1. Introduction WINNER project is aiming at a Beyond-3G (B3G) radio system using a frequency bandwidth of 100 MHz for one radio connection and a radio frequency lying most probably somewhere between 2 and 6 GHz in spectrum. The research concerning the suitability of certain communication parameters, like modulation, coding, symbol rate, MIMO antenna utilisation etc., is performed through extensive simulations. The simulation results depend strongly on the radio channel. Hence, the radio channel is a crucial part of the simulation. On one hand, it is very important to use a very accurate and realistic channel model in the simulation to enable reliable simulation results. On the other hand, the complexity of the simulation should be kept low. Therefore, the research challenge is to create a channel model which is realistic enough and simple.

WINNER Work Package 5 (WP5) is focused on multi-dimensional radio channel modelling. Totally six partners are involved in WP5, namely Elektrobit (EBIT, in year 2004, and Elektrobit Testing EBITT in year 2005), Helsinki University of Technology (HUT), Nokia (NOK), Royal Institute of Technology in Stockholm (KTH), Swiss Federal Institute of Technology Zurich (ETHZ), and Technical University of Ilmenau (TUI). Up to now, the situation is such that there are no widely accepted channel models available which are suitable for WINNER system parameters. Therefore, WINNER WP5 has to create new channel models needed in the project. For the initial purposes, WP5 selected and recommended two existing channel models, which are called initial channel models [D5.1]. The models are 3GPP/3GPP2 Spatial Channel Model (SCM) [3GPP SCM] for outdoor simulations and IEEE 802.11n MIMO model [802.11n] for indoor simulations. Because the SCM model was not suitable for WINNER simulations as such, WP5 performed some modifications and implemented the extended SCM model (SCME) [SCME]. However, in spite of these modifications, the initial channel models were not good enough for the advanced simulations. Consequently new WINNER models are needed.

The WINNER channel models were implemented in two steps. In the first step, channel models for the most urgently needed propagation scenarios with a limited number of parameters were created. Propagation scenario means here the propagation environment and certain propagation related parameters specified to meaningful values. The main difference between different propagation scenarios exists due to the diverse environments. Channel model parameters were defined for five propagation scenarios (prioritised scenarios) according to [D7.2], namely indoor small office (A1), urban micro-cell (B1), stationary feeder (B5), urban macro-cell (C2), and rural macro-cell (D1). These models are described in the deliverable D5.3 [D5.3]. In the second step the channel models were upgraded so that more parameters are included in the models. Two more scenarios indoor (B3) and suburban (C1) are also included based on the feedback from other work packages. The channel models created in the first step, and updated in the second step, are described in this deliverable, D5.4.

In this deliverable, we describe a generic channel model framework that is subsequently used as a basis for the channel models of all scenarios, except B5. Furthermore, we present clustered delay line (CDL) models for calibration and comparison simulations. The generic modelling approach allows the creation of virtually unlimited double directional radio channel realizations. The generic channel model is a ray-based multi-link model that is antenna independent, scalable and capable of modelling channels for MIMO connections. The models are based on the existing literature and the parameters extracted from eleven measurement campaigns performed by the WP5. The selection of the model parameters is based both on the measurements and information found in the literature. The measurements were performed by five partners, namely EBIT/EBITT, HUT, KTH, NOK, and TUI. Different channel sounders, most of them capable of measurements at 2 and 5 GHz frequency ranges and 100 MHz bandwidth, were used. Measurement results were analyzed using beam-forming and super-resolution methods. The analyzed items, e.g. path loss, shadow fading characteristics, power delay profiles, delay spreads, angle-spreads, and cross-polarisation ratio (XPR), were analyzed for the scenarios of interest.

In WP5 one activity has been the implementation of the 3GPP/3GPP2 SCM channel model. The model was implemented in software by the WP5. Later, its extension to 5 GHz frequency range and 100 MHz bandwidth [SCME] was implemented. The extension work has been published in [BGS+05].

We have compiled a set of requirements from various documents, specifically the WP2 Channel Model Requirements, the WP5 Deliverable D5.2 [D5.2], WP7 deliverable D7.2 [D7.2], and reported shortcomings of the channel models selected for initial usage [D5.1]. The main requirements are proper characterisation of spatial properties for MIMO support, large set of possible channels as well as some limited randomness channels, consistency in time, frequency and space, e.g. inherent link between angle spectrum and Doppler spectrum, time-variability of bulk parameters, and extended polarisation support.

The document is organized in a way to provide best readability. Its overall content is divided into 2 major parts. The first part is relatively short and contains the core information provided in this deliverable. Part I begins with an introduction, background information concerning our approach, and the requirements on

WINNER D5.4 v. 1.4

Page 13 (167)

the models defined within the WINNER project. It is followed by related scenario definitions. The major and last chapter of part I contains the brief but comprehensive definition of the channel models. Part II provides more elaborate background information on model development. It contains detailed discussion on our modelling approach, the underlying data of our models (measurements and literature review), and the interpretation thereof. Two more chapters are dedicated to the channel model implementation, and the test and verification of the model. The document ends with references and further, so far unused results of this project.

WINNER D5.4 v. 1.4

Page 14 (167)

2. WINNER Scenarios These are the propagation scenarios defined in WINNER. Scenarios marked in bold are prioritized scenarios that were modelled and implemented as the WINNER channel models.

Table 2.1: Propagation scenarios defined in WINNER.

Scenario Definition LOS/NLOS

Mob. AP ht UE ht Distance range

Note

A1 In building

Indoor small office / residential

LOS/ NLOS

05 km/h

2 m 1 m 3 - 100 m Deterministic room layout

A2 In building

Indoor to outdoor NLOS 05 km/h

AP inside and coverage outside the building.

B1 Hotspot

Typical urban micro-cell

LOS/ NLOS

070 km/h

Below RT, e.g. 10 m

1.5 m 20 - 400 m

B2 Hotspot

Bad urban NLOS 070 km/h

B3 Hotspot

Indoor LOS 05 km/h

B4 Hotspot

Outdoor to indoor NLOS 05 km/h

Airport-type. Coverage in shopping hall with BTS outside.

B5a Hotspot

LOS stat. feeder, rooftop to rooftop

LOS 0 km/h Above RT. Above RT.

30m - 8 km

B5b Hotspot

LOS stat. feeder, street-level to street-level

LOS 0 km/h 2-5 m 2-5 m

B5c LOS stat. feeder, below-rooftop to street-level

LOS 0 km/h As B1. As B1. As B1. As B1.

B5d NLOS stat. feeder, rooftop to street-level

NLOS 0km/h As C2. 1.5-10 m. As C2. As C2.

C1 Metropol

Suburban LOS/ NLOS

070 km/h

35 - 3000 m

C2 Metropol

Typical urban macro-cell

LOS/ NLOS

070 km/h

Above RT, e.g. 32 m

1.5 m 35 - 3000 m

C3 Metropol

Bad urban NLOS 070 km/h

C4 Metropol

Outdoor to indoor NLOS 070 km/h

C5 Metropol

LOS feeder LOS 0 km/h

D1 Rural

Rural macro-cell LOS/ NLOS

0200 km/h

Above RT, e.g. 45 m

1.5 m 35m - 10 km

D2 Rural

LOS moving networks (feeder)

LOS 0300 km/h

2.1 Scenario definitions In the following subsections, we present WP5 view to the environments of the five prioritized scenarios.

2.1.1 Scenario A1: Indoor small office Scenario A1 environment is described in [D7.2]. This represents typical office environment, where the area per floor is 5000 m2, number of floors is 3 and room dimensions are 10 m x 10 m x 3 m and the corridors have the dimensions 100 m x 5 m x 3 m. The A1 indoor office model is illustrated in Figure 2.1.

