Introduction to MIMO OTA Environment Simulation, Calibration, Validation, and Measurement Results Dr. Michael D. Foegelle Director of Technology Development Garth D’Abreu Director of RF Engineering
Introduction to MIMO OTA Environment
Simulation, Calibration, Validation, and
Measurement Results
Dr. Michael D. Foegelle
Director of Technology Development
Garth D’Abreu
Director of RF Engineering
Outline
The Meaning of MIMO
MIMO and the RF Environment
The Channel Emulator
Understanding the Channel Model
Implications of OTA Testing
Spatial Environment Simulation
ETS-Lindgren AMS-8700
2
Outline
Calibrating the System
System Validation
Wi-Fi and LTE Throughput Measurement Results
with the ETS-Lindgren AMS-8700 OTA
Environment Simulator
Metrics
Reference MIMO Antennas
3
MIMO stands for Multiple Input, Multiple Output and refers to the characteristics of the communication channel(s) between two devices.
In communication theory, a channel is the path by which the data gets from an input (transmitter) to an output (receiver).
For Ethernet or USB, the channel is the cable used.
For wireless, the channel includes the RF frequency bandwidth, the space between antennas, and anything that reflects RF energy from one point to the other.
Often includes antennas and cables too.
The Meaning of MIMO
4
The term MIMO is often used to represent a range of bandwidth/performance enhancing technologies that rely on multiple antennas in a wireless device.
These can be classified into several categories: “True” Spatial Multiplexing MIMO.
SIMO (Single Input, Multiple Output) technologies like beam forming and receive diversity.
While this discussion will concentrate on downlink MIMO, uplink MIMO/MISO concepts are similar.
The Meaning of MIMO
5
“True” MIMO uses multiple transmit and receive
antennas to increase the total information
bandwidth through time-space coding.
Multiple channels of communication (streams) share the
same frequency bandwidth allocation simultaneously.
The Meaning of MIMO
6
MIMOTransmitter
MIMOReceiver
1101
0010 1010 1001 0000 1100 1110 0110 0
01 101 1001 0 1 00 111 01
1
11101 111 0
1 00
SIMO technologies use the multiple (receive) antennas to improve single channel performance under edge-of-link (EOL) conditions.
Beam forming allows creating a stronger gain pattern in the direction of the desired signal while simultaneously rejecting undesired signals from other directions.
The Meaning of MIMO
7
Null (Low Gain)oriented in direction of interfering signal
DesiredSignal PathUnwanted
Interferer
Main Lobe(Highest Gain)
oriented in desiredcommunication direction
Receive diversity uses multiple antennas to overcome channel fades by using additional antenna(s) to capture additional information that may be missing from the first channel.
Includes simple switching diversity or more complicated techniques like maximal ratio combining or other combinatorial diversity techniques.
The Meaning of MIMO
8 1905 19101906 1907 1908 1909
-140
-80
-130
-120
-110
-100
-90
1905 19101906 1907 1908 1909
-140
-80
-130
-120
-110
-100
-90
1905 19101906 1907 1908 1909
-140
-80
-130
-120
-110
-100
-90
All of these multiple antenna technologies share one thing in common – their performance is a function of the environment in which they’re used.
The device adapts to its environment through embedded algorithms that change its (effective) radiation pattern.
MIMO and the RF Environment
9
Radiated Performance
Po
we
r (
dB
m)
-85
-55
-80
-75
-70
-65
-60
Y
Z
X
Azimuth = 104.6
Elevation = -25.1
Roll = -51.5
Radiated Performance
Po
we
r (
dB
m)
-80
-55
-75
-70
-65
-60
Y
Z
X
Azimuth = 104.6
Elevation = -25.1
Roll = -51.5
Radiated Performance
Po
we
r (
dB
m)
-85
-55
-80
-75
-70
-65
-60
Y
Z
X
Azimuth = 104.6
Elevation = -25.1
Roll = -51.5
Traditional TRP and TIS metrics are properties
of the mobile device only. These represent
the average performance of the device to
signals from any direction.
MIMO and the RF Environment
10
Y
Z
X
TRP
Metrics like Near Horizon Partial Radiated
Power/Sensitivity terms or Mean Effective
Gain apply simple environmental models to
fixed pattern data, but the basic behavior of
the device does not change.
MIMO and the RF Environment
11
Y
Z
X
TRP
26.3 dBm
Y
Z
XY
Z
X
NHPRP +/-45° (Pi/4)
25.2 dBm
NHPRP +/-30° Pi/ 6)
23.7 dBm
(
For MIMO technologies, performance is a
function of the system and cannot be restricted to
the mobile device.
Individual device performance can only be
evaluated or compared in a given environment.
This implies the need for environment simulation.
MIMO and the RF Environment
12
Environment #1 Environment #2
A channel emulator is typically used for
conducted testing of MIMO radios.
The channel emulator simulates the wireless
channel between transmit and receive radios
using a channel model.
Channel models simulate not only a given
environment, but also properties of the base
station and mobile device including antenna
patterns, antenna separation, and angles of
departure/arrival (AOD/AOA).
The Channel Emulator
13
The Channel Emulator
A typical RF channel emulator consists of a
number of VSA receivers and VSG transmitters
connected to a DSP modeling core that
introduces delay spreads, fading, etc. at
baseband.
