wireless insite 5g

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Wireless InSite – 5G


Contents1. Wireless InSite overview

• Channel Modeler vs. Planning Tools

• GIS data and CAD

• Antennas and MIMO Arrays

• UI and API workflow

2. 3GPP and 5G/mmWave overview• 3GPP Deployment Scenarios

• mmWave effects

3. Diffuse Scattering

4. Massive MIMO Beamforming

5. Wireless InSite 5G upcoming features• Version 3.3 and 3.4 roadmap

6. 28 GHz Boston example with beamforming• SU-MIMO vs MU-MIMO beamforming

7. 39 GHz San Jose coverage example• LTE Throughput and capacity

8. High Performance Computing and RXAPI example• Outdoor Rosslyn, VA API example

9. Antenna Array Tool Concept


Wireless InSite overview


Shifts in 5G planning

Remcom seeks to address two significant shifts brought about

through the use of 5G, MIMO, and mmWave technology

• Network planning has moved from the macro to the microscale

and is far more complex due to network densification and the use

of beamforming protocols

• Antenna array design and channel characterization are

increasingly integrated disciplines, and are no longer handled in

isolation.


Channel Modeler vs. Planning ToolsFeature/Specifications Wireless InSite Channel Modeler Traditional Planning Tools

Valid Frequencies Full3D ray tracing from 100 MHz to 100 GHz Ray tracing under 6 GHz, statistical mmWave

Material DefinitionsMaterials defined for each building or object facet,

detailed control, library available with diffuse scatteringBroad assumptions, defined per-structure

GIS and CAD dataFully detailed models, fine control over objects like

window frames, humans, and office/street furnitureWorks best with simplified models

Spaces Considered Indoor, outdoor, indoor-to-outdoor and vice versa Indoor or outdoor, only

Ray-tracingFull 3D with specular reflections, transmissions, and

diffractionsUTD/GTD with specular reflections

Diffuse Scattering

Lambertian and Degli-Esposti models, may be

considered at any ray interaction, user-defined on a per-

material basis

Not considered

Beamforming

H-Matrices available for beamforming postprocess,

single-user MIMO beamforming in release 3.3, multi-user

MIMO beamforming in release 3.4

Not supported

Motion support

Support for transceivers on routes and trajectories with

Doppler effects, moving scatterers can be

accommodated via script

Not supported


Wireless InSite Workflow

CAD - Terrain and City with material

assignments

Antennas with Waveform along

with Tx/Rx

Run locally or cluster

Process GeometryRay Tracing using

UTD

E-field calculation and output generation

UI or API Workflow

Calculation Engine

Workflow


Wireless InSite UI Workflow (detailed)• CAD

• Terrain: Obtained from USGS or any geodata provider• DTED, DEM format (and others)

• City: Obtained from any geodata provider• SHP, COLLADA, DXF format (and others)

• Objects: Obtained from any CAD source or geodata provider• COLLADA, DXF format (and others)

• Materials• Database of materials available in Wireless InSite

• Users can create PEC, dielectrics (single or multiple layers) and import materials (transmission and reflection coefficients)

• Antennas, Waveforms and Transceivers• Database of analytic antennas available in Wireless InSite

• SISO or MIMO antennas can be assigned to Tx/Rx

• Import near-field simulated antennas

• Sinusoid waveform from 100 MHz to 100 GHz can be specified

• Tx/Rx can be placed via UI or geo-located file

• Study Area• User can specify manual simulation area size

• User can specify propagation model settings (reflections, transmissions, diffractions; atmospheric properties; foliage model etc)

• Calculation can be partitioned locally or externally (in beta)


Wireless InSite API Workflow (detailed)• CAD

• Terrain: Obtained from USGS or any geodata provider• Import TIF or convert to .ter (WI native file)

• City: Obtained from any geodata provider• Import SHP and COLLADA

• Convert to .city (WI native file)

• Objects: Obtained from any CAD source or geodata provider• Import SHP and COLLADA

• Convert to .object (WI native file)

• Materials• Materials are assigned on a per feature basis

• Antennas, Waveforms and Transceivers• Database of analytic antennas available in Wireless InSite

• SISO antennas can be assigned to Tx/Rx

• Use near-field simulated antennas

• Sinusoid waveform from 100 MHz to 100 GHz can be specified

• Tx/Rx can be placed in geo-located file

• Study Area• User can specify manual simulation area size

• User can specify propagation model settings (reflections, transmissions, diffractions; atmospheric properties; foliage model etc)

• Calculation can be run on the GPU (for X3D model)


GIS data sourcing• Vector data of buildings and footprints with

heights

• 3D objects, floorplans, models of buildings, with or without complex roofs

• Terrain ground usage - clutter and elevation data

• Vector data of vegetation and data with tree trunks

• Material property definitions for the various elements in the scene, like glass, concrete, soil, vegetation, etc. may be assigned on a per-facet basis


Inputs – Terrain, Buildings, Foliage, Materials

• Terrain

• Import most any popular terrain formats,

like DEM, DTED, and GDAL

• Buildings

• Simulation-ready high, medium, low

resolution data available

• Foliage

• include as vegetation vector data or

detailed trees with trunks and correct

profiles

• Materials

• material assignments per individual CAD facet


Inputs – Antennas & MIMO Arrays

• Import per-element or aggregate MIMO array patterns from an antenna modeler like XFdtd