WINNER D5.4 v. 1.4

Page 15 (167)

Figure 2.1: Layout of the A1 indoor scenario.

The measured environment resembles this definition, but is not identical [WP5AR]. It is assumed that propagation parameters can be deduced from these measurements.

2.1.2 Scenario B1: Urban micro-cell This scenario is defined for environment where both fixed station and mobile station antenna heights are below surrounding buildings and both are outdoors. This scenario covers both LOS and NLOS propagation conditions. The environment is defined for Manhattan like grid. The environment streets can be classified as a main street, where the fixed station is located, perpendicular streets and parallel streets. The scenario is defined for street distance from 20 m to 400 m. In this environment, the radio propagation and cell shape are confined within the area defined by the surrounding buildings.

2.1.3 Scenario B3: Indoor hotspot The scenario B3 is described in [D7.2] and represents a typical indoor hot spot application with a wide coverage area but non-ubiquitous and low mobility (0-5 km/h). In this scenario traffic of high density can be expected. Typically application scenarios can be found in conference halls, factory halls, entrance halls of train stations and airports, where the indoor environment is characterised by large distances. The dimensions of such large halls can range from 20 m x 20 m x 5 m up to more then 100 m in width and length as well as 20 m in height. Both LOS and NLOS propagation situations can be found in this scenario.

2.1.4 Scenario B5: Stationary feeder The definition of this scenario is less well understood by WP5 than are the others. WP5 found that that NLOS cases are also of interest for the feeder applications. We therefore discuss models for NLOS cases as well. The following different feeder scenarios have been studied:

B5a Hotspot LOS stationary feeder: rooftop-to-rooftop

B5b Hotspot LOS stationary feeder: street-level-to-street-level.

B5c Hotspot LOS stationary feeder: blow-rooftop-to-street-level

B5d Hotspot NLOS stationary feeder: above-rooftop-to-street-level.

The scenarios of B5a and B5b are discussed below:

2.1.4.1 Scenario B5a: LOS stationary feeder: rooftop-to-rooftop

Our understanding of this case is illustrated in Figure 2.2. Wireless feeder master-station, probably on an elevated building, is connected to one or several wireless feeder peripheral stations. A hot-spot wireless access point is then connected to the peripheral. As indicated in the picture, a cable is needed to connect the roof-top wireless feeder peripheral antenna. Alternatively a wireless solution may be possible also for these hops but then requiring additional antennas and transceivers.

WINNER D5.4 v. 1.4

Page 16 (167)

Feeder-link

Cable

Hot-spot

Master-station

Peripheral

Figure 2.2: Illustration of LOS stationary feeder: rooftop-to-rooftop.

2.1.4.2 Scenario B5b: LOS stationary feeder: street-level to street-level

Our understanding of this case in indicated in Figure 2.3. Both ends of the link are located a few meters above ground and the model is aimed for 2-5 meter antenna heights. In many cases it may be possible to place the antennas high enough such that the first Fresnel zone is clear and therefore free-space propagation conditions apply.

Hotspot and feeder- master.

Hotspot and feeder- peripheral.

Feeder-link

Figure 2.3: Illustration of wireless LOS feeder-link: street-level.

2.1.5 Scenario C1: Suburban macro-cell The scenario C1 is defined for a suburban outdoor environment, where the coverage is ubiquitous. In suburban macrocells base stations are located well above the rooftops to allow wide area coverage. Buildings are typically low residential detached houses with one or two floors, or blocks of flats with a few floors. Occasional open areas such as parks or playgrouds between the houses make the environment rather open. Streets have random orientations, and no urban-like regular strict grid structure is observed. Vegetation is modest.

2.1.6 Scenario C2: Urban macro-cell In typical urban macrocell, mobile station is at street level and fixed base station clearly above surrounding building heights. As for propagation conditions, non- or obstructed line-of-sight is a common case, since street level is often reached by a single diffraction over the rooftop. The building blocks can form either a regular Manhattan type of grid, or have more irregular locations. Typical building heights in urban environments are over four storeys. Outdoor-to-indoor modelling is not part of typical urban macrocell scenario, but is a different scenario (Table 2.1, C4).

WINNER D5.4 v. 1.4

Page 17 (167)

2.1.7 Scenario D1: Rural macro-cell Scenario D1 is defined only through its size (100 km2) and hexagonal cell lay-out in [D7.2].

The rural environment we measured is flat, consisting of mainly sparsely located houses along roads that lead trough fields and some small forests and a small village. This should be considered when interpreting results based on our model.

3. WINNER Channel Models This chapter describes WINNER MIMO channel models of seven propagation scenarios for link level and system level simulations. Link level is defined for a single communication link. System level is defined for multi communication links and base stations. Five of these scenarios are the prioritized propagation scenarios defined in the WINNER project in [D7.2] for short range and wide area wireless communications. The prioritized scenarios are: Scenario A1 for indoor small office environments, Scenario B1 for microcell urban environment, Scenario B5 for hotspot LOS stationary wireless feeder, Scenario C2 for Metropolitan ubiquitous coverage in macrocell urban environment, Scenario D1 for macrocell rural environment. The two additional scenarios are part of the WINNER channel model: Scenario B3 for indoor propagation and Scenario C1 for macrocell suburban environment.

In this chapter, we provide description of the generic channel model, which is based on the principles of the SCM [3GPP SCM], for scenario A1, B1, B3, C1, C2, and D1. We also present clustered delay line (CDL) models for the mentioned six scenarios of interest to generic model, and stationary feeder models for scenario B5. The generic channel model is a geometric-based stochastic channel model. The following subsections describe the WINNER phase-I MIMO channel models at 5 GHz.

3.1 Generic model We apply the framework of the generic channel modelling approach presented in Chapter 4 to WINNER scenarios A1, B1, B3, C1, C2, and D1. Scenario B5 is not considered in the generic channel model since it is a stationary wireless feeder scenario, where transmitter and receiver ends are fixed. Scenario B5 is modelled separately as clustered (tapped) delay line model (CDL) in Section 3.2.4.

The generic channel model generates a number of ZDSCs. Their delays and directional properties are extracted from statistical distributions that correspond to a specific scenario, which are obtained from measurement results or from literature. The number of ZDSCs varies from one scenario to another. Indeed, the number of ZDSCs itself is a random variable. However, in order to reduce the complexity for simulation purpose, it has been kept as a fixed parameter. The median of the number ZDSCs is selected. We fix the number of rays within each ZDSC to 10 rays that have same delays and powers and may differ in angles, either departure or arrival. The directional properties of each ZDSC may vary from one scenario to another and from departure side to arrival side. The WINNER generic channel model is antenna independent. Hence, different antenna configurations can be supported. In later terminology, the downlink is considered, where the transmitter is the fixed station (BS) and the receiver is the mobile station (MS). However, the same models can also be used for uplink simulations due to the reciprocity of the radio channel.

3.1.1 Large-scale parameters The radio channel is in general not stationary. Nevertheless, over short periods of time and space, channel parameters experience small variations, and the assumption of short-term stationarity is often a very good approximation. The parameters characterizing our channel model are called bulk parameters. The time durations, over which these bulk parameters are constant, are termed channel segments a.k.a. drops in the nomenclature of the SCM. Over time and space, bulk parameters change and we characterize this variability statistically.

There are a large number of bulk parameters. Bulk parameters include detailed or low-level bulk parameters such as number of paths, path powers, path angles at both link ends, path elevations at both link ends, and path delays. To characterize the channel with fewer parameters, higher level, e.g. second-order, statistics are extracted on a per-segment basis, which we denote large-scale or dispersion metric parameters. Large-scale parameters characterize the distributions of and between previously mentioned low-level bulk parameters. Because realisations of large-scale parameters are drawn only once per channel segment, they are bulk parameters themselves. The following large-scale parameters are considered:

Shadowing. The log-normal shadowing (LNS) value is the common shadowing across (i.e., for all) clusters. The variability across clusters around the LNS is given by an additive (in log-domain) Gaussian distribution with a fixed standard deviation of 3 dB.