14
Receiver(VSA)
Transmitter(VSG)
DSPModeling
Core
Receiver(VSA)
Transmitter(VSG)
Receiver(VSA)
Transmitter(VSG)
The Channel Emulator
The ideal channel emulator routes multiple inputs
to multiple outputs after applying appropriate
modeled delay spreads, fading, etc.
15
Transmitter 1
Receiver1
Transmitter2
Receiver2
ChannelEmulation
Understanding the Channel Model
In the real world, various objects in the
environment cause reflections of the transmitted
signal that are seen at the receiver.
16
Reflecting Objectsin Environment
Propagation Ray Paths
Transmitter
Receiver
Understanding the Channel Model
Signal paths are often classified as Line-of-Sight
(LOS) and Non-Line-of-Sight (NLOS).
17
Transmitter
Receiver
NLOS
LOS
NLOS
Understanding the Channel Model
Each path has a different length (propagation
delay).
18
Transmitter
Receiver
B
A
C
D
A
B
C
D
Understanding the Channel Model
Plotting the signal strength vs. time gives a
power delay profile (PDP) for the model.
19
A
B
C
D
Time
Sig
nal
Str
en
gth
Power Delay Profile
A
B
C
D
Understanding the Channel Model
The individual times of arrival are called taps,
drawing from the concept of a tapped delay line.
20
Time
Sig
na
l S
tre
ng
th
Power Delay Profile
A
B
C
D
Taps
Understanding the Channel Model
Reflecting objects typically don’t just cause one
reflected signal.
Instead, scatterers produce a cluster of
reflections with slightly different delays and
varied magnitude and phase.
21
Transmitter
Receiver
“Cluster” of Reflections
Understanding the Channel Model
Each cluster produces its own unique statistical
PDP.
22
Transmitter
Receiver
“Cluster” of Reflections
Time
Sig
na
l S
tre
ng
th
Cluster PDP
Combining the cluster concept with the tap
concept produces realistic time domain profiles.
Understanding the Channel Model
23
Time
Sig
na
l S
tre
ng
th
Power Delay Profile
A
B
C
D
Taps
Now the modeled data looks a lot like real
measured time domain data acquired using a
vector network analyzer.
Understanding the Channel Model
24
Understanding the Channel Model
Motion of the transmitter, receiver, or other
objects within the environment causes Doppler
shift of the frequency.
25
Transmitter
Receiver
Understanding the Channel Model
Moving towards a wave increases its frequency,
while moving away decreases the frequency.
A moving radio results in Doppler
spread since it moves towards
some reflections and away from
others.
26
=+
=+
Spatial channel models include geometric
information about the location of scatterers, and
determine channel behavior based on angles of
departure and arrival (AOD/AOA) and the angular
spread for each cluster.
Understanding the Channel Model
27
Spatial channel models for conducted testing
also apply assumed antenna patterns for the
source and receiver.
Understanding the Channel Model
28
2x2 Channel Emulation
Two Transmitter
BSE
Two Receiver
Radio
Simulated TransmitAntenna Patterns
Simulated ReflectionClusters
Simulated ReceiveAntenna Patterns
A primary goal of OTA testing is to determine radio performance of the DUT with the actual antenna patterns, orientation, and spacing.
If this was all that was required, a combination of antenna pattern measurement and conducted channel modeling would suffice.
However, traditional OTA measurements of TRP/TIS perform simultaneous evaluation of the entire RF signal chain for a variety of reasons:
Implications of OTA Testing
29
Platform Desensitization – interference from
platform components enters radio through
attached antennas.
Implications of OTA Testing
30
GPS
WCDMA
Baseband
ProcessorWi-Fi
Backlight
Bluetooth
Antenna Another
Antenna
And Yet
Another
Antenna
One More
Antenna
Near Field Influences – including platform
structure, head, hands, body, table top, etc.
Implications of OTA Testing
31
Mismatch and other Interaction Factors – performance of a radio into a matched 50 Ohm load may not be the same as that into a mismatched or detuned antenna, resulting in non-linear behavior.
Antenna-Antenna Interactions – mutual coupling of antennas may not be accounted for properly in pattern tests.
Cable Effects – currents on feed cables can alter the radiation pattern, especially for small DUTs.
Implications of OTA Testing
32
MIMO relies on a complex multipath environment to provide the information necessary to reconstruct multiple source signals that have been combined into multiple receive signals.
Spatial Environment Simulation
33
Reflecting Objectsin Environment
Propagation Ray Paths
MIMOTransmitter MIMO
Receiver
Spatial Environment Simulation
The goal of the OTA Environment Simulator is to
place the DUT in a controlled, isolated near field
environment and then simulate everything
outside that region.
Reflecting Objectsin Environment
Propagation Ray Paths
MIMOTransmitter MIMO
Receiver
34
Spatial Environment Simulation
The goal of the OTA Environment Simulator is to
place the DUT in a controlled, isolated near field
environment and then simulate everything
outside that region.
35
Spatial Environment Simulation
The goal of the OTA Environment Simulator is to
place the DUT in a controlled, isolated near field
environment and then simulate everything
outside that region.
36
Spatial Environment Simulation
From inside the bubble, everything looks the
same, even though everything outside the bubble
is simulated.
37
Spatial Environment Simulation
Practical limitations may result in a low resolution
picture of the environment.