• CST and HFSS converters available

• Coupling effects are handled in the near field tool

• MIMO arrays allow for unique patterns, orientations, magnitude and phase for each array element

• Built in library of analytical antennas


Summary of Wireless InSite

Control

• Import high facets CAD models

• Define or import antennas

• Account for frequencies from 100 MHz to 100 GHz

5G Modeling

• Massive MIMO modeling

• Beamforming (in development)

• Export MIMO outputs for post processing or channel emulation

Accuracy

• Validated, deterministic models

• Solid understanding of runtime/accuracy trade-offs

• Capture diffuse scattering effects

Speed

• GPU-enabled X3D model

• Ray-tracing optimizations

• Job queuing and partitioning


3GPP and 5G/mmWave overview


Planning for 5G mmWave Systems

• Millimeter Wave bands offer significant new spectrum for 5G; however, this comes with many challenges

• Millimeter waves suffer higher path loss, larger penetration losses from structures and foliage, and more complex scattering that impacts polarization and phase

• Recent innovations with MIMO, including Massive MIMO, increase the complexity of channel modeling for 5G even further

• These changes bring significant complexity for planning

• Wireless InSite offers simulation approaches to handle many of these new complexities, and demonstrate some examples of simulation of Massive MIMO beamforming

• Our intent is to present our approach, get feedback, and continue to make improvements to support 5G and the wireless community


Opportunities for mmWave in 5G

• 3GPP outlines 3 general use cases for 5G and 12 deployment scenarios• 5 allow for mm wave (~30 GHz):

• Indoor, dense urban, urban macro

• High-speed trains, satellite extensions to terrestrial

• Most of the 3GPP 5G deployment scenarios can be simulated with Wireless InSite

3GPP TR 38.913 [1] 5G Use Cases:1. eMBB (enhanced Mobile Broadband)2. mMTC (massive Machine Type Comm)3. URLLC (Ultra-Reliable/Low Latency Comm)

3GPP 5G Deployment Scenarios:1. Indoor hotspot (includes mmWave)2. Dense urban (includes mmWave)3. Rural4. Urban macro (includes mmWave)5. High speed (e.g., trains) (includes mmWave)6. Extreme long distance coverage/low density7. Urban coverage for massive connection8. Highway Scenario9. Urban Grid for Connected Car10. Commercial Air to Ground scenario11. Light aircraft scenario12. Satellite extension to Terrestrial (includes mmWave)


3GPP Deployment Scenario Descriptions

Scenario Frequency Bands Antennas Scenario Layout & Environments

1. Indoor Hot Spots Around 4, 30, 70 GHz

BS up to 256

UE’s up to 32 (mmWave)

Indoor office w/20m inter-site distance (ISD); 10

UE / TRxP, 3km/h

2. Dense Urban Around 4, 30, 70 GHz

BS up to 256


Outdoor small cells w/200m ISD; 10 UE’s per BS,

80% indoor (3km/h), 20% outdoor (30km/h)

3. Rural Around 0.7, 2, 4 GHz

BS up to 256@4 GHz

UE’s up to 8

Outdoor macrocells, 0.5-1.7km; 10 UE’s per BS,

50/50% indoor (3km/h)/outdoor veh.(120 km/h)

4. Urban Macro Around 2, 4, 30 GHz

BS up to 256


Outdoor macro w/500m ISD; 10 UE’s per BS, 80%

indoor (3km/h), 20% outdoor (30km/h)

5. High-Speed

BS to relay: 4, 30 GHz

Relay to UE: 30, 70 GHz

BS & relays up to 256


BS’s, RRH lining tracks, 580-1700m spacing. Up to

300 UE’s / cell; on trains (500 km/h)

6. Extreme Long distance,

low density

< 3 GHz, with priority < 1

GHz Not specified

Isolated macrocell, possibly wilderness near road.

Up to 100km range; up to 160 km/h

7. Urban coverage for

massive connection 700, 2100 MHz

BS: 2-8 antennas

UE’s: 1

Macro: ISD 0.5-1.7km. 20% outdoor, possibly in-

car (100 km/h); 80% indoor devs (3km/h).


3GPP Deployment Scenario Descriptions

Scenario Frequency Bands Antennas Scenario Layout & Environments

8. Highway Scenario <6GHz BS up to 256; UE to 8

Macrocells 500m, RSU 50-100m; UE’s in cars

(100-300km/h)

9. Urban Grid for Connected

Car <6GHz BS up to 256; UE to 8

Macrocells 0.5-1.7km, RSU 50-100m; UE’s in cars

(15-120km/h urban; 70-250km/h freeway)

10. Commercial Air-to-Ground <4GHz

Not specified 15km altitude; 100km BS to relay on plane (up to

1000km/h). Relay to UE’s up to 80m.

11. Light aircraft scenario <4GHz Not specified

3km altitude; 100km BS to UE (up to 6 users).

Speed up to 370km/h.

12. Satellite extension to

terrestrial

Access: 1.5, 2 GHz

Backhaul: 20-50 GHz

Not specified Satellite-to-ground; LEO, MEO, GEO orbits.

Fixed, portable, or mobile UE’s.


