ESIEE_MIMO

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Concepts of 3GPP LTE9 Oct 2007Page 1

Page 1Page 1Page 1

MIMO MIA!

…or the different faces of MIMO!

Taking LTE MIMO from

Standards to Starbucks

Moray Rumney 30th April 2009




Page 2Page 2

Agenda

• Just a little MIMO theory

• MIMO in the LTE air interface

• LTE MIMO conformance testing

•Testing MIMO in the real world







Agilent LTE Book

www.agilent.com/find/ltebook

www.amazon.com

In print April 16th

The first LTE book dedicated

to design and measurement

30 Authors460 pages

Page 3




http://www.agilent.com/find/ltebook

http://www.amazon.com/

http://www.amazon.com/

http://www.agilent.com/find/ltebook




Page 4Page 4

Book overview

Chapter 1 LTE IntroductionChapter 2 Air Interface Concepts

Chapter 3 Physical Layer

Chapter 4 Upper Layer Signaling

Chapter 5 System Architecture Evolution

Chapter 6 Design and Verification Challenges

Chapter 7 Conformance Test

Chapter 8 Looking Towards 4G: LTE-Advanced







Page 5Page 5

Agenda




•Testing MIMO in the real world







Page 6Page 6

Basic channel access modes

Transmit

Antennas

Receive

Antennas

SISO

The Radio

Channel

MISO

Single Input Single Output

Multiple Input Single Output

(Transmit diversity)

Receive

Antennas

Transmit

Antennas

MIMO

The Radio

Channel

SIMO

Single Input Multiple Output

(Receive diversity)

Multiple Input Multiple Output

(Multiple data streams)

Taking LTE MIMO fromStandards to Starbucks





MIMO principles

•Transmitting multiple data streams in the same space andtime used to be called interference!

• So how does MIMO work?

1. MIMO capacity gains come from taking advantage of spatial

diversity in the radio channel

2. Depending on channel conditions and noise levels, the rank(number of simultaneous streams) can be varied

3. The performance can be optimized using precoding

• These three MIMO principles can seem complex to

understand particularly abstract mathematical descriptions• But we intuitively already know these MIMO principles in the

way they apply to our perception of audio

Page 7








Understanding MIMO spatial diversity through

Audio - Dual Stream (Stereo)

Page 9



Interference! MIMO!Interference!

For MIMO to work:• Must have at least as many receivers as transmitted streams

• Must have spatial separation at both transmit and receive antennas

• More transmitters enables beamforming in addition to MIMO

Interference!




Understanding MIMO precoding through Audio

• MIMO Precoding is a pre-emphasis

technique used to improve the separation

of the streams at the receiver due to

unhelpful coupling in the channel

• In audio systems precoding is similar to

stereo “balance” • If the receiver is not positioned directly

between the speakers the received

streams will be at different levels

• Adjusting the balance at the transmitter

can mitigate the problem

• Balancing requires feedback from the

receiver to the transmitter

Page 10



Not enough R




Understanding MIMO precoding through Audio

• The receiver could just amplify the right channel but in the

presence of noise the corrected signal would degrade:

• Precoding the transmission as L, 5R optimizes signal recovery

Page 11



L + NL, 0.2 R + NR

L + NL, R + 5*NR

L + NL, R + NR

Problem!

Solution!




Understanding MIMO Rank adaptation through

Audio

•In good radio conditions an FM stereo receiver will attempt todecode both the left and right signals (streams)

• When the noise gets too high the receiver switches to mono

and the quality improves although stereo is lost

• This is the audio equivalent of rank adaptation where the

number of streams is reduced under poor conditions

• Transmit matrix encoded FM stereo as L + R, L – R

• Receive (L + R) + N1, (L – R) + N2

• Since N1

and N2

are largely correlated, adding the two

streams (maximum ratio combining) cancels most of the

noise

Page 12






The role of channel correlation and noise in

system performance

•

In a ideal 2x2 system the potential capacity gain is 2x• The actual gain depends on how easily the receiver can

descramble the simultaneous transmissions – this depends

on the amount of unwanted correlation and noise

• In audio systems channel correlation and noise also affectsperceived stereo performance

– Spaced living room speakers - lots of correlation degrades stereo,

susceptible to external noise

–Open headphones

–zero correlation, good stereo but stillsusceptible to noise

– Closed headphones – zero correlation, minimal noise

Page 13






So what makes a good channel for MIMO?