Using active spatial channel emulation provides
motion simulation, etc.
38
Spatial Environment Simulation
We may also only care about a portion of the
environment.
E.g. Most reflections cluster near the horizon.
39
Spatial Environment Simulation
For comparison, using a reverberation chamber
averages out the spatial picture. The same
(statistical) signal comes from all directions.
40
Spatial Environment Simulation
The alternate two antenna method proposed is a
less accurate representation of the real
environment.
41
Spatial Environment Simulation
The two stage method uses antenna pattern data
applied to a conducted channel emulation model.
42
Spatial Environment Simulation
Example: Typical Multi-Path Power Delay Profile
from a Real World Environment
43
Spatial Environment Simulation
Using a fully anechoic chamber to isolate the DUT, a
matrix of antennas arrayed around the DUT can be
used to produce different angles of arrival (AOA).
DUT
Path 1 (LOS)AOA = 0
Path 2 AOA ~135°
Path 3 AOA ~225°
Path 4 AOA ~45°
44
A spatial channel emulator (a channel emulator
with modified channel models) simulates the
desired external environment between BSE and
DUT.
Spatial Environment Simulation
45
DUT
MIMOTester
SpatialChannelEmulator
Evaluation of SIMO functions like beam forming
and receive diversity likely require only
rudimentary environment simulation.
Sufficient to simulate only basic directional effects and
spatial fading.
While there are a variety of simplistic ways to
create an external environment containing delay
spread, fading, and even repeatable reflection
“taps”, they may be insufficient for proper
evaluation of MIMO performance.
Spatial Environment Simulation
46
Spatial channel models include clusters of
scatterers with each tap having an angular spread
as well as a delay spread.
Spatial Environment Simulation
47
The angular spread of a given cluster is
simulated by feeding multiple antennas with an
appropriate statistical distribution of the source
signal.
Designing the OTA Environment Simulator
DUT
48
Converting a conducted channel model to an
OTA channel model:
Conducted model simulates TX and RX antennas.
Spatial Environment Simulation
2x2 Channel Emulation
Two Transmitter
BSE
Two Receiver
Radio
Simulated TransmitAntenna Patterns
Simulated ReflectionClusters
Simulated ReceiveAntenna Patterns
49
Conducted channel model:
Ray paths from reflections in simulated environment are
collected at each simulated receive antenna.
Spatial Environment Simulation
2x2 Channel Emulation
Two Transmitter
BSE
Two Receiver
Radio
Simulated Ray PathsBetween TX and RX Antennas
50
Converting a conducted channel model to an
OTA channel model:
Clusters produced different angles of arrival (AOA)
Spatial Environment Simulation
2x2 Channel Emulation
Two Transmitter
BSE
Two Receiver
Radio
Directions of Received Signals(Angles of Arrival)
51
Converting a conducted channel model to an
OTA channel model:
Grouping AOAs, we can remove virtual RX antennas.
Spatial Environment Simulation
2x2 Channel Emulation
Two Transmitter
BSE
Two Receiver
Radio
Region around Simulated DUT
52
OTA channel model:
2xN channel emulator used to feed N antennas for AOA
simulation around DUT with real antennas.
Spatial Environment Simulation
DUT withIntegrated DualReceivers and
Antennas
2xN Environment Simulation
Two Transmitter
BSE
53
Spatial Environment Simulation
Ideally, the sphere around the DUT would define a
perfect boundary condition that exactly reproduces
the desired field distribution inside the test region.
Practicality and physical limitations impose
restrictions that create a less than ideal environment
simulation.
The chosen number of antenna positions limits the
available range of “Real” propagation directions.
Splitting clusters across discrete antennas does not
produce true plane wave behavior in test region.
Results in an interference pattern with wave-like distribution in
center of test region.
54
Spatial Environment Simulation
Discretization results in an interference pattern with
wave-like distribution in center of test region.
Quality depends on angular spacing and number of antennas
used to create interference pattern.
Plane Wave Source at 15°
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.05
0
0.05
0.1
Simulated 15° Source using Plane Waves at 0° and 45°
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.05
0
0.05
0.1
55
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Plane Wave Illumination with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
56
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 10° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
57
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 15° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
58
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 20° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
59
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 30° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
60
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 45° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
61
Spatial Environment Simulation
Effect of angular resolution on simulated AOA.
Coherence Region for Antennas at 60° Spacing with 10 cm Wavelength
Ele
ctr
ic F
ield
Y
(m)
X (m)
-0.002
0.002
-0.001
0
0.001
-0.125 0.125-0.1 -0.05 0 0.05 0.1
-0.125
0.125
-0.1
-0.075
-0.05
-0.025
0
0.025
0.05
0.075
0.1
62
Spatial Environment Simulation
A perfect spherical boundary condition produces
perfect spherical symmetry.
63 E
lec
tric
Fie
ld
(V/m
)
Y
(m)
X (m)
0
0.00011
1e-05
2e-05
3e-05
4e-05
5e-05
6e-05
7e-05
8e-05
9e-05
0.0001
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Using only eight antennas at 45° spacing produces a
uniform test volume that’s only about a wavelength.