Wireless InSite – Urban, Indoor and Rural

Urban, Indoor, & Rural Scenarios (1, 2, 3, 4, 7)• Wireless InSite is well-suited to urban (outdoor, out-to-indoor) and indoor, as

well as more rural environments• mmWave absorption, exact path corrections for phase and polarization, and

diffuse scattering support higher frequencies (up to 100 GHz)• MIMO capability can handle large arrays (e.g., 256 elements)• Ranges (small cells up to 1.7km) easily within typical scenarios sizes• Emphasis of models are on coverage (received power or SINR) and peak

throughput

Caveats:• Mobility not yet implemented in the tool (moving objects available via script)

• Rural coverage might require breaking the problem up into smaller areas


Wireless InSite – High Speed

High-Speed Scenario (5)• Environments and layouts of base stations and relays straightforward

• Relays to passenger UE’s could be handled as indoor problem

• Ranges for a problem over a track segment of a few kilometers should be reasonable to handle

Caveats: • Current throughput calculations do not take effects of speed into account. However,

degraded throughput tables could be created


Wireless InSite – Vehicles

Vehicular Scenarios (8, 9)• Identical to urban or rural coverage scenarios

• Urban, terrain, and foliage modeling handle the environment

• Can support higher frequencies (in this case up to 6 GHz)

• MIMO capabilities support large array specs

• Scenario and spacing for base stations and RSU’s can be handled

Caveats: • Identical to high-speed scenario

• Also, Device-to-Device/Vehicle-to-Vehicle communications will use moving point sets (to be released) capability that allows both ends of a link to be in motion


Wireless InSite – Long range

Long-range Scenarios (6, 10, 11)• Wireless InSite’s vertical plane model extends beyond 100km range

• Terrain and foliage modeling supports environment

Caveats: for #6 with flat terrain and/or very long distances, foliage penetration model may over-estimate foliage loss, so considering future diffraction modifications for low-angle, longer range conditions

Satellite extension to terrestrial (scenario 12) – not supported• Wireless InSite does not currently have a satellite-communications model, though R&D is

beginning with plans to develop a SATCOM model by late 2019


Key Effects: mmWave Propagation Losses

• Path Loss: at 30 GHz, ~15-30dB greater than bands < 6 GHz

• Atmospheric absorption: significant at some bands, but only about 0.13dB/km at 28 GHz

• Rain attenuation: can be significant over several hundred meters

0 100 200 300 400 500 600 700 800 900 100020

40

60

80

100

120

140

Range (m)

Pa

th L

oss (

dB

)

Path Loss vs Range for MM Wave

1 GHz

10 GHz

30 GHz

100 GHz

100

101

102

10-3

10-2

10-1

100

101

102

Frequency (GHz)

Sp

ecifi

c A

tte

nu

atio

n (

dB

/km

) Atmospheric Absorption

Dry Air

H2O

Total


Key Effects: Interactions with Walls & Trees

Penetration loss from structures and trees can be severe• Refs: Weissberger [2], ITU-R p.2040[3], 3GPP TR36.873 [4]

• Diffractions around corners also drop off rapidly

• However, reflections have similar strength to lower frequencies (apart from scattering effects), so some aspects of outdoor propagation and multipath are very much the same

1 10 1000

10

20

30

40

50

60

Frequency (GHz)

Exce

ss F

olia

ge

Lo

ss (

dB

)

Foliage Loss (Weissberger Model)

5m depth (~1 tree)

10m depth (~2 trees)

25m depth (~5 trees)

1 10 1000

10

20

30

40

50

60

Frequency (GHz)

Atte

nu

atio

n fro

m R

efle

ctio

ns(d

B)

Attenuation from Reflections (ITU-R p.2040)

10cm Concrete

5cm Wood Boards

1.25cm Glass

1 10 1000

10

20

30

40

50

60

Frequency (GHz)

Wa

ll P

en

etr

atio

n L

oss (

dB

)

Wall Penetration Loss (ITU-R p.2040)

20cm ITU Concrete

3GPP Concrete

ITU Glass, 2*1/4"

3GPP glass

3GPP IRR glass

3GPP Old Bldg

3GPP New Bldg


Key Effects: Diffuse Scattering

Fields scatter in non-specular directions, partially losing both coherence and polarization

Wall

Incidentfield

(Diffuse Scattering)

(DiffuseScattering)


Diffuse Scattering


Diffuse Scattering Model

• Implemented method described by Degli-Esposti and extended with cross-polarization scattering terms

• Material properties can have Lambertian, Directive, and/or Directive with Backscatter

• Lambertian (scattering lobe normal to wall)• Scattering strongest normal to wall and falls off to sides

• Directive: scattering clusters around specular direction (forward lobe)

• Directive with Backscatter: directive with some energy diverted to backscatter direction


Wireless InSite Materials Library

Wireless InSite includes a number of predefined materials

• In support of 5G and WiFi, Remcom has added diffuse scattering values to all materials in the built in material library

• Future capability to define tabulated dielectrics vs. frequency to simplify selection

• Diffuse scattering is still an area of active research with limited measured data to feed the Degli-Esposti model

• Scattering defaults for the ITU materials were based on limited data at 1.3 & 60 GHz from [3],[4], scaling linearly with frequency to extrapolate when required

[1] ITU-R p.2040-1 “Effects of building materials and structures on radiowave propagation above

about 100 MHz,” July, 2015.

[2] ITU-R P.527-3, ”Electrical Characteristics of the Surface of the Earth,” 1992.

[3] V. Degli-Esposti, F. Fuschini, E. M.Vitucci, and G. Falciasecca, ``Measurement and modelling of

scattering from buildings,'' IEEE Trans. Antennas Propag., vol. 55, no. 1, pp. 143153, Jan. 2007.