• A perfect MIMO channel is

like the closed headphones:

channels 2 and 3 don’t exist

• By simple observation it follows that R0

= T0

and R1

= T1

• This is the case that creates double the capacity

• But suppose we create a

simple static channel like this:

• How do we know if it will

provide capacity gain?

Page 14

1 0

0 1

Channel H

0.8 0.2

0.3 -0.9

Channel H



ch1

ch4

T0

T1

R0

R1






The MIMO challenge: Recovering the signal

• So is the earlier example good or bad for MIMO?

• We can recover the original signal

• In fact any H matrix other than the unity matrix can be

resolved PROVIDED there is no external or internal noise!

• So what kinds of channels are robust to noise?

Page 16

0.8 0.2

0.3 -0.9

Channel HR0 = 0.8 T0 + 0.3 T1

R1 = 0.2 T0 - 0.9 T1

T0 = 1.15 R0 + 0.39 R1

T1 = 0.26 R0 - 1.03 R1

Giving:





Concepts of 3GPP LTE

9 Oct 2007Page 17

The MIMO challenge: Recovering the signal

• The receiver can untangle the two signals because it knows

the coupling coefficients, based on the reference signals, but

reference estimation is susceptible to noise

• But pilot estimation is susceptible to noise

• If the estimate is wrong the recovered signal is impaired

• Consider these equations for T0 from different channels:

• Errors in T0 recovery happen due to estimation errors in thecoefficients or large coefficients amplifying noise N0 and N1

• It is possible to analyze the matrix H to predict the impact of

noise on signal recovery

Page 17

T0 = 1.15 (R0 + N0) + 0.39 (R1 + N1)

T0 = 27.3 (R0 + N0) + 16.5 (R1 + N1)






9 Oct 2007Page 18

Condition Number Measures the short term

MIMO channel performance

R0 = 0.8 T0 + 0.3 T1

R1 = -0.9 T1 + 0.2 T0

0.8 0.2

0.3 -0.9

Channel H

0.8 0.3

0.2 -0.9

Channel HT

0.73 -0.11

-0.11 0.85

Channel HTH Eigenvalues

0.914

0.666

Singular values

0.957

0.815

К = Condition number

0.957 / 0.815 = 1.17

The condition number is the ratio of the singular values of HHT

The dB value of К approximates the increase in SNR required

to recover the signal


Moray Rumney 30th April 2009Page 18Page 18Page 18




9 Oct 2007Page 19

MIMO needs better SNR than SISO

High К increases SNR requirements further

The extra SNR required to achieve the same recovered signal

quality as SISO rises as the condition number rises






9 Oct 2007Page 20

Ped. A Channel Condition Number vs. Freq.

Page 20

Condition number and channel response across 10 MHz, 10 ms

0 dB






9 Oct 2007Page 21

Impact of condition number frequency

dependency

• The previous example of how the condition number varies

across the channel during one 10 ms frame and how the

pattern varies a few frames later depending on speed

• This variability is both a challenge and an opportunity

• OFDMA systems can transmit at different frequencies within

the channel to target that part of the channel offering the

best MIMO gains

• CDMA systems cannot do this and have to accept the

average performance across the channel

Page 21






9 Oct 2007Page 22

Antenna influence on performance

• The dynamic condition number example did not isolate

effects from different components, including the antenna

• In real life, the instantaneous channel matrix H is made up

from the interaction of three components:

• The static 3D antenna pattern of the transmitter

• The dynamic multipath and Doppler characteristics of the radio

channel

• The static 3D antenna pattern of the receiver

• The overall antenna contribution is the product of the

transmit and receive antennas known as the channelcorrelation matrix

Page 22






9 Oct 2007Page 23

Antenna correlation

Page 23

• The correlation between antennas is a primarily a function of

distance and polarization

• For non polarized antennas the correlation decreases with

larger separation in the y axis - usually expressed in terms of

wavelength λ






9 Oct 2007Page 24

Examples of low and high antenna correlation

Page 24

Spaced non polarized:

High correlation

Compound spaced and

cross polarized:Low correlation






9 Oct 2007Page 25

Antenna correlation by type

Page 25



AS =

Azimuth

Spread




9 Oct 2007Page 26

Generating the overall channel correlation

matrix

Page 26

Transmit antenna correlation Receive antenna correlation

The correlation matrix R s

is the Kronecker product R BS

R MS

The α and β terms are complex and will vary by frequency






9 Oct 2007Page 27

Example channel correlation matrices

Page 27

Cross polarized, UE (0, 90) BS (-45, 45), -8dB XPR ratio

Cross polarized, UE (-10, 80) BS (-30, 60), -8dB XPR ratio

Channels

balanced

Diagonal = 1

Channelsunbalanced

Not ideal






9 Oct 2007Page 28

Computing the instantaneous channel

Page 28

The complex instantaneous channel coefficients are obtained

by applying each path of the desired fading profile to eachchannel of the correlation matrix

Ch 1 Ch 3Ch 2 Ch 4

Ch 1

Ch 3

Ch 2

Ch 4

The received signals and condition number are dynamic

in both the time and frequency domains according to the

chosen fading profile



ch1

ch4

T0

T1

R0

R1








9 Oct 2007Page 31Page 31Page 31

Agenda




• Testing MIMO in the real world






9 Oct 2007Page 32

LTE downlink transmission modes3GPP TS 36.213 subclause 7.1

LTE has seven different downlink transmission modes:

1.Single-antenna port; port 0 SISO

2.Transmit diversity MISO

3.Open-loop spatial multiplexing MIMO – no precoding

4.Closed-loop spatial multiplexing MIMO - precoding5.Multi-user MIMO MIMO -separate UE

6.Closed-loop Rank=1 precoding MISO - beamsteering

7.Single-antenna port; port 5 MISO – beamsteering

Each mode is suited to different channel and noise conditions

Page 32






9 Oct 2007Page 33

The LTE MIMO toolset

• The LTE standard recognizes the complexity of the MIMO

channel and has developed a very flexible air interface

• OFDMA allows for frequency-selective scheduling with 180

kHz and 1 ms granularity (one resource block)

• Comprehensive channel state information

• Channel Quality Indicator (CQI) –

• Precoding Matrix Indicator (PMI) – codebook based

• Rank Indication (RI)

• Subband reporting for CQI & PMI, RI is wideband only

• Highly configurable reporting mechanisms to account for different scenarios

• The UE can select what subbands to report on

Page 33






9 Oct 2007Page 34

CQI definition3GPP TS 36.213 Table 7.2.3-1

CQI index modulation code rate x 1024 efficiency

0 out of range1 QPSK 78 0.1523

2 QPSK 120 0.2344

3 QPSK 193 0.3770

4 QPSK 308 0.6016

5 QPSK 449 0.8770

6 QPSK 602 1.1758

7 16QAM 378 1.4766

8 16QAM 490 1.9141

9 16QAM 616 2.4063

10 64QAM 466 2.7305

11 64QAM 567 3.3223

12 64QAM 666 3.9023

13 64QAM 772 4.523414 64QAM 873 5.1152

15 64QAM 948 5.5547

Page 34

For each CQI reporting

period the UE is requiredto return the highest CQI

index that would have

resulted in an error

probability of less than

10% for a single transportblock transmitted using

the reported modulation

and code rate.

Subband CQI reporting

can be configured downto the resource block level






9 Oct 2007Page 35

PMI definition3GPP TS 36.211 Table 6.3.4.2.3-1

Page 35

For single stream

transmission theprecoding produces

beamsteering

For the 4 layer case

there are 16 entries

Subband PMI reporting

can be configured down

to the resource block

level






9 Oct 2007Page 36Page 36Page 36

Agenda




• Testing MIMO in the real world






9 Oct 2007Page 37

LTE MIMO conformance testing

• The performance requirements for LTE are based on a

number of simplifications to real world operation

• This often involves a modular approach of doing open loop

testing of parts of the functionality rather than a more end-to-

end approach

• This is a bit like measuring engine performance and other

components rather than going for a test drive or a real track

• The modular approach is useful and separates the test

equipment from the DUT but does not tell the whole story

• The consequence is that conformance test results cannot beeasily mapped to real life conditions to predict typical

