mimo wireless communication per hjalmar lehne, telenor guest lecture at unik 1 march 2012

MIMO Wireless Communication

Per Hjalmar Lehne, Telenor

Guest lecture at UniK1 March 2012

Agenda

What is MIMO?

Different gains of multiple antenna systems

Fundamental Limits of Wireless Transmission

• Shannon capacity of Wireless Channels

• Multiple antennas at one end

• Capacity of MIMO Links

Data transmission over MIMO Systems

• General principles

• Diversity using Space Time Block Codes

• Spatial Multiplexing

Wireless channel modelling

• Theoretical Models

• Heurestic Models

• Broadband Channels

• Measured Channels

System Level Issues

• Optimum use of multiple antennas

• MIMO in Mobile Broadband

MIMO Transmission Scheme for HSPA and LTE

01.03.20122

Agenda

What is MIMO?















System Level Issues




01.03.20123

What is MIMO?

MIMO: Multiple input – multiple output

Given an arbitrary wireless communication system:

• ”A link for which the transmitting end as well as the receiving end is equipped with multiple antenna elements”

The signals on the transmit antennas and receive antennas are ”combined” to improve the quality of the communication (ber and/or bps)

MIMO systems use space-time processing techniques

• Time dimension is completed with the spatial dimension

01.03.20124

Agenda

What is MIMO?















System Level Issues




01.03.20125


”Smart antenna” gain

• Beamforming to increase the average signal-to-noise (SNR) ratio through focussing energy into desired directions

Spatial diversity gain

• Receiving on multiple antenna elements reduces fading problems. The diversity order is defined by the number of decorrelated spatial branches

Spatial multiplexing gain

• A matrix channel is created, opening up the possibility of transmitting over several spatial modes of the matrix channel increasing the link throughput at no additional frequency, timer or power expenditure

01.03.20126

Multiple antenna fundamentals

7

Channel

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,

demodulation, decoding

Data

Data stream

Tx antenna ports

Rx antenna ports

Data

Recovered data stream

01.03.2012


8

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,


Data

Data stream

Tx antenna ports

Rx antenna ports

Data


343332

242322

141312

31

21

11

hhh

hhh

hhh

h

h

h

H

N transmit antennas

M receive antennas

Channel matrix

01.03.2012


9 01.03.2012

A1

A2

A3

A4

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,


Data

Data stream

Tx antenna ports

Rx antenna ports

Data


Multiple antenna fundamentalsSpatial multiplexing

10 01.03.2012

The different data streams are

divided in space

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,


Data

Data stream

Tx antenna ports

Rx antenna ports

Data


343332

242322

141312

31

21

11

hhh

hhh

hhh

h

h

h

H

rank(H) determines how many streams are possible to transmit

Multiple antenna fundamentalsTransmit diversity

11 01.03.2012

A1

A2

A3

A4

Redundancy:

The data streams contain the same

data

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,


Data

Data stream

Tx antenna ports

Rx antenna ports

Data


Multiple antenna fundamentalsBeamforming

12 01.03.2012

A1

A2

A3

A4

Only the best spatial channel is used to

maximize C/N

Coding, modulation,

weigthing/m

apping

Weighting, /dem

apping,


Data

Data stream

Tx antenna ports

Rx antenna ports

Data


Agenda

What is MIMO?















System Level Issues




01.03.201213

Fundamental limits of wireless transmission

Shannon capacity of Wireless Channels:

• h is the unit power complex Gaussian amplitude of the channel– h is a random variable

• Multiple antennas at one end:

• Capacity of MIMO Links:

Average capacity Ca

Outage capacity Co

)1(log

)1(log2

2

2

hC

C

)1(log2*hhC

*HHI

NC M

detlog2

%9..9.99 oCCP

01.03.201214

Shannon capacity of Wireless ChannelsIdeal Rayleigh Channel

)1(log2

2 hC

)1(log2*hhC

*HHI

NC M

detlog2

01.03.201215

Agenda

What is MIMO?