[4] J. Pascual-García, et. Al., "On the importance of diffuse scattering model parameterization in

indoor wireless channels at mm-wave frequencies", IEEE Access, vol. 4, pp. 688-701, Feb. 2016.


Paths for Surface Integration

Actual Paths: breaks down facets, finds & verifies paths to many integration points

• Paths are used to compute diffuse scattered power and phase

Stores Aggregate Paths: Stores power-weighted average points on each facet to reduce file I/O

• Has no effect on accuracy


Model 1: Lambertian

Lambertian Scattering Pattern

Scattering centered around surface normal

Parameters:Scattering Fraction (0-1) of incident factor

field that scatters diffusely

Cross-pol Fraction (0-1) of scatteredFraction field that is cross-polarized

Wall

Incidentfield


Model 2: Directive

Directive Scattering Pattern

Scattering centered around reflection angle




Alpha Value (1-10) defines how broad forward beam will be

Wall

Incidentfield


Model 3: Directive w/Backscatter

Directive w/Backscatter

Directive scattering in forward and reverse directions




Alpha Value (1-10) defines how broad forward beam will be

Beta Value (1-10) defines how broad backscatter beam will be

Fraction (0-1) of power that scatters in forward direction (vs. backscatter)

Wall

Incidentfield


Scattering Patterns for Typical Ranges

Directive Model

Alpha typically between 1 & 10 (Wireless InSite default is 4)

Directive w/Backscatter

Beta also between 1 & 10 (default 4)

Forward fraction (Lambda) in plot is 0.75

Incidentfield

Incidentfield

Wall Wall


Massive MIMO Beamforming



• What is Massive MIMO?• Tens to hundreds of base station

antennas

• Directs beams to each UE within same frequency band

• High gain increases throughput to UEs

• Shared frequency increases overall spectral efficiency of a small cell

• Physical Size of Arrays• Spacing typically ~0.5 wavelength (0.5

cm at 30 GHz)

• Makes mm-wave well-suited for Massive MIMO



Capturing effects in simulations

• 3D ray-tracing has been used for in-situ performance assessment for years• Increasingly being used for planning for

complex urban scenes (e.g. small cells)

• Provides accurate channel data, with w/full time, angle, phase & polarization

• Challenges: • Data:

• Available at cost depending upon wireless technology

• Run time:• Sims in this presentation use Wireless InSite®

GPU-accelerated ray-tracer, with MIMO optimizations to minimize run-time cost

Propagation Paths for Channel between

1 Transmit/Receive MIMO Antenna Pair

Complex Impulse Response

Time of Arrival (s)

Re

ceiv

ed

Po

wer

(d

Bm

)



Developing 3 MIMO techniques:• Spatial Multiplexing with Singular Value

Decomposition (SVD)• Creates isolated data streams to each Rx

antenna (spatial multiplexing)

• Available in WI 3.3 release

• Max. Ratio Transmission beamforming• Generates max power to 1 Rx antenna

• Available in WI 3.3 release

• Zero-forcing beamforming (ZFBF)• Sets beamforming weights to minimize

interference to all other users in cell, placing them within local nulls

• Has been prototyped for MU-MIMO and used with Wireless InSite in several studies/papers, generating beams to specific UE’s laid out in small cells

Intended device(otherdevices)

Maximum Ratio Transmission

Intended device(otherdevices)

Zero Forcing

SVD: Multiple Streams

Antenna 1

Antenna 2


Interference and Noise

• SINR is a key measure for determining throughput of a channel

• Sources of noise and interference• Ambient RF noise

• Interference from other base stations

• For MU-MIMO, also interference from beams to other UEs

Signal

Interference + NoiseSINR =

UE 1 UE 2


Wireless InSite upcoming features


Wireless InSite – 5G planning release• Expansion of MIMO outputs (WI 3.3)

• RMS delay spread, K-factor, Angle Spread of Arrival/Departure, LOS

• MIMO Techniques (WI 3.3)• SU-MIMO: Receiver Diversity, Closed-Loop Spatial Multiplexing (SVD),

Beamforming, Tabulated Tx Precoding• Combining: Beamforming and Tx precoding can be combined with Rx

diversity techniques

• Parallel Computing (available in beta, officially in WI 3.4)• Local Partitioning• External Queuing Integration


Wireless InSite – Future 5G development• Mobility for 5G

• Ray tracing enhancements to allow for moving objects

• Moving point sets

• Placement of RCS signatures in simulation space

• Expanded Protocols

• WiFi ad and ax MIMO throughput

• Tie in with a new Array Tool (Concept)

• MU-MIMO

• Beamforming protocols

• Spatial Multiplexing

• Expansion of cluster and server farm support

• Seamless submission of simulations using a job queue manager


MIMO Techniques: Receiver Diversity

• Include support for three methods• Selection Combining: choose Rx channel with strongest SINR

• Equal Gain Combining: align Rx channel phases and sum magnitudes

• Max Ratio Combining (MRC): weighting vector adjusts magnitude and phase to optimize total SINR

• Result: 1 stream of data with SINR that is higher and/or more robust to fading• Once reduced to a single stream, process like SISO

• Generate standard power & interference outputs (e.g., received pwr, SINR)