WINNER D5.4 v. 1.4

Page 18 (167)

Cross-polarization ratio (XPR). No distinction is made between clusters and segments in the current model. Therefore, the resulting variability of XPR is equivalent if evaluated across clusters or across segments.

Total angle-spread and delay-spread. These parameters characterize the power dispersion in angle and delay domain across clusters. Note that this is a high-level characterization. The more detailed properties of angle and delay dispersion are each defined by a set of two variables. This is firstly, a mean angle and a delay offset for each single cluster, and secondly, an angle-spread and a delay-spread for each cluster. Here,

The angle-spread per cluster and delay-spread per cluster values are constants.

The mean angle per cluster and the delay offset per cluster distributions are functions of the total (per segment) angle-spread and the total (per segment) delay-spread.

Large-scale parameters of the channel have clear influence on the channel characteristics. This can be noticed in delay domain characteristics through the RMS delay spread and in the angle domain through the RMS angle-spread in departure and in arrival. The RMS delay spread has influence on power delay spectrum and on the probability density function (pdf) of path delays through the parameter r.. The statistical distributions that generate spatial properties of the ZDSCs are functions of RMS angle-spread

through azimuth angle propationality factor ( r ) and RMS azimuth angle-spread ( ) in the arrival side,

and through departure angle proportionality factor ( r ) and RMS departure angle-spread ( ) in the departure side. The dispersion parameters and are sometimes correlated with log-normal shadowing (LNS), which is important for interference calculations, handover algorithms, etc. For each set of RMS delay spread and RMS angle-spread departure, RMS angle-spread departure arrival and LNS within each channel segment, correlation between them has to be considered. These large-scale parameters are often reported in literature to have log-normal distributions.

Our framework allows for any distribution for the large-scale parameters and also introduces a modelling of the auto-correlation over the service area. This is achieved by using scenario and parameter specific transformations ( )g to transform the large-scale parameters into a domain where they can be treated as Gaussian. The mean, , cross-correlation and auto-correlation matrix ( )0R are then defined in the transformed domain. The realizations of the large-scale parameters are then obtained as

( )( )?Rg + yx,5.01 , where ( )1g is the inverse transform, and ( )05.0R is obtained as ( ) 5.05.0 0 = ER from the eigen-decomposition ( ) TEER =0 of ( )0R . The auto-correlation is achieved by generating m ( 6=m for A1, and 4=m for all other scenarios) independent Gaussian random processes,

( ) ( ) ( )[ ]T? yxyxyx m ,,, 1 = , each one with mean zero and variance one in the positions yx, where the mobiles are located. The auto-correlation of the process ( )yxc , is given by

( ) ( ){ } ( )ccc ryxyxE /exp,, 2211 = , where ( ) ( )201201 yyxxr += . However, we also define a no auto-correlation mode (NACM) in which the parameters m ,,1 are all set to zero, or equivalently, the random variable ( ) ( ) ( )[ ]T? yxyxyx m ,,, 1 = , is randomized independently for each location. The required parameters for generating the correlated large-scale parameters are thus the transformation

( )( )yxyx ,),(~ sgs = (or actually its inverse), the mean and correlation ( )0R of the transformed large-scale parameters, and the de-correlation distance parameters m ,,1 . This information is available in Table 3.1 to Table 3.5. Table 3.1 lists the distribution function for each modelled parameter in each scenario. For normally distributed random variables the original and transformed variable is identical, except for the delay-spread in scenario B3, where the transformation is a multiplication with a factor 910 (for numerical reasons). For parameters of log-Gumbel and log-Logistic distribution, the transformation (and their inverse) are given by:

( ) ( )( )( ) ,,slog~ 10Gumbel1 FQsgs == (3.1)

( )( ) ,),~()10log(exp)~( 11 sQFsgs Gumbel == (3.2) and

( ) ( )( )( ) ,,slog~ 10Logistic1 FQsgs == , (3.3) ( )( ) ,),~()10log(exp)~( 11 sQFsgs Logistic == (3.4)

WINNER D5.4 v. 1.4

Page 19 (167)

respectively, where ( ) ,,Gumbel xF and ( ) ,,Logistic xF are the CDF of the Gumbel and Logistic distributions defined in Section 5.4.3, and ( )xQ 1 is the inverse of the CDF for Gaussian random variables i.e.

( ) dttxQx

=

2exp

2

1 2

. (3.5)

In Table 3.3, the so-called position and scale parameters for the distributions are listed, except for Scenario A1 (with 6 instead of 4 parameters) which is listed in Table 3.5. This means that if the large-scale parameter c is log-Gumbel or log-Logistic, the transformed distribution will have zero mean, and unit variance, i.e., 0=c and ( ) .10, =ccR This can be understood by noting that the mean and variance are taken into account already in the transformation. For log-normal distributions, we use the transformation

( ) ( )ssgs 10log~ == (3.6) ( ) ssgs ~1 10~ == (3.7) with the exception of shadow-fading (or sometimes called log-normal shadowing, LNS) where we use

( ) ( )ssgs 10log10~ == (3.8) ( ) ssgs ~1.01 10~ == (3.9) in order to get the transformed shadow-fading in dB scale. For a log-normal distributed parameter c , the

mean c and standard deviation ( )0,ccR are the mean and standard deviation listed in Table 3.3. For normally distributed bulk parameters no transformation is required (i.e. the transformed and

untransformed value are identical) and thus the mean and c and standard deviation ( )0,ccR are listed in Table 3.3.

In Table 3.5, the cross-correlation between the transformed parameters are listed for scenario A1, and in Table 3.2 for the other scenarios. In teRMS of ( )0R , the cross-correlation between parameters r and c is given by

( )

( ) ( )000

,,

,,

ccrr

crcrc

RR

R= . (3.10)

Thus by combining the cross-correlation and variance information, the matrix ( )0R can be derived. In Table 3.3, a correlation distance is listed for each large-scale parameter. The correlation distance is based on fitting of a single exponential ( ) /exp r to the auto-correlation function of the transformed large-scale parameter. This value is based on measurements or literature or a combination thereof. However, since the true auto-correlation actually follows the equation (*) of Section 4.1.4.1.4, i.e.

( ) ( ){ } ( )ryxyx = Rss 2211 ,,E , ( ) ( )212212 yyxxr += (3.11)

( ) ( ) ( )0exp,,exp0 T,5.01

5.0 RRR

=

m

rrdiagr

(*). (3.12)

where ( )05.0R is obtained from the eigendecomposition ( ) TEER =0 as ( ) 5.05.0 0 = ER . This means that each autocorrelation function, ( )rcc ,R , will be a mixture of the m exponentials of (*). However, they are selected in a way that the results are roughly the same as the single exponential. The values of the eigenvalue auto-correlation distances m ,,1 are listed in Table 3.4. Note that there is no one-to-one mapping between any of the lambda parameters and any of the large-scale parameters. The correlation distance is included to allow a more easy interpretation of the auto-regressive characteristics of the model.

The justification for the expression (*) is that it produces a model from which it is computationally simple to generate data, and which at the same time gives a fit to experimental auto-correlation functions which is typically equally good as the single exponential modelling.

The derivation of some of parameters m ,,1 for each scenario, and in some case also other parameters, are given in Section 5.4.13 below.

WINNER D5.4 v. 1.4

Page 20 (167)

The values and distributions were obtained from measurements at 5 GHz and from literature.