performance

Page 37






9 Oct 2007Page 38

MIMO conformance testing vs. real world

Page 38

Attribute Conformance testing Real world operation

Correlation matrixHigh, medium and zero -not linked to reference

antenna design

Real correlation based on

actual antenna pattern

Fading channel Extended PA, VA, TU Channels with dynamic taps

Adaptive Modulation &

coding

Off – UE becomes fading

channel discriminator

On – coding aims for constant

symbol to noise at UE receiver

CQI, PMI, RI Separate open loop tests Included as part of throughput

Cell-edge Interference

signal

Static wideband

Gaussian

Narrowband frequency-

selective based on loading

Live antenna testingDeveloping open loop

Over The Air proposals

Closed loop Real loading due

to body/hand effects

Scheduling None, Single UEMultiple UE, real scheduler with

frequency selectivity base on

subband CQI/PMI

Transmission mode FixedVariable based on prevailing

conditions





O




9 Oct 2007Page 40

Testing MIMO in the real world

• Most of the simplifications in conformance testing can be

overcome with alternative test methods to get closer to real

world performance

• We will now look at a few of the possibilities

Page 40





PXB t l l ti b d f




9 Oct 2007Page 42

PXB creates real correlation based on reference

antenna designs

Page 42

Rx antenna pattern, omni, 3 sector or 6 sector

Rx antenna #1 location

and polarization

Rx antenna #2 location

and polarization



Fl ibl A t C fi ti d C l ti




9 Oct 2007Page 43

Flexible Antenna Configuration and Correlation

In this example, thereare 6 paths, each with

complex cross

coupling coefficients

Path 1

Path 6

Path 2



PXB t i bl f di i l ti i l di




9 Oct 2007Page 44

PXB customizable fading simulation including

dynamic channel taps



Fl ibl MIMO t t i S t V




9 Oct 2007Page 45

Flexible MIMO test using SystemVue

• TD-LTE or LTE-FDD MIMO Baseband data is

generated by SystemVue and sent to PXB

• Flexible Fading applied by PXB• Two phase locked ESGs/MXGs driven by PXB

generate Receiver test signals for the DUT

• Two MXAs capture received signals from DUT

output and send to SystemVue

• SystemVue demodulates and decodes MIMO

signals to measure receiver performance

2xE4438C Signal Gen2xN9020A Signal Analyzer

N5106A PXB

SystemVue

DUT



M d li MIMO t lk i S t V

http://www.home.agilent.com/agilent/product.jspx?cc=US&lc=eng&nid=-536906746.536910861.00&imageindex=1

http://www.home.agilent.com/agilent/product.jspx?cc=US&lc=eng&nid=-536906746.536910861.00&imageindex=1

http://images.google.com/imgres?imgurl=http://icd.el.utwente.nl/rflab/pictures/E4438C-lg.jpg&imgrefurl=http://icd.el.utwente.nl/rflab/instruments/&usg=__5S_yyhpGhX_pSXj_XQEOa7I9n5g=&h=248&w=600&sz=44&hl=zh-TW&start=1&sig2=yW9LtJj8klak-e1ab_E5dQ&um=1&tbnid=_1XUy9ZVp7BbeM:&tbnh=56&tbnw=135&ei=1ISXSeG8LZWm6wOws93vCA&prev=/images?q=E4438C&um=1&hl=zh-TW&rls=com.microsoft:*:IE-SearchBox&rlz=1I7GGLJ&sa=N

http://images.google.com/imgres?imgurl=http://icd.el.utwente.nl/rflab/pictures/E4438C-lg.jpg&imgrefurl=http://icd.el.utwente.nl/rflab/instruments/&usg=__5S_yyhpGhX_pSXj_XQEOa7I9n5g=&h=248&w=600&sz=44&hl=zh-TW&start=1&sig2=yW9LtJj8klak-e1ab_E5dQ&um=1&tbnid=_1XUy9ZVp7BbeM:&tbnh=56&tbnw=135&ei=1ISXSeG8LZWm6wOws93vCA&prev=/images?q=E4438C&um=1&hl=zh-TW&rls=com.microsoft:*:IE-SearchBox&rlz=1I7GGLJ&sa=N

http://images.google.com.hk/imgres?imgurl=http://cp.home.agilent.com/upload/cmc_upload/N5106A_175x103.jpg&imgrefurl=http://www.home.agilent.com/agilent/product.jspx?pn=N5106A&NEWCCLC=TWcht&usg=__EkS4nw6gwNgRxToGuuFf8c2i9DA=&h=103&w=175&sz=36&hl=zh-TW&start=12&sig2=i7Wh1_aGs8XAi0X1XuTITg&um=1&tbnid=HwFuU5EGpjdHYM:&tbnh=59&tbnw=100&ei=D4WXSdWWAonPkAW55uGwCw&prev=/images?q=N5106A&um=1&hl=zh-TW&rlz=1T4GGLJ_zh-TWHK304HK305&sa=N