• Use to then estimate throughput and theoretical Shannon capacity


Closed-Loop Spatial Multiplexing w/SVD

• Closed-Loop Spatial Multiplexing• Singular value decomposition (SVD)

• Converts MxN H-matrix into vector of S streams

• S=min(M,N)

• Result: multiple streams of data, each with computed SINR• Process each stream like SISO

• Sum the capacity and throughput from each stream to get total for MIMO system

• Power and interference outputs will consist of results for stream with highest SINR


MIMO Techniques: Beamforming

• Beamforming using Maximum Ratio Transmission (MRT)• Form single beam to UE

• When more than 1 UE antenna, form beam to first antenna, and can optionally combine with Rx diversity technique to further increase SINR

• Result: converts MxN H-matrix to 1 higher SINR beam• Process stream like SISO

• Generate standard power & interference outputs (e.g., received pwr, SINR)

• Use to then estimate throughput and theoretical Shannon capacity


MIMO Techniques: Tabulated Tx Precoding

• Provides arbitrary precoding matrices• Allows flexibility to support any technique with prebuilt matrices

• User can combine with Rx diversity techniques

• If > 1 set of coefficients, sample them all and choose best one (could be used to sample from fixed set of beams, for example)

• Constraints: user must choose table with number of coefficients that match # Tx antennas, AND all Tx’s must have same number of antennas

• Result: converts MxN H-matrix to 1 or more streams● Process each stream like SISO● If UE has > 1 antenna, can combine with a receiver diversity technique

(default = selection combining)


MIMO Analysis Workflow concept

Step 1: Select Tx Options

Step 2: Select Rx Options

Step 1: Select Spatial Multiplexing using SVD

Beamforming/Diversity Closed-Loop Spatial Multiplexing (SVD)


Example: 64x2 MIMO, looking at SINR

Case 1: SISO (Sector Horn)

Case 2: Beamforming (MRT-

MRC) improves SINR over

most of coverage area

Case 3: Spatial Multiplexing

(SVD) – forms 2 streams with

minimal improvement to SINR


Example: 64x2 MIMO, looking at Throughput

Case 1: SISO (Sector Horn)

Case 2: Beamforming (MRT-MRC)

achieves good throughput

over higher % of area

Case 3: Spatial Multiplexing

(SVD) - 2 streams nearly

doubles throughput in areas

where SINR is strong


28 GHz Boston throughput analysis


Introduction

• FD-MIMO is a promising new technology in 5G deployment

• This example shows how Wireless InSite® MIMO can be used to predict performance for FD-MIMO systems in an urban small cell

• Wireless InSite® MIMO provides an innovative and optimized capability for high-fidelity predictive simulation of complex channel characteristics

• We extend these results to evaluate SINR and throughput for several different beamforming methods

• The result is a tradeoff of MIMO methods and throughput for a sample planning scenario, demonstrating the value of high-fidelity simulation


Example: mmWave MIMO in Downtown Boston

• Scenario: • Augment capacity of existing macro-

cell in downtown Boston

• 3 Small cell base stations• 2 on lampposts (15m height)

• 1 (left) placed 45m up on building to propagate over trees in square

• Using new mm Wave spectrum• Frequency: 28 GHz

• Bandwidth: 100 MHz (3GPP NR, Verizon 5GTF)


Comparing Massive MIMO to SISO

• Massive MIMO Base Stations• 8x8 array of patch antennas

• ±45o Cross-Pol

• 128 total antenna elements (including cross-pol)

• Output power 5W

• Baseline (for comparison)• 120o sector horns

• User equipment (UE)• Assume small number of antennas,

effectively operating as 1 dipole (2.15dBi gain)

8x8 Patch Array, ±45o Cross-Pol

MIMO Antennas


SISO Baseline: SINR

• All base stations set up with sector horns

• Calculated SINR accounting for:• Ambient noise, -167dBm/Hz

• Interference from other base stations

• SINR map shows good coverage near LOS, but• Shadowing in side streets

• Interference at cell edges (see next slide)


SISO Baseline: Shadowing & Interference

• Inter-cell interference occurs at cell edges between base stations (shown in red)

• Shadowing by buildings in side streets (shown in blue)

(Inter-cell Interference)

(Building Shadowing)

(Inter-cell

Interference)

(Path Loss,

Foliage Loss)


SISO Throughput for 100 MHz Bandwidth

• Calculated throughput using LTE-A table (up to 256QAM), scaled up from 20 MHz to 100 MHz (5x)

• Throughput results:• High in regions near LOS

• Drops significantly in areas with interference or shadowing

• Areas of low throughput are what beamforming must address


First Case: Single-User MIMO

• Determine best that beamforming could provide to each point• Use MRT beamforming to maximize

power to each point

• Interference: power sum over 128x1 channel matrices from other base stations

• Results:• SINR map (right) shows significant

improvement in shadows and cell edges

• Throughput increases significantly as well (next slide)


Throughput: Beamforming Improvement vs. SISO


SU-MIMO increases Throughput along Route

• Significant increase to max throughput along route• Table gives mean and min values

for peak throughput at each point

• Most notable: eliminates worst dropouts along route

Peak Throughput along Route:

Mean (Mbps) Min (Mbps)

SISO 269 0

SU-MIMO 433 1420 100 200 300 400 500 600 700 800 900 1000

0

100

200

300

400

500

600

700

Max Throughput: SU-MIMO MRT vs. SISO

Range (m)