In simulations which include both LOS and NLOS mobiles, the large-scale parameters of the LOS and NLOS mobiles are modelled as independent, and thus they should be generated separately.

Table 3.1: Distribution functions of large-scale parameters.

A1 B1 B3 C1 C2 D1

LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

Delay-spread

LN LN Gumb Gumb N N LN LN LN LN LN

AoD spread

LN LN Logist Gumb N N LN LN LN LN LN

AoA spread

LN LN Logist Gumb N N LN LN LN LN LN

Shadowing LN LN LN LN LN LN LN LN LN LN LN

AoD Elevation spread

LN LN

AoA Elevation spread

LN LN

N Normal (Gaussian)

LN Log-normal, i.e., log10(Gauss)

Gumb Log-Gumbel

Logist Log-Logistic

Table 3.2: Cross-correlation between large-scale parameters.

B1 B3 C1 C2 D1 Scenarios

LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

vs 0.50 0.18 0.17 0.13 -0.29 0.3 0.4 -0.07 -0.35

vs 0.76 0.42 -0.2 0.49 0.78 0.7 0.6 0.21 0.12

vs LNS -0.45 -0.40 -0.17 0.11 -0.16 -0.3 -0.3 -0.11 0.13

vs LNS -0.50 0.01 -0.32 -0.18 0.36 -0.4 -0.6 -0.07 0.60

vs LNS -0.41 -0.65 0.17 0.34 -0.71 -0.4 -0.4 -0.71 -0.51 Cro

ss-C

orre

latio

ns

vs 0.37 0.07 0.19 0.28 -0.35 0.3 0.4 -0.49 -0.15

Note: Sign of LNS has been defined so that positive LNS means more received power at MS than predicted by PL model.

Table 3.3: Distributions parameters of large-scale parameters.

B1 B3 C1 C2 D1 Scenarios

LOS NLOS LOS NLOS LOS NLOS LOS LOS NLOS

-7.38 -7.09 26 45 -8.8 -7.26 -6.63 -7.8 -7.6

0.24 0.11 8.2 6.9 0.49 0.33 0.32 0.57 0.48

WINNER D5.4 v. 1.4

Page 21 (167)

(m) 6.0 5.0 4.5 1.82 64 40 40 64.2 36.3

0.40 1.24 26.4 38 1.14 0.53 0.93 1.22 0.96

0.23 0.20 10.5 11.7 0.12 0.36 0.22 0.21 0.45

13.2 2.4 2.2 0.62 2.0 30 50 24.8 2.7

1.4 1.6 13.1 9.5 1.61 1.67 1.72 1.52 1.52

0.12 0.19 7.6 4.5 0.20 0.3 0.14 0.18 0.27

(m) 1.6 3.2 0.83 0.61 18.2 30 50 3.5 15.1

(dB)

2.3 3.1 1.4 2.1 4.0

6.0 8 8

3.5

6.0 8.0

LN

S

LNS

(m) 9.1 5.2 4.36 6.16 23.0 50 50 40 120

Notes:

1. Values for are merely provided for information. Values of (see table below) are used in coefficient generation.

2. Scenarios C1 LOS and D1 LOS contain two shadowing std. deviations; one (top) for before and one (bottom) for after the path-loss breakpoint.

Parameters:

: Location parameter (i.e., mean in case of normal distribution)

: Scale parameter (i.e., standard deviation in case of normal distribution)

: Correlation distance of normal variable

Table 3.4: Lambda parameters.

A1 B1 B3 C1 C2 D1

LOS

NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

1 (m) 2.0 3.5 2.0 5.0 4.5 7.0 40.0 44.0 50.0 3.0 15.0

2 (m) 2.0 2.0 12.0 2.3 0.8 0.6 2.0 30.0 45.0 10.0 2.0

3 (m) 2.0 3.0 3.0 3.0 4.0 1.8 35.0 30.0 40.0 60.0 15.0

4 (m) 3.0 2.5 9.1 5.2 2.2 0.6 27.0 47.0 52.0 42.0 120.0

5 (m) 3.5 5.0

6 (m) 6.0 4.0

Table 3.5: Distribution parameters for A1 sub-scenarios.

Scenario Correlation coefficients Standard deviations

Means

Decorrelation distance (m)

LOS

1,00 0,46 0,74 -0,68 0,49 0,63 0,46 1,00 0,40 -0,05 0,77 0,38

0.27 0.31

-7.40 0.74

7.00 5.90

WINNER D5.4 v. 1.4

Page 22 (167)

0,74 0,40 1,00 -0,44 0,42 0,83 -0,68 -0,05 -0,44 1,00 -0,11 -0,28 0,49 0,77 0,42 -0,11 1,00 0,44 0,63 0,38 0,83 -0,28 0,44 1,00

0.26 3.10 0.20 0.22

1.52 0.00 0.88 0.94

2.30 6.00 1.30 3.50

NLOS 1,00 -0,10 0,31 -0,50 -0,61 -0,05

-0,10 1,00 -0,26 -0,01 0,20 -0,14 0,31 -0,26 1,00 -0,41 -0,28 -0,19 -0,50 -0,01 -0,41 1,00 0,25 0,10 -0,61 0,20 -0,28 0,25 1,00 0,45 -0,05 -0,14 -0,19 0,10 0,45 1,00

0.19 0.23 0.14 3.50 0.21 0.17

-7.60 1.30 1.57 0.00 1.06 1.10

4.20 4.90 2.50 3.40 3.20 2.60

Order of parameters: delay-spread, AoD azimuth-spread, AoA azimuth-spread, shadowing, AoD elevation-spread, AoA elevation-spread

3.1.2 Average power of ZDSC conditioned on their delays The average power of every ZDSC is calculated in delay domain as explained in Chapter 4. Two functions are required to calculate the expected power of each ZDSC conditioned on their delays. They are the power delay spectrum and the probability density function of ZDSC delays. It is shown in Chapter 4, that for the case when both ( )P and ( )f are exponential, the expected power of ZDSC depends on the value of the parameter r and the RMS delay spread of the channel segment . In order to make the average power of ZDSC varying from delay to delay and from one channel segment to another in a similar manner that is usually seen in measurement results, the shadowing randomization effect on each ZDSC is modelled. Thus, the expected power of ZDSC of each segment is obtained as:

{ } ( ) 102' 101exp nrr

EPn

= , (3.13)

where n is an i.i.d. Gaussian random variable with zero mean and standard deviation , and the delay is the normalized delay to the delay of the first arrival ZDSC. The normalized delay of the first arrival ZDSC is zero. For the case when the ZDSC delays have uniform distribution, the expected power of ZDSC of each segment is obtained as:

{ } ( ) 102' 10'exp nEPn

= (3.14)

The ZDSC delay distributions and power delay spectrum of different scenarios are presented in Table 3.6. For calculation of channels with cross-polarisation, the cross-polarisation ratio (XPR) for vertical to horizontal (XPRV) and for horizontal to vertical (XPRH) are needed (for definition see 5.4.12). The values XPRV and XPRV of different scenarios are given in Table 3.6.

Table 3.6: Formulae for calculating the ZDSC power conditioned on delay for the considered scenarios and XPRV and XPRH.

A1 B1 B3 C1 C2 D1 Scenarios LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

ZDSC Delay

distribution

Exp

Exp

Exp Uniform

(0,800ns)

Exp

(0,130ns)

Exp

(0,220ns)

Exp Exp Exp Exp

Exp

r 3.0 2.4 3.2 2.2 1.90 1.58 2.4 1.5 2.3 3.8 1.7

1r

r

1rr

1rr

1

1rr

1rr

1rr

1rr

1rr

1rr

1r

r

(dB) 3 '

nP 1010n nte

XPRV 11.4 9.7 8.6 8 0.5 0.1 7.9 3.3 7.6 6.9 7.9

WINNER D5.4 v. 1.4

Page 23 (167)

(dB) 3.4 3.5 1.8 1.8 1.07 0.69 3.3 2.5 3.4 2.3 3.5

10.4 10.0 9.5 6.9 Not avail. Not

avail. 3.7 5.7 2.3 7.2 7.5 XPRH (dB)

3.4 3.1 2.3 2.8

Not avail.