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
3.5e-05
2e-06
4e-06
6e-06
8e-06
1e-05
1.2e-05
1.4e-05
1.6e-05
1.8e-05
2e-05
2.2e-05
2.4e-05
2.6e-05
2.8e-05
3e-05
3.2e-05
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Spatial Environment Simulation
64
Using 24 antennas at 15° spacing produces a
much larger uniform test volume.
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
6e-05
1e-05
2e-05
3e-05
4e-05
5e-05
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Spatial Environment Simulation
65
Spatial Environment Simulation
Radial fall-off from traditional antennas in close proximity to DUT does not behave like reflections from distant objects (i.e. non-plane-wave behavior).
Relative Signal Strength for Point Source at 1.0 m Distance
Re
lati
ve
Po
we
r (
dB
)
Y
(m)
X (m)
-2.5
2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-0.25 0.25-0.2 -0.1 0 0.1 0.2
-0.25
0.25
-0.2
-0.1
0
0.1
0.2
Relative Signal Strength for Point Source at 3.0 m Distance
Re
lati
ve
Po
we
r (
dB
)
Y
(m)
X (m)
-2.5
2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-0.25 0.25-0.2 -0.1 0 0.1 0.2
-0.25
0.25
-0.2
-0.1
0
0.1
0.2
66
Standardization of MIMO OTA Testing
CTIA, 3GPP, COST all looking at MIMO OTA.
WiMAX Forum is also interested.
CTIA MIMO Anechoic Chamber subgroup
(MACSG) has been merged with Reverb
Chamber subgroup (RCSG) to create the new
MIMO OTA subgroup (MOSG)
This has slowed the pace of CTIA development
similar to 3GPP as more approaches are
proposed to MOSG.
67
Standardization of MIMO OTA Testing
3GPP finished its second round robin to
evaluate methodologies.
Round robin originally planned to prove
reproducibility of MIMO anechoic method.
Was expanded to add comparison between all
methodologies.
Usefulness of the results is limited due to some
limitations in the process.
68
ETS-Lindgren Intellectual Property
We published first papers on this topic in 2006, after applying for multiple patents.
20080056340 – “Systems and methods for over the air performance testing of wireless devices with multiple antennas”
Anechoic array MIMO OTA concept using channel emulator, delay lines, etc.
20080305754 – “Systems and methods for over-the-air testing of wireless systems”
Covers anechoic, reverb, lossy reverb, delay lines, etc. as well as combinations thereof.
69
ETS-Lindgren Intellectual Property
Patent #7,965,986 “Systems and methods for
over-the-air testing of wireless systems” has
been granted based on 20080305754.
70
AMS-8700 Environment Simulator
The MIMO OTA Environment Simulation System
is referred to as the
AMS-8700 series.
The baseline AMS-8700
has eight dual polarized
antennas and one 8-ch
channel emulator.
The mounting ring allows
different spacing to support
single cluster & distributed configurations.
71
AMS-8700 Environment Simulator
The baseline provides only eight active
elements, switchable between vertical & horiz.
72
AMS-8700 Environment Simulator
A base station with throughput
testing options is used for MIMO.
A VNA with multiple channels can
be used for evaluating correlation
of embedded MIMO antennas.
A MAPS is provided for 3-D tests.
An additional SISO-only antenna
can be added for TRP/TIS.
Other antenna configuration/test
options are available.
73
AMS-8700 Environment Simulator
EB’s was the first to market with an OTA channel
emulator solution.
They include an OTA modeling tool for creating
OTA spatial channel models.
74
AMS-8700 Environment Simulator
Additional antennas can be added (up to the
available space on the ring) by adding the
required number of
channel emulators.
Minor incremental
cost to the chamber
but multiplies the
instrumentation cost.
75
AMS-8700 Environment Simulator
An AMS-8900 can be combined with an AMS-
8700 to allow high speed APM and TRP/TIS.
Chamber cost is a fraction of overall system
cost.
User must weigh
value of doing
APM/TRP/TIS
vs. having
inactive channel
emulator(s).
76
EMQuest EMQ-108 MIMO OTA Testing
An optional EMQ-108 MIMO OTA expansion
module has been added to EMQuest.
Option adds support for channel emulators as
variable gain devices.
Includes calibration/validation tests for spatial
channel emulation.
Includes special vector APM post processing for
calculating antenna envelope correlation.
Will be sold as a separate option as well.
77
EMQuest EMQ-108 MIMO OTA Testing
Requirements of MIMO OTA introduce a higher
level of complexity to system and test
automation.
Interactions between BSE/VSA, channel emulator, and
amplifiers.
Uplink port isolation.
Validation tests produce huge data sets.
An adequate level of software control is required
to automate the calibration and test routines.
78
Calibrating the System
A typical RF channel emulator consists of a
number of VSA receivers and VSG transmitters
connected to a DSP modeling core that
introduces delay spreads, fading, etc. at
baseband.
79
Receiver(VSA)
Transmitter(VSG)
DSPModeling
Core
Receiver(VSA)
Transmitter(VSG)
Receiver(VSA)
Transmitter(VSG)
Calibrating the System
The ideal channel emulator routes multiple inputs
to multiple outputs after applying appropriate
modeled delay spreads, fading, etc.
80
Transmitter 1
Receiver1
Transmitter2
Receiver2
ChannelEmulation
Calibrating the System
In a real system, there are external path losses
that must be accounted for.