Th

rou

gh

pu

t (M

bp

s)

SISO

SU-MIMO MRT


Next Case: Multi-User MIMO• Place UEs throughout each cell

• 8 each => see red cubes

• Use zero-forcing to form beams to all UEs simultaneously• ZF minimizes inter-beam interference

• Assumption: base station has perfect Channel State Information for DL (ideal case)

• Calculate SINR & throughput to each

• CoMP: use CS/CB• One BS beamforms to mobile UE at a time

• Other 2 zero force to minimize interference as it crosses cell-edges


MU-MIMO also increases Throughput to route

• MU-MIMO results (MU-MIMO ZFBF):• Improves on SISO, but doesn’t

overcome worst dropouts

• But transmitting simultaneously to 8 UE’s-per-cell in the same band (next slide)

Peak Throughput

along Route:

Mean (Mbps) Min (Mbps)

SISO 269 0

SU-MIMO 433 142

MU-MIMO 377 0

0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

Max Throughput: MU-MIMO (ZFBF) vs. SU-MIMO & SISO

Range (m)

Th

rou

gh

pu

t (M

bp

s)

SISO

SU-MIMO MRT

MU-MIMO ZFBF


MU-MIMO: Total Peak Throughput in the Cell

• MU-MIMO uses spatial multiplexing to transmit beams simultaneously to users in the small cell

• Table shows total potential peak throughput during downlink from the sum of all beams in each cell (averaged over simulation)

• Caveats• UE’s in example mostly in or near LOS

• Average in TDD would be lower due to UL/DL, etc.

0 100 200 300 400 500 600 700 800 900 10000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Total Throughput for 8 UEs + Route in each Cell

Range (m)

To

tal T

hro

ug

hp

ut (9

UE

s)

(Mb

ps)

MU-MIMO Cell 1

MU-MIMO Cell 2

MU-MIMO Cell 3

CaseTotal Throughput (Mbps)

Cell 1 Cell 2 Cell 3

SISO 293 231 226

MU-MIMO

Beamforming (spatial

multiplexing)

3,617 3,141 2,743


Effects of Diffuse Scattering on Throughput

• At mm Wave frequencies diffuse scattering impacts propagation• Scatters waves in more directions

• Depolarizes signal

• Refined simulations to include these effects (MU-MIMO w/Diffuse Scattering)• In many parts of route, this has positive

effect, perhaps due to the additional multipath it provides

• In some, though, it decreases throughput estimate 0 100 200 300 400 500 600 700 800 900 1000

0

100

200

300

400

500

600

700

Max Throughput: MU-MIMO with Effects of Diffuse Scattering

Range (m)

Th

rou

gh

pu

t (M

bp

s)

MU-MIMO ZFBF

MU-MIMO w/Diffuse Scattering


Can JT CoMP Further Improve?

• To study how well Joint Transmission might be able to do under ideal conditions, combined antenna arrays when route was near cell edges• Near cell-edge, base stations combine

beams as if larger distributed array

• Ideal case: does not account for additional coordination between base stations

• Objective: determine how much the additional gain from joint transmission could boost SINR at cell edges

Joint Transmission


Can JT CoMP Further Improve?

• Results (MU-MIMO w/COMP):• Under ideal conditions, JT CoMP

provides modest improvement to throughput in areas with drop-outs

• Would need to simulate macro-cell over full coverage area to assess true benefit

0 100 200 300 400 500 600 700 800 900 10000

100

200

300

400

500

600

700

Max Throughput: MU-MIMO (ZFBF) vs. SU-MIMO & SISO

Range (m)

Th

rou

gh

pu

t (M

bp

s)

SISO

SU-MIMO MRT

MU-MIMO ZFBF

MU-MIMO w/CoMP

Peak Throughput

along Route:

Mean

(Mbps)

Min (Mbps)

SISO 269 0

SU-MIMO 433 142

MU-MIMO 377 0

MU-MIMO w/

JT CoMP

391 28.7


39 GHz San Jose propagation study


San Jose GIS data


Propagation study

• Includes high resolution buildings with material classification, terrain and foliage

• 30 transceivers with 37 dBi gain isotropic antenna in downtown San Jose propagating at 39 GHz

• Rx grid with ~30,000 points

• Simulation with 6 reflections and 1 diffraction run with on 44 cores and a GPU

• Runtime of ~10 minutes per Tx


Paths and CIR from TxRx12 to TxRx13


Paths and CIR from TxRx13 to TxRx12


Heat map with TxRx12 active


Heat map with TxRx13 active


Combined LTE capacity of Tx12 and Tx13


Combined LTE throughput of Tx12 and Tx13


Combined LTE capacity of all active Tx’s


Combined LTE throughput of all Tx’s


Large Simulations

• Wireless InSite has built in optimizations to handle large simulations• 64-bit GUI and solver

• Unlimited facet size

• Unlimited simulation size

• Massive MIMO arrays

• Multi-processor and GPU enabled propagation model

• Partitioning and cluster support


Simulation Size

• Geodata along with CAD features can be easily imported and setup in Wireless InSite’s 64-bit GUI

• The calculation engine is supported on both Windows and Linux

• No limit on the number of transmitters and receivers placed

• Unlimited size MIMO array can be specified as Tx/Rx


Optimizations

• Optimizations to speed up simulation run time and output generation are built in

• MIMO ray tracer will ray trace from center of array and then adjust the paths to each element in the MIMO array