Not avail. 2.5 2.9 0.2 2.8 4.0

Notes:

1. For scenario B3, XPRH values are not available. In the channel model implementation, these values have been substituted by XPRV.

2. Distribution of XPR is log-normal, i.e., XPR = 10X/10, where X is Gaussian with standard deviation and mean .

Average powers of the ZDSC are normalized so that the total power of all ZDSCs is equal to one. Then, the normalized power of the nth ZDSC is

=

= Q

nn

nn

P

PP

1

'

'

(3.15)

where Q is the number of ZDSCs. For the case when LOS model is used, the power of the direct component is considered in the normalization such that the ratio of the direct power to the scattered power is the K-factor.

( )=

+= Q

nn

nn

PK

PP

1

'

'

1, (3.16)

1+

=k

kPD . (3.17)

The K-factor for LOS scenarios can be calculated as given in Table 3.7.

Table 3.7: K factor formulae for LOS scenarios.

Scenarios A1 B1 B3 C1 D1

K [dB] 8.7 + 0.051*d 3 0.0142d+ 6 - 0.26*d 17.1 0.021*d 3.7 + 0.019*d Distance d is in m.

It should be noted that when LOS component exists, the ZDSC will have 10+1 rays.

3.1.3 Directional distributions of ZDSCs There are two types of angle information for each ZDSC. These are the mean angle and the offset angles of each ray from the mean within each cluster. The zero mean azimuth departure (azimuth arrival) angle is the transmitter (receiver) broadside direction. The generated mean angle of departures and angle of arrivals are relative to the direct path between transmitter and receiver with respect to the broadside of transmitter or receiver, respectively. The angle definitions and references that are used in the generic channel model are the same as those presented in [3GPP SCM]. The relation between the mean azimuth-departure and the mean azimuth-arrival probability density functions of each ZDSC and their delays is generated through correlated large-scale parameters used in the corresponding density functions. We fixed the power azimuth spectrum of each ZDSC at the departure and the arrival sides assuming it as Laplacian. The RMS angle-spread of each ZDSC is fixed to one value, which may be different in departure from arrival and may vary from scenario to scenario. However, the power azimuth spectrum (PAS) and the angle-spread values ( AS ) of each ZDSC can be changed in the model if needed. The distribution parameters of the mean angle of departure (AoD) and the mean angle of arrival (AoA) the ZDSCs may vary from one scenario to another. The ZDSC azimuth-departure PAS and azimuth-arrival PAS are defined by 10 rays having predefined offset angles for Laplacian PAS from the mean angles of

WINNER D5.4 v. 1.4

Page 24 (167)

the ZDSC. The 10 rays are spaced in angle domain and have identical power. The power of each ray is Pn/10, where Pn is the average power of the nth ZDSC. The offset angle spacing depends on the value of

AS of ZDSC_D for the departure side and value of AS of ZDSC_A for the arrival side. The AS

and AS are per ZDSC and are different from the and , respectively, which are the composite angle-spread involving all ZDSC. The rays of the ZDSC have random phases. The angle distributions and power azimuth spectrum at the transmitter or receiver sides may vary from one scenario to another. Table 3.8 states the angle distributions of different scenarios that are defined in WINNER generic channel

model. The and influence in generation of the ZDSC directional information through the

parameters r and r , respectively. The location parameter for all distributions is zero and the scaling

parameters are defined by r for departure angles and by r for arrival angles. The generation of

the angle offsets from the rays from cluster mean angle depends on AS for departure side and on

AS arrival cluster. The offset angles are determined by multiplication of the angles spread per ZDSC

with basis vector of offset angles (BO), e.g., AS *BO. Table 3.9 states the number of ZDSCs and the

number of rays in each cluster as well as their AS and AS for the considered scenarios. The angles of BO vector and the calculation of the offset angles are presented in Table 3.10.

Table 3.8: Distributions of azimuth and departure angles.

A1 B1 B3 C1 C2 D1 Scenarios LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

AoD distribution Wrapped Gaussian

AoD scaling

parameter 2.0 1.2 3.4 1.1 1.9 1.3 1.4 2.3 3.2 0.8 1.2

AoA distribution Wrapped Gaussian

AoA scaling

parameter 1.7 2.1 3.6 3.6 1.5 1.6 1.8 1.8 3.2 2.2 1.3

Table 3.9: Number of ZDSCs and the number of rays in each cluster.

A1 B1 B3 C1 C2 D1 Scenarios

LOS NLOS LOS NLOS LOS NLOS LOS NLOS NLOS LOS NLOS

Number of ZDSC 16 11 8 16 15 24 15 14 20 11 10

Rays per ZDSC 10

AS (deg) 5 5 3 10 4.7 5.5 5 2 2 1.5 1.5

AS (deg) 5 5 18 22 5.4 12.5 5 10 15 3 3

Table 3.10: Offset angles Rays within a ZDSC as a function of AS , AS , AS , and AS .

Ray number Basis vector offset angles (BO) Rays offset angles

1,2 0.0742

3,4 0.2532

5,6 0.4986

7,8 0.8913

OA = AS X BO

WINNER D5.4 v. 1.4

Page 25 (167)

9,10 1.9718

OA : Offset angles, BO: Basis vector of offset angles

Rays angle offsets of different values of AS or AS in Table 3.9 can be obtained by multiplying the

angle-spread values of AS or AS times each angle of the basis vector offset angles (BO). For

example AS = 5; the offset angles of the rays within a ZDSC is OA = 5 x BO = [ 0.2226 0.7596 1.4960 2.6740 5.9154].

Elevation angle information are important for indoor environments, i.e., Scenario A1 and B3. The generic model includes elevation plane for these scenarios. The elevation plane model parameters of these two scenarios are given in Table 3.11.

Table 3.11: Elevation plane model parameters.

A1 B3 Scenarios

LOS NLOS LOS NLOS (S-dB) 0.88 1.06

(S-dB) 0.20 0.21

(m) 1.3 3.2

(S-dB) 0.94 1.10

(S-dB) 0.22 0.17

(m) 3.5 2.6

Elevation AoD distribution Wrapped Gaussian

AoD scaling parameter 1.9 1.4

Elevation AoA distribution Wrapped Gaussian

AS (deg) 3 3

AS (deg) 3 3

vs 0.46 -0.61

vs 0.74 -0.05

vs LNS -0.05 0.25

vs LNS -0.44 0.11

Cro

ss-C

orre

latio

ns

vs 0.44 0.45

3.1.4 Antenna gain In principle the channel model is antenna independent at both fixed station and mobile station. Any 2D antenna configuration and pattern can be embedded in the model. If elevation plane parameters are included, 3D antenna geometries can be embedded. For example, one can use the 3GPP antenna pattern in WINNER model. The antenna pattern that has been used in [3GPP SCM] at the BS is 3-sector antenna used for each sector. It is specified by:

( )2

3

min 12 , mdB

A A

=

, where 180 180o o< < (3.18)

WINNER D5.4 v. 1.4

Page 26 (167)

where is defined as the angle between the direction of interest and the boresight of the antenna. The 3dB is the 3dB beamwidth in degrees, and Am is the maximum attenuation. For a 3 sector scenario 3dB

is 70 degrees, Am = 20dB. However, other antenna patterns can also be used, if needed.