For an OTA system, this includes cable losses,
antenna gains, and range path losses.
External amplifiers are also typically required
81
ChannelEmulator
Receiver1
Receiver2
Transmitter 1
Transmitter2
Total Emulated Channel
Calibrating the System
For input calibration, the requirement is that
G1 1 + G1 2 = G2 1 + G2 2 = GX 1 + GX 2
so that PA = PB = PX, when PTX is applied to each
input cable.
82
Channel Emulator
Receiver1
Receiver2
Transmitter 1
Transmitter2
Total Emulated Channel
Net Input Path Loss Net Output Path Loss
A
B
C
D
G1 1 G1 2
G2 1 G2 2
G1 3 G1 4
G2 3 G2 4
GA-C
GB-D
GB-C
GA-D
Channel Gain
IN1
IN2
OUT1
OUT2
Calibrating the System
Similarly, for output calibration, requirement is
that
G1 3 + G1 4 = G2 3 + G2 4 = GX 3 + GX 4
so that net losses to test volume are equal.
83
Channel Emulator
Receiver1
Receiver2
Transmitter 1
Transmitter2
Total Emulated Channel
Net Input Path Loss Net Output Path Loss
A
B
C
D
G1 1 G1 2
G2 1 G2 2
G1 3 G1 4
G2 3 G2 4
GA-C
GB-D
GB-C
GA-D
Channel Gain
IN1
IN2
OUT1
OUT2
Calibrating the System
Simple channel model with four equivalent inputs
(red) and eight resulting outputs (blue).
84
-35
-30
-25
-20
-15
-10
-5
0
1 2 3 4 5 6 7 8
Po
we
r (d
Bm
)
Port Number
Simple Channel Model
Input
Output
Calibrating the System
Result of input and output path losses applied to
channel model.
85
-60
-50
-40
-30
-20
-10
0
1 2 3 4 5 6 7 8
Po
we
r (d
Bm
)
Port Number
Effect of Input and Output Cables on Channel Model
Input
Output
Net Output
Calibrating the System
Correcting for relative input losses (purple)
flattens inputs to channel model. Net output is
still wrong.
86
-60
-50
-40
-30
-20
-10
0
10
1 2 3 4 5 6 7 8
Po
we
r (d
Bm
)
Port Number
Applying Input Gain to Offset Input Cable Differences
Input
Input Gain (dB)
Channel Input
Output
Net Output
Calibrating the System
Correcting the relative output levels reproduces
the desired impact of clusters within the
environment.
87
-70
-60
-50
-40
-30
-20
-10
0
1 2 3 4 5 6 7 8
Po
we
r (d
Bm
)
Port Number
Applying Output Gain to Offset Output Cable Differences
Input
Channel Input
Channel Output
Output Gain (dB)
Output
Net Output
Calibrating the System
Finally, to predict the average power level in the
center of the test volume, the average path loss
must be calculated, including the loss (gain) of
the channel model.
This requires detailed knowledge of the channel
model gains, as well as assumptions about the
relative input levels of each input path for MIMO.
Application of a SIMO output calibration to a
MIMO model requires understanding/adjustment
of source power definition.
88
Calibrating the System
where GChannel is the gain of a single channel path.
89
Channel Emulator
Receiver1
Receiver2
Transmitter 1
Transmitter2
Total Emulated Channel
Net Input Path Loss Net Output Path Loss
A
B
C
D
G1 1 G1 2
G2 1 G2 2
G1 3 G1 4
G2 3 G2 4
GA-C
GB-D
GB-C
GA-D
Channel Gain
IN1
IN2
OUT1
OUT2
N
j
GGG
iiijjjiChannelGGg
1
214310log10
Calibrating the System
However, when properly calibrated
and likewise
so that
which can be simplified to:
90
N
j
gG
inputioutputjiChannelgg
1
10log10
413143 GGGGg jjoutput
21 iiinput GGg
N
j
G
outputinputijiChannelggg
1
10log10
Calibrating the System
This simplification shows that both the input and
output gain of the system can be easily altered to
address output levels and modulation headroom
of the source, and to vary total path loss in the
system.
Such changes must be accounted for in determining the
power in the test volume.
When changing channel models, the total
channel model gain must be recalculated.
Changing the number of active inputs and
outputs also alters the gain.
91
Calibrating the System
When evaluating the gain of a MIMO system
where the same power, PTX, is applied to each
input cable, the total gain to the test volume can
be given by:
Note that this sum includes the array gain of the
multiple transmitters. For average power gain,
92
M
i
g
totalig
1
10log10
M
i
g
averagei
Mg
1
101
log10
System Validation
A wide range of tests are possible to evaluate:
Chamber/RF system quality
Channel emulation quality
Calibration quality
Combined system performance
Many tests are more interesting for research
purposes rather than system validation
It’s important to separate out tests that provide
useful system information vs. component level
performance.
E.g. correlation or field distribution vs. Doppler spread
93
System Validation
A re-configurable ETS-Lindgren AMS-8700 MIMO
OTA system with 16 dual polarized antennas and
two 8-output channel emulators was evaluated.
94
A linear positioner and turntable were used to
map a 1 m diameter disc in the center of the test
volume at 1 cm by 1 degree (0.87 cm at edge)
resolution.
System Validation
95
System Validation
Spatial Field Mapping is used to compare the
measured environment to a theoretical model.