• Binary output file generation for MIMO ray tracer

• Adjacent Path Generation can be used on dense Rx grids


High Performance Computing

• Ability to run simulations on one node with several CPU’s (up to 32 threads)

• Ability to run simulations on several nodes with several CPU’s (1000+ threads)

• Simulation can be split based on simulation area and Tx sets

• Simulation can be run locally or on a remote cluster (AWS/GCE)


Local Sequential Run

8 Transmitters with 36-element MIMO Tx’s, 6 reflections and 1 diffraction

Sequential Run

(Threads)

Time (mm:ss)

40 12:00

8 20:30

4 21:00

Note: Times do not include 30 seconds of geometry processing


Simple Cluster Study

Partitions (MPI ranks

– 8 threads per rank)

Times (mm:ss)

8 03:19

4 05:30

2 08:45

1 13:31

Note: Includes 40 seconds of network communication, 30 seconds of geometry processing

8 Transmitters with 36-element MIMO Tx’s, 6 reflections and 1 diffraction


Local Sequential Run

Sequential Run

(Threads)

Times

(minutes)

40 34

8 40

4 72

Note: Times do not include 10 minutes of geometry processing

2.2 sq.km city - 100 Transmitters with 36-element MIMO Tx/Rx, 6 reflections and 1 diffraction


Large Cluster Study (TBD)Partitions (MPI ranks


Times (mm:ss)

100 xx:xx

50 xx:xx

10 xx:xx

5 xx:xx

2.2 sq.km city - 100 Transmitters with 36-element MIMO Tx/Rx

Partitions (MPI ranks


Times (mm:ss)

100 xx:xx

50 xx:xx

10 xx:xx

5 xx:xx

6 reflections and 1 diffractions

2 reflections and 0 diffractions


Next Steps

• Run various MPI simulations with up to 100 Tx’s active

• Show speed up of runtime vs number of nodes

• Cost estimate of cloud services• Example: AWS p2.xlarge cluster is $0.9 per Hour per node

• To run 2000 Tx’s, 500 nodes with 16 threads per node can be used


Batch Processing using the WI API

Each Wireless InSite transmitter requires a separate simulation run. Studies like the following are best performed using batch processing leveraging the RXAPI

• Large-scale or dense transmitter scenarios – Scenes with tens, hundreds, or thousands of transmitters can be run concurrently

• Optimization studies – Varying configurations of transmitter locations throughout the scene

• Monte-carlo studies – Statistical variations of material and other parameters

• Automation – Using the command line or scripting API, geometries, Tx/Rx locations, waveforms, etc., can be run without user intervention


Simple API Example – Outdoor in Rosslyn, VA

• The following slides show the process of setting up and running a simulation using the GUI (left) and API scripts (right).

• These commands and other project attributes may be configured and run in batch using the Wireless InSite API

• Geometry

• Tx/Rx Sets

• Study Areas

• Run simulation


Rosslyn, VA Geometry

Replace the flat terrain with Rosslyn_Realistic_terrain.ter rosslyn_streets.cpp

// add the realistic rosslyn terrainremcom::rxapi::TerrainHandle terrain = remcom::rxapi::Terrain::New( );

// again, since we are using a native Wireless InSite geometry file, create a WirelessInSiteGeometryHandleremcom::rxapi::WirelessInSiteGeometryHandle terrainSource = remcom::rxapi::WirelessInSiteGeometry::New( );remcom::rxapi::RString terrainFilename( remcom::rxapi::RString( std::string( DATA_DIR ) + "Rosslyn_Realistic_Terrain.ter" ) );terrainSource->getFilename( )->getFilename( )->setValue( terrainFilename );

// assign the sourceterrain->setGeometrySource( terrainSource );

// add geometryscene->getGeometryList( )->addGeometry( city );

scene->getGeometryList( )->addGeometry( terrain );


Tx/Rx Sets

Check the control points of the Tx 2B transmitter point.

rosslyn_streets.cpp

// add transmitters/receivers// add Tx 2B from the Wireless InSite projectremcom::rxapi::PointSetHandle tx2b = remcom::rxapi::PointSet::New( );tx2b->setOutputID( 1 );

// set Tx 2B's control pointremcom::rxapi::CartesianPointHandle point = remcom::rxapi::CartesianPoint::New( );point->setX( 736.4469 );point->setY( 667.8848 );point->setZ( 10.0 );

tx2b->getControlPoints( )->setProjectedPoint(point );tx2b->setConformToTerrain( true );


Tx/Rx Sets

Check the control points of the Lynn St. receiver route. rosslyn_streets.cpp

// add the Lynn St receiver route from the WirelessInSite project

remcom::rxapi::RouteSetHandle lynnSt = remcom::rxapi::RouteSet::New( );

lynnSt->setOutputID( 6 );lynnSt->setConformToTerrain( true );

remcom::rxapi::CartesianPointHandle startPointLynnSt = remcom::rxapi::CartesianPoint::New( );

remcom::rxapi::CartesianPointHandle stopPointLynnSt = remcom::rxapi::CartesianPoint::New( );

startPointLynnSt->setX( 646.5510 );startPointLynnSt->setY( 482.6249 );startPointLynnSt->setZ( 2.0 );

stopPointLynnSt->setX( 643.4868 );stopPointLynnSt->setY( 773.2593 );stopPointLynnSt->setZ( 2.0 );

(continued...)