System Level Issues




01.03.201216

Data transmission over MIMO systems

Two main categories:

• Data rate maximization– Sending as many independent signals as antennas

– Spatial multiplexing

• Diversity maximization– The individual streams can be encoded jointly

– Protect against transmission errors caused by channel fading

– Minimize the outage probability

01.03.201217

Maximizing diversity with space-time block codes

Alamouti’s scheme:

• The block of symbols s0 and s1 is coded across time and space

• Normalization factor ensures total energy to be the same the case of one transmitter

Reception:

• The receiver collects the observation, y, over two symbol periods

*01

*10

2

1

ss

ssC

Tx0

Tx1

*

10 , ss

*

01, ss

Rx

nChn 10 yy0h

1h

n

nsHn ˆ*10

Tyy

Tss 10s

*0

*1

10

2

1ˆhh

hhH 10 hhh

01.03.201218

Spatial multiplexing

Extending the Space-Time Block Coding

• Transmitting independent data over different antennas

• The receiver must un-mix the channel

• Limited diversity benefit

NHCY

C H Y

01.03.201219

Spatial multiplexing - decoding

Zero-forcing (ZF)

• Inverting matrix H

• Simple approach

• Dependent on low-correlation in H

Maximum likelihood (ML)

• Optimum

• Comparing all possible combination with the observation

• High complexity

Nulling and cancelling

• Matrix inversion in layers

• Estimates one symbol, subtracts and continues decoding successively

YHC

NHCY1

ˆ

CHYC

C ˆˆ

minargˆ

01.03.201220

Transmission scheme performance

Same transmission rate

• Alamouti

• Spatial multiplexing – zero forcing

• Spatial multiplexing – maximum likelihood

• Combined STBC spatial multiplexing

01.03.201221

Agenda

What is MIMO?












• Heuristic Models



System Level Issues




01.03.201222


The promise of high MIMO capacities largely relies on the decorrelation properties:

• Between antennas

• Full-rankness of the MIMO channel matrix H– E.g. spatial multiplexing becomes completely inefficient if the channel has

rank 1

Aim of channel modelling:

• Get an understanding of what performance can be reasonably expected form MIMO systems

• To provide the necessary tools to analyze the impact of selected antenna or propagation parameters– Spacing, frequency, antenna height..

• To influence the system design in the best way

23 01.03.2012


Four approaches

• Theoretical Models– E.g. the ”idealistic” channel matrix of perfectly uncorrelated (i.i.d.)

random Gaussian elements

• Heurestic Models– In practice, MIMO channels will not fall completely into any of the

theoretical cases

• Broadband Channels– Frequency selective fading is experienced a new MIMO matrix is

obtained at each frequency/sub-band

• Measured Channels– Validate the models, provide acceptance of MIMO systems into

wireless standards

01.03.201224

Theoretical channel models

Ideal channel (i.i.d.):

• Corresponds to a rich multipath environment

Emphasizing the separate roles

• Antenna correlation (transmit or receive)

• Rank of the channel– Uncorrelated High Rank (UHR aka i.i.d.)

– Correlated Low Rank (CLR)

– Antennas are placed too close to each other, or

– Too little angular spread at both transmitter and receiver

– Uncorrelated Low Rank (ULR)

– ”pin-hole” model

25 01.03.2012

**txrxtxrx gg uuH

*txrxggH

Heuristic channel models

Display a wide range of MIMO channel behaviours through the use of as few relevant channel parameters as possible, with as much realism as possible

• What is the typical capacity of a MIMO channel?

• What are the key parameters governing capacity?

• Under what simple conditions do we get full rank channel?