96
Calculated Far-field Field Structure, 45 Degree Spacing
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
3.2e-05
1.1e-06
2.2e-06
3.3e-06
4.4e-06
5.5e-06
6.6e-06
7.7e-06
8.8e-06
9.9e-06
1.1e-05
1.21e-05
1.32e-05
1.43e-05
1.54e-05
1.65e-05
1.76e-05
1.87e-05
1.98e-05
2.09e-05
2.2e-05
2.31e-05
2.42e-05
2.53e-05
2.64e-05
2.75e-05
2.86e-05
2.97e-05
3.08e-05
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Calculated Far-Field Field Structure with High Resolution Boundary Condition
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
7.83e-05
2.7e-06
5.4e-06
8.1e-06
1.08e-05
1.35e-05
1.62e-05
1.89e-05
2.16e-05
2.43e-05
2.7e-05
2.97e-05
3.24e-05
3.51e-05
3.78e-05
4.05e-05
4.32e-05
4.59e-05
4.86e-05
5.13e-05
5.4e-05
5.67e-05
5.94e-05
6.21e-05
6.48e-05
6.75e-05
7.02e-05
7.29e-05
7.56e-05
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Ideal Free-Space or Continuous Boundary Condition Interference Pattern from Eight Evenly Spaced Plane Waves
Calculated Field Structure at 2.0 m Radius, 45 Degree Spacing
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
0.016
0.0006
0.0012
0.0018
0.0024
0.003
0.0036
0.0042
0.0048
0.0054
0.006
0.0066
0.0072
0.0078
0.0084
0.009
0.0096
0.0102
0.0108
0.0114
0.012
0.0126
0.0132
0.0138
0.0144
0.015
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
Measured Field Structure, 45 Degree Spacing, Vertically Polarized
Ma
gn
itu
de
X
(cm
)
Angle (°)
0
0.0192
0.0007
0.0014
0.0021
0.0028
0.0035
0.0042
0.0049
0.0056
0.0063
0.007
0.0077
0.0084
0.0091
0.0098
0.0105
0.0112
0.0119
0.0126
0.0133
0.014
0.0147
0.0154
0.0161
0.0168
0.0175
0.0182
Scale: 5/div
Min: 0
Max: 50
0
180
30
210
60
240
90270
120
300
150
330
System Validation
Modeling a 2 m range length instead of a plane
wave shows excellent correlation.
97
Measured Interference Pattern from Eight Antennas, r = 2 m Calculated Interference Pattern from Eight Antennas , r = 2 m
Comparing a single cut through the test volume.
System Validation
98
Comparison of Measured Field Structure to Theory for 8 Antenna Array (45° Spacing)
Re
lati
ve
Fie
ld L
ev
el
X (cm)
-50 50-40 -30 -20 -10 0 10 20 30 40
0
1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Measured Field Theoretical Field Ideal Free-Space Field
Measured Field Structure, 22.5 Degree Spacing
Ma
gn
itu
de
X
(cm
)
Angle (°)
0
7.04
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
Scale: 5/div
Min: 0
Max: 50
0
180
30
210
60
240
90270
120
300
150
330
Increasing the resolution of the boundary condition
from 8 to 16 antennas increases usable test volume.
Calculated Field Structure at 2.0 m Radius, 22.5 Degree Spacing
Ele
ctr
ic F
ield
(V
/m)
Y
(m)
X (m)
0
0.0226
0.0008
0.0016
0.0024
0.0032
0.004
0.0048
0.0056
0.0064
0.0072
0.008
0.0088
0.0096
0.0104
0.0112
0.012
0.0128
0.0136
0.0144
0.0152
0.016
0.0168
0.0176
0.0184
0.0192
0.02
0.0208
0.0216
-0.5 0.5-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
-0.5
0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
System Validation
99
Measured Interference Pattern from 16 Antennas, r = 2 m Calculated Interference Pattern from 16 Antennas , r = 2 m
System Validation
Spatial Correlation evaluates field structure and
channel model behavior.
Move one dipole through test volume and evaluate
correlation vs. separation.
Requires replay of channel model at each position.
Single cluster behavior most straightforward to evaluate.
100
SingleCluster
1 m slice through test volume on 1 cm steps
Spatial Correlation evaluates RF system +
emulation.
System Validation
101
Spatial Correlation for 8 Antenna (45° Spacing) Configuration
Co
rre
lati
on
X (cm)
-50 50-40 -30 -20 -10 0 10 20 30 40
0.2
1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Both tests show similar system performance
results. Comparison of Spatial Correlation and Field Structure for 22.5° Resolution Configuration
X (cm)
-50 50-40 -30 -20 -10 0 10 20 30 40
0
1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Spatial Correlation Field Structure Free-Space Field Structure
System Validation
102
System Validation
Channel Model Pattern – Using a narrow beam
antenna, the generated angular spread profile can
be mapped.
Works well as a quick verification for single cluster
Not as agile for more complicated models
Antenna-by-Antenna Mapping – Measure channel
frequency response of each antenna across
statistically large set of IR steps and TD
transform.
Evaluates PDP and AS of channel model.
Can be numerically compared to summation of all antennas
active (requires valid phase calibration).
103
Throughput Measurement Results
Unlike traditional TRP/TIS tests, which provide edge
of link performance metrics, MIMO performance is all
about high bandwidth with large SNRs.