3.1.5 Path-loss models Path-loss models at 5 GHz for considered scenarios have been developed based on measurement results or from literature. The developed path models are presented in Table 3.12 including the shadow fading values. The path-loss models have the form as in (3.21), where d is the distance between transmitter and receiver, the fitting parameter A includes the path-loss exponent parameter and parameter B is the intercept.

( ) BdAPL += log (3.19)

Table 3.12: Path-loss models.

Scenario path loss [dB] shadow fading

standard dev.

applicability

range

LOS 18.7 log10 (d[m]) + 46.8 = 3.1 dB 3 m < d < 100 m A1

NLOS 36.8 log10 (d[m]) + 38.8 = 3.5 dB 3 m < d < 100 m

LOS 22.7 log10 (d[m])+41.0 = 2.3dB 10 m < d < 650 m

B1 NLOS 0.096 d1[m] + 65 +

(28 0.024d1[m]) log10 (d2[m]) = 3.1dB 10 m < d1 < 550 m

w/2 < d2 < 450 m *)

LOS 13.4 log10 (d[m]) + 36.9 s = 1.4 dB 5 m < d < 29 m B3

NLOS 3.2 log10 (d[m]) + 55.5 s = 2.1 dB 5 m < d < 29 m

LOS 23.8 log10 (d) + 41.6

40.0 log10 (d/dBP) + 41.6 +

23.8 log10(dBP) ****) +)

s = 4.0 dB

s = 6.0 dB,

30 m < d < dBP

dBP < d < 5 km C1

NLOS 40.2 log10 (d[m]) + 27.7 **) = 8 dB 50 m < d < 5 km

C2 NLOS 35.0 log10 (d[m]) +38.4 ***) = 8 dB 50 m < d < 5 km

LOS 21.5 log10 (d[m]) + 44.6

40.0 log10 (d/dBP) + 44.6 +

21.5 log10 (dBP) ****) +)

= 3.5dB

= 6.0dB

30 m < d < dBP

dBP < d < 10 km D1

NLOS 25.1 log10 (d[m]) + 55.8 = 8.0dB 30 m < d < 10 km *) w is LOS street width, d1 is distance along main street, d2 is distance along perpendicular street. **) Validity beyond 1 km not confirmed by measurement data. ***) Validity beyond 2 kms not confirmed by measurement data. ****) dBP is the break-point distance: dBP = 4 hBS hMS / ?, where hBS is antenna height at BS, hMS is antenna height at MS, and ? is the wavelength. Validity beyond dBP not confirmed by measurement data. +) BS antenna heights in the measurements: C1 LOS: 11.7 m, D1: 19 25 m.

3.1.6 Probability of line of sight System level simulations require the probability of line of sight for considered scenarios A1, B1, B3, C1, C2, and D1. They are given as follows:

WINNER D5.4 v. 1.4

Page 27 (167)

3.1.6.1 Scenario A1

( )( )1 3310

1 2.5m

1 0.9 1 1.24 0.61 log ( ) 2.5m

dP

d d

= >

(3.20)

3.1.6.2 Scenario B1

( )( )( )1 3310

1 15

1 1 1.56 0.48log 15

d mP

d d m

=

> (3.21)

where 2 21 2d d d= + , and d1 and d2 are like in Table 2.9.

3.1.6.3 Scenario B3

For the big factory halls, airport and train stations:

++=

WINNER D5.4 v. 1.4

Page 35 (167)

Shadow-fading s free=3dB, brr ,

beyond =7dB, brr >

Range definition Range 1: Loss

WINNER D5.4 v. 1.4

Page 36 (167)

ZDSC #

delay [ns]

Power [dB]

AoD []

AoA [] Freq. of

one scatterer

mHz

K-factor

[dB]

MS speed N/A

1 0 -1.5 0.0 0.0 744 13.0 -1.8* -24.7**

2 5 -10.2 -71.7 70.0 -5 -20.2

3 30 -16.6 167.4 -27.5 -2872 -26.6

4 45 -19.2 -143.2 106.4 434 -29.2

5 75 -20.9 34.6 94.8 294 -30.9

6 90 -20.6 -11.2 -94.0 118 -30.6

7 105 -16.6 78.2 48.6 2576 -26.6

8 140 -16.6 129.2 -96.6 400 -26.6

9 210 -23.9 -113.2 41.7 71 -33.9

10 210 -12.0 -13.5 -83.3 3069 -22.0

11 250 -23.9 145.2 176.8 1153 -33.9

12 270 -21.0 -172.0 93.7 -772 -31.0

13 275 -17.7 93.7 -6.4 1298 -27.7

14 475 -24.6 106.5 160.3 -343 -34.6

15 595 -22.0 -67.0 -50.1 -7 -32.0

16 690 -29.2 -95.1 -149.6 -186 -39.2

17 855 -32.9 -2.0 161.5 -2288 -42.9

18 880 -32.9 66.7 68.7 26 -42.9

19 935 -28.0 160.1 41.6 -1342 -38.0

20 1245 -29.6 -21.8 142.2 -61

-

Num

ber o

f ray

s/ZD

SC =

10+

Ray

Pow

er [d

B]

-39.6

ZDSC

AS

at

MS

[]

= 2

ZDSC

AS

at

BS

[]

= 2

Com

posi

te A

S at

MS

[] =

42.8

C

ompo

site

AS

at B

S [

] = 5

0.2

* Power of dominant ray, ** Power of each other ray

+ Clusters with high K-factor will have 11 rays.

Table 3.24: Clustered delay-line model street-level to street-level range 3.

ZDSC #

delay [ns]

Power [dB]

AoD []

AoA [] Freq. of

one scatterer

mHz

K-factor

[dB]

MS speed N/A

1 0 -2.6 0.0 0.0 744 10.0 -3.0* -23.0**

2 10 -8.5 -71.7 70.0 -5 -18.5

3 90 -14.8 167.4 -27.5 -2872 -24.8

4 135 -17.5 -143.2 106.4 434 -27.5

5 230 -19.2 34.6 94.8 295 -29.2

6 275 -18.8 -11.2 -94.0 118 -28.8

7 310 -14.9 78.2 48.6 2576 -24.9

8 420 -14.9 129.2 -96.6 400 -24.9

9 630 -22.1 -113.2 41.7 71 -32.1

10 635 -10.3 -13.5 -83.3 3069 -20.3

11 745 -22.2 145.2 176.8 1153

-

Num

ber o

f ray

s/ZD

SC =

10+

Ray

Pow

er [d

B]

-32.2

ZD

SC A

S a

t M

S [

] =

2

ZDSC

AS

at

BS

[]

= 2

Com

posi

te A

S at

MS

[] =

52.3

Com

posi

te A

S at

BS

[] =

61.

42

WINNER D5.4 v. 1.4

Page 37 (167)

12 815 -19.2 -172.0 93.7 -772 -29.2

13 830 -16.0 93.7 -6.4 1298 -26.0

14 1430 -22.9 106.5 160.3 -343 -32.9

15 1790 -20.3 -67.0 -50.1 -7 -30.3

16 2075 -27.4 -95.1 -149.6 -186 -37.4

17 2570 -31.1 -2.0 161.5 -2287 -41.1

18 2635 -31.2 66.7 68.7 26 -41.2

19 2800 -26.3 160.1 41.6 -1342 -36.3

20 3740 -27.8 -21.8 142.2 -61 -37.8 * Power of dominant ray, ** Power of each other ray


3.2.5 Scenario C1

3.2.5.1 LOS

Table 3.25: Scenario C1: LOS Clustered delay line model, suburban environment.