The model parameters should be controllable or measurable

26 01.03.2012

Antenna correlation at transmitter or receiver

A MIMO channel with correlated receive antennas:

• For ”large” values of the angle spread and/or antenna spacing, R will converge to the identity matrix

• For ”small” values of θr, dr, R becomes rank deficient (eventually rank one) causing fully correlated fading

Generalized model includes correlation on both sides:

27 01.03.2012

02/1, HRH

rr d

2/1,0

2/1, ttrr dd RHRH

The double scattering model – ”pinhole” channels

Uncorrelated low rank:

• Significant local scattering around both the BTS and the subscriber’s antennas

• Local scatterer’s are considered as virtual receive antennas– When the virtual aperture is small, either on transmit or receive, the rank of the

overall MIMO channel will fall

28 01.03.2012

Broadband channels

Frequency selective channels are experienced

MIMO capacity benefits OFDM systems with MIMO

• Additional paths contribute to the selectivity as well as a greater overall angular spread

• Improving the average rank of the MIMO channel across frequencies

29 01.03.2012

H(f)

Measured channels

Channel matrix is measured using multiple antennas at transmitter and receiver

• Results confirm the high level of MIMO capacity potential, at least in urban and suburban areas

• Eigenvalue analysis– A large number of the modes of MIMO channels can be exploited to transmit data

30

0 10 20 3010

-4

10-2

100

Diversity gain, full CSI

SNR [dB]

Cum

pro

babili

ty

0 200 400 6000

10

20

30SNR mean value and difference

Route sample no.

dB

0 200 400 6000

200

400

600

800

Route sample no.

Capacity M

bits/s

P Kvadraturen 01 15 21

0 200 400 600 8000

0.5

1

Sum capacity, C-sum [MBits/sec]

Pro

babili

ty(C

apacity <

C-s

um

)

RX= 10,14,12,16 TX= 2,6,1,5

SISO

2x2 MIMO

4x4 MIMO

LOS

NLOS

Agenda

What is MIMO?















System Level Issues




01.03.201231

System level issues – optimum use of multiple antennas

Multiple antenna usage is not new in mobile systems:

• Spatial diversity systems

Different goals:

• Beamforming is optimum using a large number of closely spaced antennas:– Directional beamforming imposes stringent limits on spacing, typically

a half wavelength

– Best performance in line-of-sight (LOS)

• MIMO algorithms focusses on diversity or data rate maximization:– Antennas will use as much space as possible to realize decorrelation

between antennas

– Turning rich multipath into an advantage and lose the gain in LOS cases

01.03.201232

MIMO in mobile broadband

A unfavourable aspect:

• Increased cost and size of the subscriber’s equipment

• Limits applicability on simple mobile devices

A better opportunity:

• Wireless LAN modems – tablets - laptops

33 01.03.2012

Agenda

What is MIMO?















System Level Issues




01.03.201234

MIMO transmission schemes for LTE

LTE supports downlink transmissions on one, two or four cell-specific antenna ports

• Up to two transport blocks can be transmitted simultaneously on up to four layers

The use of multiple antennas in the DL of LTE comprises several modes

The system adaptively switches between each mode to obtain the best possible performance as the propagation conditions vary

35 01.03.2012

LTE Transmission modes

1 Single eNB antenna

2 Tx diversity (SFBC)

3 Open-loop SM

4 Closed-loop SM

5 Multi-user MIMO

6 Beamforming

7 UE specific RS

Downlink multi-antenna support in LTE

Up to 4x4 antennas on downlink

• 8x8 on LTE-advanced

Single-user schemes

• Transmit diversity (2)

• Spatial multiplexing (3, 4)

• Beamforming (6)

Multi-user MIMO (5)

A common physical layer architecture:

ScramblingModulation

mapper

Layermapper

Precoding

Resource element mapper

OFDM signal generation




mapper

layers antenna portscode words

36 07 September 2011



3 Open-loop SM

4 Closed-loop SM

5 Multi-user MIMO

6 Beamforming

7 UE specific RS




Single-user schemes



• Beamforming (6)

Multi-user MIMO (5)



mapper

Layermapper

Precoding






mapper





3 Open-loop SM

4 Closed-loop SM

5 Multi-user MIMO

6 Beamforming

7 UE specific RS

Transmit Diversity with 2 Tx antennas

Alamouti scheme

• Transmitted diversity streams are orthogonal:

*1

*2

2111

00

)2()1(

)2()1(

xx

xx

yy

yy

Subcarrier (frequency)

Port (antenn

a)

Antenna port 0

x1 x2

Antenna port 1

-x2* x1

*

OFDM subcarriers07 September 201138




Single-user schemes



• Beamforming (6)