The corresponding metric for measuring bandwidth
is throughput, and the equivalent evaluation would be
to determine when the throughput begins to fall off.
Initial tests were performed with 802.11n devices
supporting 2x2 MIMO, to prove the capabilities of the
system and methodology.
Now that LTE communication testers are available, it
is possible to show the first LTE MIMO OTA results.
104
Wi-Fi Throughput Measurement Results
A re-configurable MIMO OTA system was
installed in ETS-Lindgren’s Cedar Park facility for
research and development of test requirements.
Eight dual polarized antenna elements were
mounted on adjustable fixtures and arranged
around a DUT positioning turntable.
The Elektrobit Propsim F8 channel emulator was
used to provide the spatial channel emulation
required for the OTA environment simulation.
Eight 30 dB gain power amplifiers drive eight
vertical antenna elements.
105
Wi-Fi Throughput Measurement Results
An 802.11n 2x2 MIMO Wireless Router with
removable, adjustable external antennas was
chosen as the DUT.
A matching NIC was used as the downlink
source.
Directly cabled conducted tests
were used to verify MIMO
operation with appropriately
higher throughput compared to
SIMO/SISO cabled
configurations.
106
Wi-Fi Throughput Measurement Results
Conducted tests of throughput vs. attenuation were performed with Propsim F8 using circulators/isolators to provide a single return uplink.
Direct single tap models were used to replicate cabled results.
Several 2x2 MIMO models suitable for OTA testing were evaluated to determine typical MIMO performance.
Modified SCME Urban Micro w/ 3 km/h fading & zero delay spread.
Modified TGn-C w/ AOD/AOA based on SCME
Modified TGn-C w/ low TX correlation (10 wavelength sep.)
107
Wi-Fi Throughput Measurement Results
Using standard 20 MHz 802.11 channels,
conducted tests show maximum SIMO
throughput around 25 MBPS, with MIMO
performance around 40-45 MBPS with typical
channel models.
Initial OTA tests with stock antennas using low
correlation TGn-C OTA model produces similar
results but shows angular dependence of MIMO
performance while SIMO (diversity) performance
remains uniform.
108
Wi-Fi Throughput Measurement Results
Throughput vs. Total Path Loss
Th
rou
gh
pu
t (
Mb
ps
)
Attenuation (dB)
30 8035 40 45 50 55 60 65 70 75
10
50
15
20
25
30
35
40
45
0° 30° 60° 90° 120° 150° 180° 210° 240°
270° 300° 330° 360° SIMO TX1 SIMO TX2
109
Wi-Fi Throughput Measurement Results
Throughput vs. Total Path Loss
Th
rou
gh
pu
t (
Mb
ps
)
Attenuation (dB)
30 8035 40 45 50 55 60 65 70 75
10
50
15
20
25
30
35
40
45
0° 30° 60° 90° 120° 150° 180° 210° 240°
270° 300° 330° 360° SIMO TX1 SIMO TX2
MIMO
Operating
Region
110
Wi-Fi Throughput Measurement Results
Throughput vs. Total Path Loss
Th
rou
gh
pu
t (
Mb
ps
)
Attenuation (dB)
30 8035 40 45 50 55 60 65 70 75
10
50
15
20
25
30
35
40
45
0° 30° 60° 90° 120° 150° 180° 210° 240°
270° 300° 330° 360° SIMO TX1 SIMO TX2
SIMO
Operation
111
Wi-Fi Throughput Measurement Results
Throughput vs. Total Path Loss
Th
rou
gh
pu
t (
Mb
ps
)
Attenuation (dB)
30 8035 40 45 50 55 60 65 70 75
10
50
15
20
25
30
35
40
45
0° 30° 60° 90° 120° 150° 180° 210° 240°
270° 300° 330° 360° SIMO TX1 SIMO TX2
TX
Beam- Forming
Region
112
LTE Throughput Measurement Results
LTE USB modem on test pedestal in middle of chamber
113
LTE Throughput Measurement Results
114
Throughput vs. Power vs. Orientation, SCME Urban Micro, 16 QAM LTE DUT
Th
rou
gh
pu
t (
Mb
ps
)
Power (dBm)
-79 -60-78 -76 -74 -72 -70 -68 -66 -64 -62
8
24
10
12
14
16
18
20
22
0° 45° 90° 135° 180° 225° 270° 315°
20 Mbps Throughput Sensitivity Pattern, 16 QAM LTE DUT
Po
we
r (
-dB
m)
Angle (°)
Scale: 1/div
Min: 71
Max: 79
0
180
30
210
60
240
90 270
120
300
150
330
6.2.1 - SCME Urban Micro, Avg = -74.0 dBm 6.2.2 - Modified SCME Urban Micro, Avg = -74.4 dBm
6.2.3 - SCME Urban Macro, Avg = -74.5 dBm 6.2.4 - Modified WINNER2, Avg = -73.1 dBm
LTE Throughput Measurement Results
115
40 Mbps Throughput Sensitivity Pattern, 64 QAM LTE DUT
Po
we
r (
-dB
m)
Angle (°)
Scale: 1/div
Min: 56
Max: 67
0
180
30
210
60
240
90 270
120
300
150
330
6.2.1 - SCME Urban Micro, Avg = -61.1 dBm 6.2.2 - Modified SCME Urban Micro, Avg = -61.0 dBm
6.2.3 - SCME Urban Macro, Avg = -58.8 dBm 6.2.4 - Modified WINNER2, Avg = -60.6 dBm
LTE Throughput Measurement Results
116
Metrics
The data acquired thus far can be evaluated in a
number of ways to define different metrics for
MIMO performance.