ZDSC #

delay [ns]

Power [dB]

AoD []

AoA []

K-factor [dB]

MS speed = 50 km/h,

direction U(0o,360o )

1 0 0 0 0 10.0 -0.41* -20.4**

2 5 -3.4 -15.9 -67.9 -13.4

3 10 -2.7 9.34 -84.0 -12.7

4 15 -2.6 -29.4 -51.2 -12.6

5 20 -4.8 -6.32 -91.2 -14.8

6 25 -7.5 -20.4 -94.5 -17.5

7 30 -9.4 1.24 -22.8 -19.4

8 35 -9.7 10.3 -17.2 -19.7

9 40 -8.8 11.3 87.4 -18.8

10 45 -8.9 5.12 -73.0 -18.9

11 50 -9.4 14.1 -120 -19.4

12 70 -13.1 -18.9 -71.8 -23.1

13 90 -14.2 2.84 2.87 -24.2

14 110 -17.4 16.2 -128 -27.4

15 130 -17.3 -14.2 20.5 -27.3

16 150 -18.1 6.30 -48.2 -28.1

17 170 -17.0 -4.64 52.0 -27.0

18 190 -16.1 4.30 -43.5 -26.1

19 210 -19.4 -0.79 11.9

-

Num

ber o

f ray

s /Z

DSC

= 1

0+

Ray

Pow

er [d

B]

-29.4

ZDSC

AS

at

MS

[] =

5

ZDSC

AS

at

BS

[] =

5

Com

posi

te A

S at

MS

[] =

45.

8

Com

posi

te A

S at

BS

[] =

14.

2

* Power of dominant ray, ** Power of each other ray


WINNER D5.4 v. 1.4

Page 38 (167)

3.2.5.2 NLOS Clustered delay line model has an RMS delay spread of 62 ns, and composite angle-spread of 53 and 5 degrees in MS and BS, respectively. No K-factor is introduced for NLOS.

Table 3.26: Clustered delay-line model for Scenario C1 NLOS

ZDSC # delay [ns]

Power [dB]

AoD []

AoA []

K-factor [dB]

MS speed = 50 km/h,


1 0 0 0 0 -10

2 5 -0.6 4 35 -10.6

3 15 -1.8 -2 60 -11.8

4 25 -2.3 -6 -39 -12.3

5 60 -7.8 1 -56 -17.8

6 80 -14.0 -5 165 -24.0

7 105 -12.9 -8 -69 -22.9

8 120 -9.8 -10 -109 -29.8

9 205 -19.5 12 75 -29.5

10 240 -17.4 22 120 -27.4

11 255 -15.1 -25 138 -25.1

12 350 -18.3 10 -177 -28.3

13 380 -13.9 4 150 -23.9

14 410 -19.9 -1 179

-

Num

ber o

f ray

s /Z

DSC

= 1

0

Ray

Pow

er [d

B]

-29.9

ZD

SC A

S a

t M

S [

] =

10

ZDSC

AS

at

BS

[]

= 2

Com

posi

te A

S at

MS

[] =

53

Com

posi

te A

S at

BS

[] =

5

3.2.6 Scenario C2 Clustered delay line model has an RMS delay spread of 310 ns, and composite angle-spread of 53 and 8 degrees in MS and BS, respectively. No K-factor is introduced. The parameters are given in Table 3.27.

Table 3.27: Scenario C2: NLOS Clustered delay line model.

ZDSC # delay [ns]

Power [dB]

AoD []

AoA []

K-factor [dB]

MS speed = 50 km/h,


1 0 -0.5 0 0 -10.5

2 5 0.0 4 4 -10.0

3 135 -3.4 -3 7 -13.4

4 160 -2.8 -4 10 -12.8

5 215 -4.6 -7 21 -14.6

6 260 -0.9 8 -45 -10.9

7 385 -6.7 10 -75 -16.7

8 400 -4.5 17 65 -14.5

9 530 -9.0 -8 160 -19.0

10 540 -7.8 -8 155 -17.8

11 650 -7.4 -4 88 -17.4

12 670 -8.4 -7 80 -18.4

13 720 -11.0 -9 -90 -21.0

14 750 -9.0 -9 -105

-

Num

ber o

f ray

s /Z

DSC

= 1

0

Ray

Pow

er [d

B]

-19.0

ZD

SC A

S a

t M

S [

] =

15

ZDSC

AS

at

BS

[]

= 2

Com

posi

te A

S at

MS

[] =

53

Com

posi

te A

S at

BS

[] =

8

WINNER D5.4 v. 1.4

Page 39 (167)

15 800 -5.1 12 8 -15.1

16 945 -6.7 -17 45 -16.7

17 1035 -12.1 19 50 -22.1

18 1185 -13.2 12 -15 -23.2

19 1390 -13.7 19 -25 -23.7

20 1470 -19.8 21 100 -29.8

3.2.7 Scenario D1

3.2.7.1 LOS

Table 3.28: Scenario D1: LOS Clustered delay line model, rural environment.

ZDSC # delay [ns]

Power [dB]

AoD []

AoA []

K-factor [dB]

MS speed = 120 km/h,


1 0 0 0.0 0.0 10.9 -0.34* -21.2**

2 5 -3.4 45.7 -25.5 -13.4

3 10 -11.4 -12.7 35.6 -21.4

4 15 -16.4 20.7 54.0 -26.4

5 25 -17.8 -9.6 25.0 -27.8

6 35 -17.9 -24.8 136.9 -27.9

7 45 -18.8 -9.8 -7.6 -28.8

8 55 -19.3 -9.6 21.5 -29.3

9 65 -19.5 -21.3 -96.5 -29.5

10 75 -18.5 8.2 -26.5 -28.5

11 85 -19.0 21.9 -92.7 -29.0

12 95 -19.6 23.2 -5.0 -29.6

13 160 -18.7 28.7 -64.5

-

Num

ber o

f ray

s /Z

DSC

= 1

0+

Ray

Pow

er [d

B]

-28.7

ZDSC

AS

at

MS

[] =

1.5

ZDSC

AS

at

BS

[] =

1.5

Com

posi

te A

S at

MS

[] =

24

Com

posi

te A

S at

BS

[] =

21.

5

* Power of dominant ray, ** Power of each other ray,


3.2.7.2 NLOS

Table 3.29: Scenario D1: NLOS Clustered delay line model, rural environment.

ZDSC #

delay [ns]

Power [dB]

AoD []

AoA []

K-factor [dB]

MS speed = 120 km/h,


1 0 0 0 0 -10.0

2 5 -1.0 -14.3 -27.1 -11.0

3 10 -5.8 20.1 27.6 -15.8

4 15 -8.2 21.6 -14.3 -18.2

5 20 -9.0 14.5 15.9 -19.0

6 25 -9.5 -13.9 -28.5 -19.5

7 30 -10.1 7.06 23.7

-

Num

ber o

f ray

s /Z

DSC

=

10+

Ray

Pow

er [d

B]

-20.1 ZD

SC A

S a

t M

S [

] =

3

ZDSC

AS

at

BS

[] =

1.

5

Com

posi

te A

S at

MS

[] =

17.

9

Com

posi

te A

S at

BS

[] =

22.

4

WINNER D5.4 v. 1.4

Page 40 (167)

8 35 -10.6 -66.7 -50.4 -20.6

9 40 -11.1 9.92 50.5 -21.1

10 45 -11.6 -21.3 32.0 -21.6

11 50 -12.0 -34.9 15.7 -22.0

12 65 -13.1 -4.88 12.7 -23.1

13 80 -13.8 19.1 -7.40 -23.8

14 95 -15.3 11.6 -4.82 -25.3

15 110 -16.4 9.8 0.16 -26.4

16 125 -16.8 -13.3 31.6 -26.8

17 140 -17.9 -14.2 3.62 -27.9

18 155 -18.6 71.1 14.6 -28.6

19 170 -18.6 -20.2 27.4 -28.6

WINNER D5.4 v. 1.4

Page 41 (167)

PART II This second part contains more detailed information about our modelling

approach, our measurements and literature analysis, and the channel model

implementation.