9 Oct 2007Page 46

Modeling MIMO crosstalk in SystemVue

Specify LO Phase Noise

dBc/Hz @ Freq. Offset

Specify 1dB

Comp. Pt.



M i i t f t lk d h i




9 Oct 2007Page 47

Measuring impact of crosstalk and phase noise

on demodulated MIMO streams

-29dB Tx0 / Rx1

QPSK 64 QAM

-29dB Tx0 / Rx1

• These measurement were made using the MIMO features of

the Agilent 89601A Vector Signal Analyzer which fully

integrates with the SystemVue design software



U i TOL b t




9 Oct 2007Page 48

Upcoming TOL webcast

• For further information on SystemVue:

LTE MIMO System-Level Design and Test

5/27/2009

Greg Jue



Testing closed loop AMC with CQI PMI and RI




9 Oct 2007Page 49

Testing closed loop AMC with CQI, PMI and RI

with integral fading

• Open loop testing with no AMC avoids having to define the

reference behaviour of the test equipment

• However, it is still necessary to investigate closed loop

• The E6620A wireless

communications test set is

designed to go beyond basicconformance to test closed

loop MIMO up to 4x2

• Central to this is the inclusion

of a baseband fading emulator • This solution is the basis for development of scheduling

algorithms and transmission mode selection criteria



E6620A Wireless Communications Test Set

Conclusion:




9 Oct 2007Page 50

Conclusion:

Making MIMO work and testing it is tough!

• The standards are very flexible and complex

• The conformance tests are simple and largely open loop

with corner case SNR and artificial correlation

• Real life is way more complex

• Real antennas

• Real channels

• Real schedulers with multiple UE per cell

• Dynamic configuration for CSI reporting

• Real TX/RX distortion impacting channel feedback

•Non Gaussian frequency-selective cell-edge interference

But Agilent is here to help you clear the way for MIMO

Page 50



LTE VSA SW

LTE Lif l




9 Oct 2007Page 51

RF Module Development

RF Proto RF Chip/Module

Design

Simulation

BTS and Mobile

BB Chipset Development

L1/PHY

FPGA and ASIC

Pre-

Conformance

Conformance

RF and BB

Design

Integration

L1/PHY

System

Design

Validation

System Level

RF Testing

BTS or

Mobile

Protocol Development

L2/L3

Manufacturing

Network Deployment

Systems for RF and

Protocol Conformance

ADS and

SystemVue

LTE VSA SW

Spectrum and signal

Analyzers, Scopes, LA and

ADS

Spectrum Analyzers

Signal Studio

Logic Analyzers

& Scopes

Signal

Generators

Battery Drain

Characterization

Distributed Network

AnalyzersDrive Test

PXB MIMORx Tester

DigRF v4

N9912A RF

Analyzer RDX for

DigRF v4

E6620A Wireless

Communications Platform

Agilent/Anite SAT

Protocol

Development

Toolset

Page 51



LTE Lifecycle

Finding MIMO: Don’t stop now! Learn more at




Finding MIMO: Don’t stop now! Learn more at www.agilent.com/find/MIMO and www.agilent.com/find/lte

MIMO Poster (5989-9618EN)

LTE Brochure

(5989-7817EN)

Webcasts on• LTE Concepts

• LTE Uplink

• LTE Design and Simulation

• LTE signaling

PXB Brochure

(5989-8970EN)

The 3GPP MIMO song:

ftp.3gpp.org/tsg_ran/WG1_RL1/TSGR1_56/Docs/R1-091041.zip

http://www.agilent.com/find/MIMO

http://www.agilent.com/find/lte

ftp://ftp.3gpp.org/tsg_ran/WG1_RL1/TSGR1_56/Docs/R1-091041.zip




http://www.agilent.com/find/lte

http://www.agilent.com/find/MIMO

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