Multi-user MIMO (5)



mapper

Layermapper

Precoding






mapper





3 Open-loop SM

4 Closed-loop SM

5 Multi-user MIMO

6 Beamforming

7 UE specific RS

Downlink spatial multiplexing for 2x2 antennas

The number of codewords equals the transmission rank and codeword n is mapped to layer n

Rank one precoders are column subsets of the rank two precoders

Recommendations on transmission rank and which precoder matrix to use is obtained via feedback from the subscriber equipment (UE)

• The base station (eNB) can override the rank recommended by the UE

Codeword to layer mapping:

jj

11,

11

11,

10

01

Codeword 1 Codeword 2

Rank 1 Layer 1

Rank 2 Layer 1 Layer 2

Rank 3 Layer 1 Layer 2 and 3

Rank 4 Layer 1 and 2 Layer 3 and 4

07 September 201140




Single-user schemes



• Beamforming (6)

Multi-user MIMO (5)



mapper

Layermapper

Precoding






mapper





3 Open-loop SM

4 Closed-loop SM

5 Multi-user MIMO

6 Beamforming

7 UE specific RS

DL peak throughputs in LTE


1 layer1 layer

2 layer

4 layer

64QAM Modulation

MIMO config

1.4 3 5 10 15 20Carrier Bandwidth (MHz)

5.2Mbps 13Mbps 21.6Mbps43.2Mbps

64.8Mbps

86.4Mbps10.4Mbps 25.9Mbps

43.2Mbps86.4Mbps

129.6Mbps

172.8Mbps

23Mbps49Mbps

82Mbps

163Mbps

245Mbps

326Mbps

Data rate (gross)

Pea

k T

hro

ug

hp

ut

1.4 3 5 10 15 20Carrier Bandwidth (MHz)

5.2Mbps 13Mbps 21.6Mbps43.2Mbps

64.8Mbps

86.4Mbps10.4Mbps 25.9Mbps

43.2Mbps86.4Mbps

129.6Mbps

172.8Mbps

23Mbps49Mbps

82Mbps

163Mbps

245Mbps

326Mbps

Data rate (gross)

Pea

k T

hro

ug

hp

ut

Downlink MIMO for HSPA (3G)

HSPA supports downlink closed-loop MIMO rank 2


Other multiple antenna schemes

Multi-user (MU-) MIMO

• Spatial multiplexing to different UEs in the same cell

• Also called Spatial Division Multiple Access (SDMA)


Summary

MIMO is using multiple antennas at both transmitter and receiver ends to set up a wireless link

MIMO gains can be beamforming, diversity or spatial multiplexing

Wireless link capacity can be multiplied by min(M,N)

Data transmission exploits the spatial dimension by maximizing either data rate or diversity

Wireless channel modelling is a tool to get the necessary understanding of perfoemence and be atool to analyze the impact of the design

Optimum use of multiple antennas contain conflicting goals in the system design, especially when it comes to antenna sizes and design

Both HSPA and LTE enables practical use of MIMO

45 01.03.2012

Literature

David Gesbert and Jabran Akhtar: ”Breaking the Barriers of Shannon’s Capacity: An Overview of MIMO Wireless Systems”. Telektronikk, 98(1), p53-54, 2002.

3G Americas White paper: "MIMO Transmission Schemes for LTE and HSPA Networks”, chapter 4, p19-30. 2009.

--

Extra reading for those interested:

David Gesbert etal.:” From Theory to Practice: An Overview of MIMO Space-Time Coded Wireless Systems”. IEEE Journal on Selected Areas in Comunications, 21(3), p281-302, April 2003.

A. Sibille, C. Oestges, A Zanella. ”MIMO: From Theory to implementation”. Academic Press, 2010. ISBN-10: 0123821940, ISBN-13: 978-0123821942

01.03.201246

mimo wireless communication per hjalmar lehne, telenor guest lecture at unik 1 march 2012

Documents

mimo wireless communication

multiple antenna fundamentals

multiple antenna elements

multiple input multiple

spatial dimension

spatial modes

channel coding

matrix channel