Tx/Rx Sets

Check the control points of the Lynn St. receiver route

rosslyn_streets.cpp

(...continued)

// we can call setProjectedPoint( ) to set the first pointin the listlynnSt->getControlPoints( )->setProjectedPoint( startPointLynnSt );

// we need to call addProjectedPoint( ) to add subsequentpointslynnSt->getControlPoints( )->addProjectedPoint( stopPointLynnSt );

// set the spacing

lynnSt->setSpacing( 5.0 );


Tx/Rx SetsCreate an X3D study area. Set the parameters to 6 reflections, 0 transmissions, and 1 diffraction to balance fidelity with runtime rosslyn_streets.cpp

// create an x3d study area

remcom::rxapi::X3DHandle x3d = remcom::rxapi::X3D::New( );

// set the model parameters to something reasonably balanced between fidelity and speed

x3d->setReflections( 6 );

x3d->setTransmissions( 0 );

x3d->setDiffractions( 1 );

x3d->setCPUThreads( 4 );

// now that we have a full scene and a model to run, create a job and hook everything together

remcom::rxapi::JobHandle job = remcom::rxapi::Job::New();

job->setScene( scene );

job->setModel( x3d );

(continued...)-


Tx/Rx SetsCreate an X3D study area. Set the parameters to 6 reflections, 0 transmissions, and 1 diffraction to balance fidelity with runtime

rosslyn_streets.cpp

(...continued)

// the x3d model uses an SQLite database to store its paths

remcom::rxapi::PathResultsDatabaseHandle database = remcom::rxapi::PathResultsDatabase::New( );

job->setPathResultsDatabase( database );

remcom::rxapi::FileDescriptionHandle databaseFilename = remcom::rxapi::FileDescription::New( );

databaseFilename->setFilename( "./rosslyn_streets.sql" );

database->setFilename( databaseFilename );

// all requirements for running an x3d calculation should now be met, so check that the job is valid and then run

if( !job->isValid( ) )

{progressReporter.reportError( job->getReasonWhyInvalid(

) );

return 1;

}


Run the simulationrosslyn_streets.cppAPI output is requested directly and can be postprocessed, saved to a file, passed to a GUI for rendering, etc.

job->execute( &progressReporter );

// once the job has finished, it will be updated with output, which can also be serialized

// remcom::rxapi::Factory::instance( ).save( job, "./rosslyn_streets_out.xml", false );

if( !(job->getOutput( )->isValid( )) ){

progressReporter.reportError( job->getOutput( )->getReasonWhyInvalid( ) );

return 1;}

// write output to the consolewriteAllChannelsToConsole( database, tx2b->getOutputID( )->getValue( ), lynnSt->getOutputID( )->getValue( ) );

writeAllChannelsToConsole( database, tx2b->getOutputID( )->getValue( ), nKentSt->getOutputID( )->getValue( ) );

Run the calculation and retrieve output. Wireless InSite writes output files which can be loaded for rendering and plotting


Wireless InSite Job Queueing

• Whether generated through the GUI or API or by a single or many users, simulations may be partitioned and queued using Wireless InSite’s WIQS

• WIQS (Wireless InSite Queueing System) paired with a job scheduler, allows users to submit simulation jobs to a cluster

• Jobs are automatically placed in the queue until the required compute resources become available

• The simulation job is run and the results generated are stored to a shared file system


Wireless InSite Job Queueing system overview

Required Components are:

• License Server (FlexLM’s lmgrd)

• Job Scheduler (e.g. SLURM, LoadLeveler, PBS, Sun GridEngine,

LSF)

• wiqs daemon (example provided by Remcom)

• Workstations running Wireless InSite User

Interface

• Compute nodes running WI Solver (running Linux, e.g.

CentOS 6)

• File Server (Linux server or NAS, several TB of capacity)File Server

LicenseServer

Job Scheduler

wiqs

Process

Workstation

Compute Node

Symbol Key:

Can be hosted on

same computer

Compute Cluster

Arrows represent data flow and direction:

low data volume (<100MB)

high data volume (100’s MB to GBs)


Array Tool Concept (in planning)

XFdtd Wireless InSite

Antenna

designBase station

antennas

Array

Tool

Antenna

Parameters

Channel

Model

Performance

Metrics


Tool for Array Processing• Target Audience:

• Device designers who use XFdtd• Device designers who use WI as a test house

• Desired initial capabilities:• 3D patterns - individual & composite antenna patterns• 2D slices - individual & composite antenna patterns• Max gain per angle for phased arrays• CDF of gain values - individual & composite antenna patterns• Histogram of gain values - individual & composite antenna patterns• ECC, MEG: tabular and vs frequency• Understand “sensitivity” of composite pattern on twiddling mag/phase per

pattern


Tool for Array Processing

• Follow-on capabilities

• Bitrates

• Throughput

• Channel models

• Phase difference

• Peak & array gain (FCC part 15 specs)

• Aperture efficiency

• Side lobe levels

• Use Cases by antenna system

• SISO

• SU-MIMO

• Phased array

• MU-MIMO w/ phased array

• Technologies

• 802.11n (2.4 GHz WiFi)

• 802.11ac (5 GHz WiFi)

• LTE, LTE-A, LTE-Pro

• WiMAX

• NB-IoT, GSM, LTE-M


Tool for Array Processing

wireless insite 5g

Documents