Removing the position axis produces average
throughput vs. power (attenuation) curves.
This could be done as a post processing step,
but if position (pattern) information is not
needed, average throughput performance can be
determined by moving DUT continuously
through simulated environment.
117
Metrics
118
Average Azimuthal Throughput vs. Total Path Loss
Th
rou
gh
pu
t (M
bp
s)
Attenuation (dB)
30 7535 40 45 50 55 60 65 70
5
45
10
15
20
25
30
35
40
TGn-C Low Correlation TGn-C Normal Correlation
Metrics
This test can be further reduced by choosing to
determine average throughput performance at a
given field level (no power level search).
E.g. At an attenuation value of 50 dB, this DUT has an
average throughput of 36 Mbps for the low correlation
TGn-C model and 30 Mbps for the normal correlation
TGn-C model.
This is similar to many conformance tests with a
simple pass/fail result, and assumes a minimum
expected network capability.
119
Metrics
By retaining angular information, or by
measuring throughput over short dwell times as
the DUT moves, peak throughput performance
can be determined.
This metric may have limited usefulness, but
does illustrate a slightly different reaction to the
two models.
120
Metrics
121
Peak Azimuthal Throughput vs. Total Path Loss
Th
rou
gh
pu
t (M
bp
s)
Attenuation (dB)
30 7535 40 45 50 55 60 65 70
5
50
10
15
20
25
30
35
40
45
TGn-C Low Correlation TGn-C Normal Correlation
Metrics
By retaining throughput vs. attenuation or using
a throughput vs. attenuation search mode, one
can define a “MIMO Sensitivity” where
throughput falls below a certain target.
This can be defined in two ways, with varying
test time requirements.
Average power required to produce the target throughput
at each angle (integrated TIS pattern)
Power required to produce desired average throughput as
device is rotated through all angles
122
Metrics
123
(Linear) Average Attenuation vs. Throughput
Av
era
ge
Att
en
ua
tio
n (
dB
)
Throughput (Mbps)
10 3515 20 25 30
40
80
45
50
55
60
65
70
75
TGn-C Low Correlation TGn-C Normal Correlation
Metrics
While the statistics of these two metrics are
slightly different and provide slightly different
results, both provide considerably more
information on the DUT, offering an “edge of
MIMO link” performance indicator.
Such information can be used to rank products
and influence improvements, while the previous
pass/fail options only offer basic acceptability
criteria.
124
“Good” device antenna patterns.
Reference MIMO Antenna
125 Images courtesy of Motorola Mobility
“Nominal” device antenna patterns.
Reference MIMO Antenna
126 Images courtesy of Motorola Mobility
“Bad” device antenna patterns.
Reference MIMO Antenna
127 Images courtesy of Motorola Mobility
Tests were performed in an AMS-8700 MIMO OTA
system using eight vertically polarized elements
evenly spaced every 45 degrees.
LTE Throughput Measurement Results
128
DUT
MIMOTester
SpatialChannelEmulator
LTE Throughput Measurement Results
129
SCME Urban Micro (TR 37.976 Section 6.2.1), 30 km/h
Av
era
ge
Th
rou
gh
pu
t (
Mb
ps
)
EPRE (dBm)
-115 -90-110 -105 -100 -95
15
24
16
17
18
19
20
21
22
23
Conducted 1:1 Constant Tap Conducted Fading Model "Good" MIMO Antenna "Nominal" MIMO Antenna
"Bad" MIMO Antenna
LTE Throughput Measurement Results
130
" Optimum Drop" Model, 30 km/h
Av
era
ge
Th
rou
gh
pu
t (
Mb
ps
)
EPRE (dBm)
-115 -90-110 -105 -100 -95
15
24
16
17
18
19
20
21
22
23
Conducted 1:1 Constant Tap Conducted Fading Model "Good" MIMO Antenna "Nominal" MIMO Antenna
"Bad" MIMO Antenna
Reference MIMO Antenna
With a measured <10 dB delta between a “good”
and “bad” antenna, this method is good at
differentiating between devices within the
expected measurement uncertainty.
Orientation of the device and polarization are
expected to have an effect. This device was
optimally oriented in the test system.
Plans to repeat test with a dual polarized setup
and possibly different orientations will follow.
131
Conclusion
Extensive efforts are underway to standardize on
a next generation platform for wireless testing.
The ability to perform realistic RF environment
simulation and evaluate end user metrics in real-
world scenarios is an invaluable resource to
wireless technology developers.
Detailed calibration and validation methods are
required to ensure the validity of measured data.
While a throughput related metric is the logical
choice, the industry must still choose the desired
target metric (e.g. throughput sensitivity).
132
Thank You!
133
QUESTIONS? Dr. Michael D. Foegelle
Director of Technology
Development
+1-512-531-6444
Garth D’Abreu
Director of RF Engineering
Garth.dabreuets-lindgren.com
+1-512-531-6438