WINNER D5.4 v. 1.4

Page 42 (167)

4. Modelling Approaches In this section, we discuss the modelling and coefficient generation approach of existing spatial channel models. Then our selected approach is presented in detail.

Apart from the generic, fully random channel model, we define a clustered delay-line model derived from our generic model by limiting the randomness (fixing the value) of certain parameters. The reduced variability aids the comparability of results based on shorter simulation times.

4.1 Generic channel modelling approach The generic channel modelling approach has been followed earlier in COST259 and in 3GPP standardization. In COST259, the approach has been followed for directional antenna channel models for smart antennas wireless applications. The COST259 was mainly for antenna array application at one end, usually the base station side. The 3GPP standardization channel model, known as the 3GPP/3GPP2 spatial channel model (SCM), was developed for MIMO approaches in third generation cellular systems. A generic channel modelling approach can be thought as a channel model framework that can be applied in different scenarios. Each scenario has scenario specific distributions and parameters. By changing the scenario specific distributions in angle and delay domains as well as the scenario specific parameters, we can have different channel models for different scenarios under the same framework of the channel model.

4.1.1 Distinction between channel models for link-level and system-level simulation Workpackage 5, as defined in the Annex, is divided into a link-level and a system-level modelling effort with task 4 representing the former and task 5 the latter part. During the evolution of our work though, we found that we had to be very careful with such a division because it turned out not to be inherently clear where to draw the line. To counter this problem, we consequently defined a set of properties for each of the two levels in our deliverable D5.2. It has turned out most practical to implement both the link-level and system-level features in one model. Here we understand the system-level channel modelling as in the SCM model [3GPP SCM]. Then it is possible to emphasize either the system-level features or the link-level features or both by selecting the parameters properly.

Our conclusion is that it can be potentially dangerous to define a certain division and separate the two channel models. Consider for example a model where shadowing is considered a higher level than delay- or angle-spread and for this reason treated independently. As a consequence, a likely conclusion drawn from low-level simulations is that angle- and delay-spread significantly improves capacity. However, if all three parameters were simulated corporately, including their cross-correlations, the solution might be completely opposite, specifically that the capacity loss from shadowing outweighs the gain from delay- and angle-spread.

In summary, we favour channel models that contain both the link-level and the system-level features defined at the same time. Hence, it depends on the application, which feature is switched on or off.

4.1.2 Comparison between deterministic and stochastic channel modeling Channel modeling can be broadly split into two areas that differ in the goal or application and the type of underlying data.

Deterministic channel modeling can be employed when detailed environment data is available. Detailed environment data means position, size and orientation of man-made objects (houses, buildings, bridges, roads, etc.) as well as natural objects (foliage or dominant plants, rocks, ground properties, etc.). The basic idea is that if the propagation environment is known to a sufficient degree, wireless propagation is a deterministic process that allows determining or predicting its characteristics at every point in space. It is also referred to as propagation prediction and is the type of modeling used for cell planning, i.e., the analysis of optimum locations for BS deployment and the prediction of the resulting coverage, capacity, and data rates. In deterministic channel modeling, channel measurements are made in the same environment for which detailed data is available and then used to optimize the match between prediction model and measurements.

Stochastic channel modeling on the other hand is based on a stochastic view of the wireless channel. Measurements are made in a large variety of locations and environments to obtain a data set with a good representation of the underlying statistical properties. Influence parameters based on the environment characteristics may be used to refine the statistical accuracy for similar environments. As such, classification is an important tool to trade off accuracy versus universality of statements.

What we aim for in WINNER is the prediction of statistical behavior of the channel. Knowledge of statistical channel parameters allows making more general statements. Especially, they allow evaluating

WINNER D5.4 v. 1.4

Page 43 (167)

the properties and usefulness of communication schemes in case of large-scale deployment. Hence, we follow the stochastic channel modeling approach in our analysis.

4.1.3 Interference modeling Interference modelling is an application subset of channel models that deserves additional consideration. Basically, communication links that contain interfering signals are to be treated just as any other link. However, in many of todays communication systems these interfering signals are not treated and processed in the same way as the desired signals and thus modelling the interfering links with full accuracy is inefficient.

A simplification of the channel modelling for the interference link is often possible but closely linked with the communication architecture. This makes it difficult for a generalized treatment in the context of channel modelling. In the following we will thus constrain ourselves to giving some possible ideas of how this can be realised. Note that these are all combined signal and channel models. The actual implementation will have to be based on the computational gain from computational simplification versus the additional programming overhead.

AWGN interference

The simplest form of interference is modelled by additive white Gaussian noise. This is sufficient for basic C/I (carrier to interference ratio) evaluations when coupled with a path loss and shadowing model. It might be extended with e.g. on-off keying (to simulate the non-stationary behaviour of actual transmit signals) or other techniques that are simple to implement.

Filtered noise

The possible wideband behaviour of an interfering signal is not reflected in the AWGN model above. An implementation using a complex SCM or WIM channel, however, might be unnecessarily complex as well because the high number of degrees of freedom does not become visible in the noise-like signal anyway. Thus we propose something along the lines of a simple, sample-spaced FIR filter with Rayleigh-fading coefficients.

Prerecorded interference A large part of the time-consuming process of generating the interfering signal is the modulation and filtering of the signal, which has to be done at chip frequency. Even if the interfering signal is detected and removed in the communication receiver (e.g., multi-user detection techniques) and thus rendering a PN generator too simple, a method of precomputing and replaying the signal might be viable. The repeating content of the signal using this technique is typically not an issue as the content of the interferer is discarded anyway.

4.1.4 Framework MIMO channel characterization, which takes into account directional characteristics at the transmitter and receiver sides, is widely known as double directional channel modelling. We separate the effective radio channel in effects from wave propagation on one hand and antenna response on the other hand to develop antenna independent MIMO channel model. By using the far-field, narrowband, discrete wave, and geometric diffraction assumption, the effect of the antennas can be reduced to the effect of field pattern and to a phase shift based on the angle of the impinging wave, its wavelength, and the geometry of the antennas. This means that any antenna configuration, orientation, and pattern of antenna elements at both ends can be inserted in the model. In multipath environment, each ray can be described by its path delay (), azimuth departure angle (), elevation departure angle (), azimuth arrival angle ( ), elevation arrival angle ( ) and complex amplitude ( ) of the wave and polarisation information matrix. The framework of the generic channel model is for all scenarios where one terminal is mobile while the other is fixed. It is based on principles of existing work presented in [3GPP SCM], [SV87], [Cor01], [GEYC], [PMF00], [Fle00], [AlPM02], and generalized to MIMO case with elevation angles at both ends. The generic model of MIMO channel for non-stationary environment can be described by channel impulse response with horizontal and vertical polarisation between antenna element s at transmitter and antenna element u at receiver as:

( )( )( )

( ) ( )( ) ( )

( )( )

( ) ( )( ) ( ) ( )( ) ( ) ( ) ( ) ( ) ( )mnmnmnmnntjxttkjxttkj

tL

n

tM

m mnmnh

uR

mnmnv

uR

hhmn

hhmn

hvmn

hvmn

vhmn

vhmn

vvmn

vvmn

T

mnmnh

sT

mnmnv

sTsu

mnuRmnmnsTmnmn

n

eee

F

F

jj

jj

F

Fth

,,,,2,,,,

)(

1

)(

1 ,,,

,,,

,,,,

,,,,

,,,

,,,,

,,,,,,,

,

,

expexp

expexp

,

final report on link level and system level channel...

Documents